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Longer inputs may use higher pricing tiers.","reasoning_effort defaults to xhigh and accepts xhigh, medium, and low; max maps to xhigh, minimal maps to low, and none disables thinking. reasoning_effort and thinking_budget cannot be set together, and Alibaba recommends reasoning_effort because enable_thinking will be deprecated."],"sourceProvider":"qwen","sourceProviderName":"Qwen","sourceDocs":["https://www.alibabacloud.com/help/en/model-studio/models","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-8-max","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-7-plus","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-8-flash","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-coder-plus","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-coder-flash","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-coder-next","https://www.alibabacloud.com/help/en/model-studio/deep-thinking","https://www.alibabacloud.com/help/en/model-studio/qwen-function-calling","https://huggingface.co/Qwen/Qwen3-Coder-Next"],"sourceStatus":"seed","apiListed":false,"source":"public-provider-conf"},"vision":true,"interleaved":{"field":"reasoning_content"},"extra_capabilities":{"reasoning":{"supported":true,"budget":{"max":262144,"default":262144,"unit":"tokens"},"continuation":["thinking_blocks"],"notes":["enable_thinking toggles reasoning; thinking_budget caps it. 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Longer inputs may use higher pricing tiers.","reasoning_effort defaults to xhigh and accepts xhigh, medium, and low; max maps to xhigh, minimal maps to low, and none disables thinking. reasoning_effort and thinking_budget cannot be set together, and Alibaba recommends reasoning_effort because enable_thinking will be deprecated."],"sourceProvider":"qwen","sourceProviderName":"Qwen","sourceDocs":["https://www.alibabacloud.com/help/en/model-studio/models","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-8-max","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-7-plus","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-8-flash","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-coder-plus","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-coder-flash","https://docs.modelstudio.console.alibabacloud.com/en/model-studio/qwen3-coder-next","https://www.alibabacloud.com/help/en/model-studio/deep-thinking","https://www.alibabacloud.com/help/en/model-studio/qwen-function-calling","https://huggingface.co/Qwen/Qwen3-Coder-Next"],"sourceStatus":"seed","apiListed":false,"source":"public-provider-conf"},"vision":true,"interleaved":{"field":"reasoning_content"},"extra_capabilities":{"reasoning":{"supported":true,"budget":{"max":262144,"default":262144,"unit":"tokens"},"continuation":["thinking_blocks"],"notes":["enable_thinking toggles reasoning; thinking_budget caps it. 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Structured output support refers to JSON mode."],"sourceProvider":"stepfun","sourceProviderName":"StepFun","sourceDocs":["https://platform.stepfun.ai/docs/en/guides/models/step-3.7-flash","https://platform.stepfun.ai/docs/en/guides/models/step-3.5-flash","https://platform.stepfun.ai/docs/en/api-reference/chat/chat-completion-create","https://platform.stepfun.ai/docs/en/guides/developer/reasoning","https://platform.stepfun.ai/docs/en/guides/pricing/details","https://huggingface.co/stepfun-ai/Step-3.7-Flash"],"sourceStatus":"seed","apiListed":false,"source":"public-provider-conf"},"vision":false,"extra_capabilities":{"reasoning":{"supported":true,"default_enabled":true,"mode":"fixed","notes":["The API returns reasoning by default; reasoning_format=deepseek-style switches the response field to reasoning_content."]}}},{"id":"step-3.5-flash-2603","name":"Step 3.5 Flash 2603","family":"step","display_name":"Step 3.5 Flash 2603","type":"chat","attachment":false,"reasoning":{"supported":true,"default":true},"temperature":true,"tool_call":true,"structured_output":true,"reasoning_options":[{"type":"effort","values":["low","high"]}],"last_updated":"2026-08-31","modalities":{"input":["text"],"output":["text"]},"cost":{"input":0.1,"output":0.3,"cache_read":0.02},"limit":{"context":262144,"output":262144},"metadata":{"selection":["coding","reasoning","cost-efficient"],"lifecycle":"active","notes":["max_tokens is bounded by the shared input/output context window. 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You can explicitly specify the focus using auto:<strategy>, such as auto (= auto:cost_optimized) cost priority: choose the cheapest as long as the capability meets the standard; auto:balanced balanced: considers capability / cost / latency\n\nauto:quality_first quality priority: prioritize selecting the strongest capability for complex reasoning and critical outputs\n\nauto:latency_critical low latency priority: prioritize selecting the fastest response","typeHints":["llm"],"features":["thinking","web","deepsearch","tools","function_calling","structured_outputs","long_context"],"inputModalities":["text","audio","image","video"],"pricing":{"input":2,"output":2},"source":"public-provider-conf"},"vision":true,"extra_capabilities":{"reasoning":{"supported":true}}},{"id":"gpt-6-astra","name":"gpt-6-astra","display_name":"gpt-6-astra","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text","image"]},"cost":{"input":10,"output":50,"cache_read":1},"limit":{"context":1050000,"output":1050000},"metadata":{"description":"GPT-6 Astra is OpenAI's newest and most intelligent model, with industry-leading performance in computer operations, web browsing, software engineering, scientific research, and professional work. It excels at executing multi-step workflows across code, browsers, and various professional software. Astra can achieve better results with significantly fewer output tokens, making its estimated API cost per task lower.","typeHints":["llm"],"features":["tools","thinking","structured_outputs"],"inputModalities":["text","image"],"pricing":{"cache_read":1,"cache_write":12.5,"input":10,"output":50,"tiers":[{"cache_read":2,"cache_write":25,"input":20,"output":75,"tier":{"size":272000,"type":"context"}}]},"source":"public-provider-conf"},"vision":true,"extra_capabilities":{"reasoning":{"supported":true}}},{"id":"gemini-3.8-flash","name":"gemini-3.8-flash","display_name":"gemini-3.8-flash","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text","image","video","audio","pdf"]},"cost":{"input":0.75,"output":3.75,"cache_read":0.075},"limit":{"context":1048576,"output":1048576},"metadata":{"description":"Gemini 3.8 Flash is Google's most intelligent Flash-series model, designed for long-running software engineering tasks, autonomous agents, and complex enterprise workflows, while retaining the Flash series' fast responsiveness and cost-effectiveness.","typeHints":["llm"],"features":["thinking","tools","function_calling","structured_outputs","web","long_context"],"inputModalities":["text","image","video","audio","pdf"],"pricing":{"cache_read":0.075,"input":0.75,"output":3.75},"source":"public-provider-conf"},"vision":true,"extra_capabilities":{"reasoning":{"supported":true,"default_enabled":true,"mode":"level","level":"high","level_options":["minimal","low","medium","high"],"summaries":true,"visibility":"summary","continuation":["thought_signatures"]}}},{"id":"gemini-3.8-flash-free","name":"gemini-3.8-flash-free","display_name":"gemini-3.8-flash-free","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text","image","video","audio","pdf"]},"cost":{"input":0,"output":0},"limit":{"context":1048576,"output":1048576},"metadata":{"description":"Gemini 3.8 Flash free version: fFree model resources are limited and provided only for trial use; stability cannot be guaranteed, and you may encounter 429 errors during use. If you need to use it in a production environment and require unlimited concurrency with absolute stability, please choose the official version: gemini-3.8-flash","typeHints":["llm"],"features":["thinking","tools","function_calling","structured_outputs","web","long_context"],"inputModalities":["text","image","video","audio","pdf"],"pricing":{"input":0,"output":0},"source":"public-provider-conf"},"vision":true,"extra_capabilities":{"reasoning":{"supported":true,"default_enabled":true,"mode":"level","level":"high","level_options":["minimal","low","medium","high"],"summaries":true,"visibility":"summary","continuation":["thought_signatures"]}}},{"id":"deepseek-v4.1-flash","name":"deepseek-v4.1-flash","display_name":"deepseek-v4.1-flash","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text","image"]},"cost":{"input":0.155,"output":0.62,"cache_read":0.0031},"limit":{"context":1000000,"output":1000000},"metadata":{"description":"DeepSeek-V4.1-Flash model official release. This is the smallest model in DeepSeek’s new model-architecture series, featuring native multimodal visual understanding capabilities. The new architecture is designed to deliver a higher capability ceiling, faster inference speeds, greater throughput, and scalability to larger-parameter models.","typeHints":["llm"],"features":["tools","function_calling","structured_outputs","thinking"],"inputModalities":["text","image"],"pricing":{"cache_read":0.0031,"input":0.155,"output":0.62},"source":"public-provider-conf"},"vision":true,"extra_capabilities":{"reasoning":{"supported":true}}},{"id":"glm-5.3-flash","name":"glm-5.3-flash","display_name":"glm-5.3-flash","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text","image","video"]},"cost":{"input":0.11268,"output":0.39438,"cache_read":0.02817},"limit":{"context":1048576,"output":1048576},"metadata":{"description":"GLM-5.3-Flash is a high-efficiency multimodal model from Z.AI. It supports a context window of roughly 1 million tokens, along with text, image, and video inputs, and includes tool-calling capabilities. It is primarily designed for coding agents, complex reasoning, and long-horizon software engineering tasks. 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It uses a mixture-of-experts (MoE) architecture with a total of 100 billion parameters, but only activates 6.1 billion parameters per inference. The model employs the original Icepop algorithm to solve the instability issues of large MoE models during reinforcement learning (RL) training, enabling its complex reasoning capabilities to continuously improve over long training cycles. Ring-flash-2.0 has achieved significant breakthroughs on multiple high-difficulty benchmarks, including mathematics competitions, code generation, and logical reasoning. Its performance not only surpasses top dense models under 40 billion parameters but also rivals larger open-source MoE models and closed-source high-performance thinking models. Although the model focuses on complex reasoning, it also performs exceptionally well on creative writing tasks. Furthermore, thanks to its efficient architecture, Ring-flash-2.0 delivers high performance with low-latency inference, significantly reducing deployment costs in high-concurrency scenarios.","typeHints":["llm"],"features":["thinking","tools","function_calling","structured_outputs"],"inputModalities":["text"],"pricing":{"input":0.136,"output":0.544},"source":"public-provider-conf"},"vision":false,"extra_capabilities":{"reasoning":{"supported":true}}},{"id":"jina-deepsearch-v1","name":"jina-deepsearch-v1","display_name":"jina-deepsearch-v1","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":0.05,"output":0.05},"limit":{"context":1000000,"output":1000000},"metadata":{"description":"DeepSearch combines search, reading, and reasoning capabilities to pursue the best possible answer. It's fully compatible with OpenAI's Chat API format—just replace api.openai.com with aihubmix.com to get started.  \nThe stream will return the thinking process.","typeHints":["llm","search"],"features":["thinking","web","deepsearch"],"inputModalities":["text","image"],"pricing":{"input":0.05,"output":0.05},"source":"public-provider-conf"},"vision":true,"extra_capabilities":{"reasoning":{"supported":true}}},{"id":"jina-embeddings-v4","name":"jina-embeddings-v4","display_name":"jina-embeddings-v4","type":"embedding","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":0.05,"output":0.05},"limit":{"context":8192,"output":8192},"metadata":{"description":"A general-purpose vector model with 3.8 billion parameters, used for multimodal and multilingual retrieval, supporting both unidirectional and multi-vector embedding outputs.","typeHints":["embedding"],"inputModalities":["text","image"],"pricing":{"input":0.05,"output":0.05},"source":"public-provider-conf"},"vision":true},{"id":"jina-reranker-v3","name":"jina-reranker-v3","display_name":"jina-reranker-v3","type":"rerank","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":0.05,"output":0.05},"limit":{"context":131072,"output":131072},"metadata":{"description":"Multimodal multilingual document reranker, 131K context, 0.6B parameters, for visual document sorting.","typeHints":["rerank"],"inputModalities":["text","image"],"pricing":{"input":0.05,"output":0.05},"source":"public-provider-conf"},"vision":true},{"id":"llama-4-maverick","name":"llama-4-maverick","display_name":"llama-4-maverick","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text","image"]},"cost":{"input":0.2,"output":0.2},"limit":{"context":1048576,"output":1048576},"metadata":{"description":"Llama 4 Maverick is a high-capacity Mixture-of-Experts (MoE) model from Meta, featuring 400B total parameters and 128 experts, while activating an efficient 17B parameters per inference. Engineered for peak performance, it excels at advanced multimodal tasks.\n\nMaverick natively supports text and image input, producing multilingual text and code. With a 1-million-token context window and instruction tuning, it is optimized for complex image reasoning and general-purpose assistant-like interactions.\n\nReleased under the Llama 4 Community License, Maverick is ideal for research and commercial applications demanding state-of-the-art multimodal understanding and high throughput.","typeHints":["llm"],"features":["tools","function_calling","structured_outputs"],"inputModalities":["text","image"],"pricing":{"input":0.2,"output":0.2},"source":"public-provider-conf"},"vision":true},{"id":"llama-4-scout","name":"llama-4-scout","display_name":"llama-4-scout","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text","image"]},"cost":{"input":0.2,"output":0.2},"limit":{"context":131072,"output":131072},"metadata":{"description":"Llama 4 Scout is a highly efficient Mixture-of-Experts (MoE) model from Meta, activating 17B out of 109B total parameters per inference. It natively supports multimodal input (text and image) and multilingual output (text and code) across 12 languages.\n\nDesigned for assistant-style interaction and visual reasoning, Scout features a massive 10-million-token context window. It is instruction-tuned for tasks like multilingual chat and image understanding and is released under the Llama 4 Community License for local or commercial deployment.","typeHints":["llm"],"features":["tools","function_calling","structured_outputs"],"inputModalities":["text","image"],"pricing":{"input":0.2,"output":0.2},"source":"public-provider-conf"},"vision":true},{"id":"qwen-image","name":"qwen-image","display_name":"qwen-image","type":"image-generation","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":2,"output":0},"limit":{"context":8192,"output":8192},"metadata":{"description":"Qwen-Image is a foundational image generation model in the Qwen series, achieving significant progress in complex text rendering and precise image editing. Experiments show that the model has strong general capabilities in image generation and editing, especially excelling in Chinese text rendering.","typeHints":["image_generation"],"inputModalities":["text","image"],"pricing":{"input":2,"output":0},"source":"public-provider-conf"},"vision":true},{"id":"qwen-image-edit","name":"qwen-image-edit","display_name":"qwen-image-edit","type":"image-generation","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":2,"output":0},"limit":{"context":8192,"output":8192},"metadata":{"description":"Qwen-Image-Edit is the image editing version of Qwen-Image. Based on the 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image's unique text rendering capabilities to image editing tasks, achieving precise text editing. Additionally, Qwen-Image-Edit can input the same image into Qwen2.5-VL (for visual semantic control) and the VAE encoder (for visual appearance control), enabling both semantic and appearance editing functionalities.","typeHints":["image_generation"],"inputModalities":["text","image"],"pricing":{"input":2,"output":0},"source":"public-provider-conf"},"vision":true},{"id":"qwen-image-max","name":"qwen-image-max","display_name":"qwen-image-max","type":"image-generation","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":2,"output":0},"limit":{"context":8192,"output":8192},"metadata":{"description":"Qwen-Image-Edit is the image editing version of Qwen-Image. Based on the 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image's unique text rendering capabilities to image editing tasks, achieving precise text editing. Additionally, Qwen-Image-Edit can input the same image into Qwen2.5-VL (for visual semantic control) and the VAE encoder (for visual appearance control), enabling both semantic and appearance editing functionalities.","typeHints":["image_generation"],"inputModalities":["text","image"],"pricing":{"input":2,"output":0},"source":"public-provider-conf"},"vision":true},{"id":"qwen-mt-plus","name":"qwen-mt-plus","display_name":"qwen-mt-plus","type":"chat","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"]},"cost":{"input":0.492,"output":1.476},"limit":{"context":16000,"output":16000},"metadata":{"description":"Based on the comprehensive upgrade of Qwen3, this flagship translation large model supports bidirectional translation across 92 languages. It offers fully enhanced model performance and translation quality, along with more stable terminology customization, format fidelity, and domain-prompting capabilities, making translations more accurate and natural.","typeHints":["llm"],"inputModalities":["text"],"pricing":{"input":0.492,"output":1.476},"source":"public-provider-conf"},"vision":false},{"id":"qwen-mt-turbo","name":"qwen-mt-turbo","display_name":"qwen-mt-turbo","type":"chat","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"]},"cost":{"input":0.192,"output":0.534912},"limit":{"context":16000,"output":16000},"metadata":{"description":"Based on the comprehensive upgrade of Qwen3, this flagship translation large model supports bidirectional translation across 92 languages. It offers fully enhanced model performance and translation quality, along with more stable terminology customization, format fidelity, and domain-prompting capabilities, making translations more accurate and natural.","typeHints":["llm"],"inputModalities":["text"],"pricing":{"input":0.192,"output":0.534912},"source":"public-provider-conf"},"vision":false},{"id":"bce-reranker-base","name":"bce-reranker-base","display_name":"bce-reranker-base","type":"rerank","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":0.068,"output":0},"limit":{"context":8192,"output":8192},"metadata":{"description":"Based on the dense foundational model of the Qwen3 series, it is specifically designed for ranking tasks. It inherits the base model’s outstanding multilingual capabilities, long-text understanding, and reasoning skills, achieving significant advancements in ranking tasks.","typeHints":["rerank"],"inputModalities":["text","image"],"pricing":{"input":0.068,"output":0},"source":"public-provider-conf"},"vision":true},{"id":"codex-mini-latest","name":"codex-mini-latest","display_name":"codex-mini-latest","type":"chat","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":1.5,"output":6,"cache_read":0.375},"limit":{"context":8192,"output":8192},"metadata":{"description":"Only supports v1/responses API calls.https://docs.aihubmix.com/en/api/Responses-API\ncodex-mini-latest is a fine-tuned version of o4-mini specifically for use in Codex CLI. For direct use in the API, we recommend starting with gpt-4.1.","typeHints":["llm"],"inputModalities":["text","image"],"pricing":{"cache_read":0.375,"input":1.5,"output":6},"source":"public-provider-conf"},"vision":true},{"id":"qwen3-embedding-0.6b","name":"qwen3-embedding-0.6b","display_name":"qwen3-embedding-0.6b","type":"embedding","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"]},"cost":{"input":0.068,"output":0},"limit":{"context":8192,"output":8192},"metadata":{"description":"The Qwen3 Embedding model series is the latest proprietary model family from Qwen, specifically designed for text embedding and ranking tasks. Based on the dense base models of the Qwen3 series, it offers comprehensive text embedding and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the excellent multilingual capabilities, long-text understanding, and reasoning skills of its base models. The Qwen3 Embedding series demonstrates significant advancements in various text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bilingual text mining.","typeHints":["embedding"],"inputModalities":["text"],"pricing":{"input":0.068,"output":0},"source":"public-provider-conf"},"vision":false},{"id":"qwen3-embedding-4b","name":"qwen3-embedding-4b","display_name":"qwen3-embedding-4b","type":"embedding","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"]},"cost":{"input":0.068,"output":0.068},"limit":{"context":8192,"output":8192},"metadata":{"description":"The Qwen3 Embedding model series is the latest proprietary model family from Qwen, specifically designed for text embedding and ranking tasks. Based on the dense base models of the Qwen3 series, it offers comprehensive text embedding and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the excellent multilingual capabilities, long-text understanding, and reasoning skills of its base models. The Qwen3 Embedding series demonstrates significant advancements in various text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bilingual text mining.","typeHints":["embedding"],"inputModalities":["text"],"pricing":{"input":0.068,"output":0.068},"source":"public-provider-conf"},"vision":false},{"id":"qwen3-embedding-8b","name":"qwen3-embedding-8b","display_name":"qwen3-embedding-8b","type":"embedding","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"]},"cost":{"input":0.068,"output":0},"limit":{"context":8192,"output":8192},"metadata":{"description":"The Qwen3 Embedding model series is the latest proprietary model family from Qwen, specifically designed for text embedding and ranking tasks. Based on the dense base models of the Qwen3 series, it offers comprehensive text embedding and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the excellent multilingual capabilities, long-text understanding, and reasoning skills of its base models. The Qwen3 Embedding series demonstrates significant advancements in various text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bilingual text mining.","typeHints":["embedding"],"inputModalities":["text"],"pricing":{"input":0.068,"output":0},"source":"public-provider-conf"},"vision":false},{"id":"qwen3-reranker-0.6b","name":"qwen3-reranker-0.6b","display_name":"qwen3-reranker-0.6b","type":"rerank","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":0.11,"output":0.11},"limit":{"context":16000,"output":16000},"metadata":{"description":"Based on the dense foundational model of the Qwen3 series, it is specifically designed for ranking tasks. It inherits the base model’s outstanding multilingual capabilities, long-text understanding, and reasoning skills, achieving significant advancements in ranking tasks.","typeHints":["rerank"],"inputModalities":["text","image"],"pricing":{"input":0.11,"output":0.11},"source":"public-provider-conf"},"vision":true},{"id":"qwen3-reranker-4b","name":"qwen3-reranker-4b","display_name":"qwen3-reranker-4b","type":"rerank","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":0.11,"output":0.11},"limit":{"context":8192,"output":8192},"metadata":{"description":"Based on the dense foundational model of the Qwen3 series, it is specifically designed for ranking tasks. It inherits the base model’s outstanding multilingual capabilities, long-text understanding, and reasoning skills, achieving significant advancements in ranking tasks.","typeHints":["rerank"],"inputModalities":["text","image"],"pricing":{"input":0.11,"output":0.11},"source":"public-provider-conf"},"vision":true},{"id":"qwen3-reranker-8b","name":"qwen3-reranker-8b","display_name":"qwen3-reranker-8b","type":"rerank","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":0.11,"output":0.11},"limit":{"context":8192,"output":8192},"metadata":{"description":"Based on the dense foundational model of the Qwen3 series, it is specifically designed for ranking tasks. It inherits the base model’s outstanding multilingual capabilities, long-text understanding, and reasoning skills, achieving significant advancements in ranking tasks.","typeHints":["rerank"],"inputModalities":["text","image"],"pricing":{"input":0.11,"output":0.11},"source":"public-provider-conf"},"vision":true},{"id":"tao-8k","name":"tao-8k","display_name":"tao-8k","type":"embedding","reasoning":{"supported":false},"tool_call":false,"cost":{"input":0.068,"output":0.068},"limit":{"context":8192,"output":8192},"metadata":{"typeHints":["embedding"],"pricing":{"input":0.068,"output":0.068},"source":"public-provider-conf"},"vision":false},{"id":"doubao-seedream-4-0","name":"doubao-seedream-4-0","display_name":"doubao-seedream-4-0","type":"image-generation","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":2,"output":0},"limit":{"context":8192,"output":8192},"metadata":{"description":"Seedream 4.0 is a SOTA-level multimodal image creation model based on leading architecture. It breaks the creative boundaries of traditional text-to-image models by natively supporting text, single image, and multiple image inputs. Users can freely combine text and images to achieve various creative styles within the same model, such as multi-image fusion creation based on subject consistency, image editing, and set image generation, making image creation more flexible and controllable.\nSeedream 4.0 supports composite editing with up to 10 images in a single input. Through deep reasoning of prompt words, it automatically adapts the optimal image aspect ratio and generation quantity, enabling continuous output of up to 15 content-related images at one time. Additionally, the model significantly improves the accuracy and content diversity of Chinese generation, supports 4K ultra-high-definition output, and provides a one-stop solution from generation to editing for professional image creation.","typeHints":["image_generation"],"inputModalities":["text","image"],"pricing":{"input":2,"output":0},"source":"public-provider-conf"},"vision":true},{"id":"embedding-v1","name":"embedding-v1","display_name":"embedding-v1","type":"embedding","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"]},"cost":{"input":0.068,"output":0},"limit":{"context":8192,"output":8192},"metadata":{"description":"Embedding-V1 is a text representation model based on Baidu's Wenxin large model technology, capable of converting text into numerical vector forms for applications such as text retrieval, information recommendation, and knowledge mining. Embedding-V1 provides an Embeddings interface that generates corresponding vector representations based on the input content. By calling this interface, you can input text into the model and obtain the corresponding vector representations for subsequent text processing and analysis.","typeHints":["embedding"],"inputModalities":["text"],"pricing":{"input":0.068,"output":0},"source":"public-provider-conf"},"vision":false},{"id":"ernie-4.5-turbo-latest","name":"ernie-4.5-turbo-latest","display_name":"ernie-4.5-turbo-latest","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text","image"]},"cost":{"input":0.11,"output":0.44},"limit":{"context":135000,"output":135000},"metadata":{"description":"Wenxin 4.5 Turbo also has significant improvements in hallucination reduction, logical reasoning, and coding capabilities. Compared to Wenxin 4.5, it is faster and more affordable.","typeHints":["llm"],"features":["tools","function_calling","structured_outputs"],"inputModalities":["text","image"],"pricing":{"input":0.11,"output":0.44},"source":"public-provider-conf"},"vision":true},{"id":"glm-4.5-x","name":"glm-4.5-x","display_name":"glm-4.5-x","type":"chat","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"]},"cost":{"input":2.2,"output":8.91,"cache_read":0.44},"limit":{"context":8192,"output":8192},"metadata":{"description":"GLM-4.5-X is the high-speed version of GLM-4.5, offering powerful performance with a generation speed of up to 100 tokens per second.","inputModalities":["text"],"pricing":{"cache_read":0.44,"input":2.2,"output":8.91},"source":"public-provider-conf"},"vision":false},{"id":"gme-qwen2-vl-2b-instruct","name":"gme-qwen2-vl-2b-instruct","display_name":"gme-qwen2-vl-2b-instruct","type":"embedding","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image","video"]},"cost":{"input":0.138,"output":0.138},"limit":{"context":8192,"output":8192},"metadata":{"description":"The GME-Qwen2VL series is a unified multimodal Embedding model trained based on the Qwen2-VL multimodal large language model (MLLMs). The GME model supports three types of inputs: text, images, and image-text pairs. All these input types can generate universal vector representations and exhibit excellent retrieval performance.","typeHints":["embedding"],"inputModalities":["text","image","video"],"pricing":{"input":0.138,"output":0.138},"source":"public-provider-conf"},"vision":true},{"id":"jina-clip-v2","name":"jina-clip-v2","display_name":"jina-clip-v2","type":"embedding","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":0.05,"output":0.05},"limit":{"context":8192,"output":8192},"metadata":{"description":"Multi-modal Embeddings Model, multilingual, 1024-dimensional, 865M parameters.","typeHints":["embedding"],"inputModalities":["text","image"],"pricing":{"input":0.05,"output":0.05},"source":"public-provider-conf"},"vision":true},{"id":"jina-reranker-m0","name":"jina-reranker-m0","display_name":"jina-reranker-m0","type":"rerank","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image"]},"cost":{"input":0.05,"output":0.05},"limit":{"context":8192,"output":8192},"metadata":{"description":"Multimodal multilingual document reranker, 10K context, 2.4B parameters, for visual document sorting.","typeHints":["rerank"],"inputModalities":["text","image"],"pricing":{"input":0.05,"output":0.05},"source":"public-provider-conf"},"vision":true},{"id":"jina-colbert-v2","name":"jina-colbert-v2","display_name":"jina-colbert-v2","type":"embedding","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"]},"cost":{"input":0.05,"output":0.05},"limit":{"context":8192,"output":8192},"metadata":{"description":"Multi-language ColBERT embeddings model, 560M parameters, used for embedding and reranking.","typeHints":["embedding","rerank"],"inputModalities":["text"],"pricing":{"input":0.05,"output":0.05},"source":"public-provider-conf"},"vision":false},{"id":"gpt-4o-search-preview","name":"gpt-4o-search-preview","display_name":"gpt-4o-search-preview","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text","image"]},"cost":{"input":2.5,"output":10,"cache_read":1.25},"limit":{"context":128000,"output":128000},"metadata":{"description":"Using the Chat Completions API, you can directly access the fine-tuned models and tool used by Search in ChatGPT.\n\nWhen using Chat Completions, the model always retrieves information from the web before responding to your query. To use web_search_preview as a tool that models like gpt-4o and gpt-4o-mini invoke only when necessary, switch to using the Responses API.\n\nCurrently, you need to use one of these models to use web search in Chat Completions:\n\ngpt-4o-search-preview\ngpt-4o-mini-search-preview\nWeb search parameter example\nimport OpenAI from \"openai\";\nconst client = new OpenAI();\n\nconst completion = await client.chat.completions.create({\n    model: \"gpt-4o-search-preview\",\n    web_search_options: {},\n    messages: [{\n        \"role\": \"user\",\n        \"content\": \"What was a positive news story from today?\"\n    }],\n});\n\nconsole.log(completion.choices[0].message.content);\nOutput and citations\nThe API response item in the choices array will include:\n\nmessage.content with the text result from the model, inclusive of any inline citations\nannotations with a list of cited URLs\nBy default, the model's response will include inline citations for URLs found in the web search results. In addition to this, the url_citation annotation object will contain the URL and title of the cited source, as well as the start and end index characters in the model's response where those sources were used.","typeHints":["llm","search"],"features":["web","function_calling","structured_outputs"],"inputModalities":["text","image"],"pricing":{"cache_read":1.25,"input":2.5,"output":10},"source":"public-provider-conf"},"vision":true},{"id":"gpt-4o-mini-search-preview","name":"gpt-4o-mini-search-preview","display_name":"gpt-4o-mini-search-preview","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text","image"]},"cost":{"input":0.15,"output":0.6,"cache_read":0.075},"limit":{"context":128000,"output":128000},"metadata":{"description":"Using the Chat Completions API, you can directly access the fine-tuned models and tool used by Search in ChatGPT.\n\nWhen using Chat Completions, the model always retrieves information from the web before responding to your query. To use web_search_preview as a tool that models like gpt-4o and gpt-4o-mini invoke only when necessary, switch to using the Responses API.\n\nCurrently, you need to use one of these models to use web search in Chat Completions:\n\ngpt-4o-search-preview\ngpt-4o-mini-search-preview\nWeb search parameter example\nimport OpenAI from \"openai\";\nconst client = new OpenAI();\n\nconst completion = await client.chat.completions.create({\n    model: \"gpt-4o-search-preview\",\n    web_search_options: {},\n    messages: [{\n        \"role\": \"user\",\n        \"content\": \"What was a positive news story from today?\"\n    }],\n});\n\nconsole.log(completion.choices[0].message.content);\nOutput and citations\nThe API response item in the choices array will include:\n\nmessage.content with the text result from the model, inclusive of any inline citations\nannotations with a list of cited URLs\nBy default, the model's response will include inline citations for URLs found in the web search results. 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The model is trained with a novel stack incorporating the MuonClip optimizer for stable large-scale MoE training.","endpoints":["chat/completions","completions","anthropic"],"features":["function-calling","structured-outputs","serverless"],"pricing":{"inputPerM":6000,"outputPerM":25000},"source":"public-provider-conf"},"vision":false},{"id":"Sao10K/L3-8B-Stheno-v3.2","name":"L3 8B Stheno V3.2","display_name":"L3 8B Stheno V3.2","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":8192,"output":8192},"metadata":{"description":"Sao10K/L3-8B-Stheno-v3.2 is a highly skilled actor that excels at fully immersing itself in any role assigned.","endpoints":["chat/completions","completions"],"features":["function-calling","serverless"],"pricing":{"inputPerM":500,"outputPerM":500},"source":"public-provider-conf"},"vision":false},{"id":"sao10k/l3-70b-euryale-v2.1","name":"L3 70B Euryale V2.1","display_name":"L3 70B Euryale V2.1","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":8192,"output":8192},"metadata":{"description":"The uncensored llama3 model is a powerhouse of creativity, excelling in both roleplay and story writing. It offers a liberating experience during roleplays, free from any restrictions. This model stands out for its immense creativity, boasting a vast array of unique ideas and plots, truly a treasure trove for those seeking originality. Its unrestricted nature during roleplays allows for the full breadth of imagination to unfold, akin to an enhanced, big-brained version of Stheno. Perfect for creative minds seeking a boundless platform for their imaginative expressions, the uncensored llama3 model is an ideal choice","endpoints":["chat/completions","completions"],"features":["function-calling","serverless"],"pricing":{"inputPerM":14800,"outputPerM":14800},"source":"public-provider-conf"},"vision":false},{"id":"sao10k/l31-70b-euryale-v2.2","name":"L31 70B Euryale V2.2","display_name":"L31 70B Euryale V2.2","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":8192,"output":8192},"metadata":{"description":"Euryale L3.1 70B v2.2 is a model focused on creative roleplay from Sao10k. 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The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks.  Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.","endpoints":["chat/completions","completions"],"features":["function-calling","serverless"],"pricing":{"inputPerM":1300,"outputPerM":3900},"source":"public-provider-conf"},"vision":false},{"id":"meta-llama/llama-4-maverick-17b-128e-instruct-fp8","name":"Llama 4 Maverick Instruct","display_name":"Llama 4 Maverick Instruct","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text","image"],"output":["text"]},"limit":{"context":1048576,"output":1048576},"metadata":{"description":"Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. Optimized for vision-language tasks, Maverick is instruction-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interaction.  Maverick features early fusion for native multimodality and a 1 million token context window. It was trained on a curated mixture of public, licensed, and Meta-platform data, covering ~22 trillion tokens, with a knowledge cutoff in August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.","endpoints":["chat/completions","completions"],"features":["function-calling","serverless"],"pricing":{"inputPerM":1700,"outputPerM":8500},"source":"public-provider-conf"},"vision":true},{"id":"meta-llama/llama-4-scout-17b-16e-instruct","name":"Llama 4 Scout Instruct","display_name":"Llama 4 Scout Instruct","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text","image"],"output":["text"]},"limit":{"context":131072,"output":131072},"metadata":{"description":"Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.  Built for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.","endpoints":["chat/completions","completions"],"features":["function-calling","serverless"],"pricing":{"inputPerM":1000,"outputPerM":5000},"source":"public-provider-conf"},"vision":true},{"id":"minimaxai/minimax-m1-80k","name":"MiniMax M1","display_name":"MiniMax M1","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":1000000,"output":40000},"metadata":{"description":"MiniMax-M1: The World's First Open-Weight, Large-Scale Hybrid Attention Inference Model  MiniMax-M1 adopts a Mixture of Experts (MoE) architecture and integrates the Flash Attention mechanism. The model contains a total of 456 billion parameters, with 45.9 billion parameters activated per token.  Natively, the M1 model supports a context length of 1 million tokens—8 times that of DeepSeek R1. Additionally, by combining the CISPO algorithm with an efficient hybrid attention design for reinforcement learning training, MiniMax-M1 achieves industry-leading performance in long-context reasoning and real-world software engineering scenarios.","endpoints":["chat/completions"],"features":["function-calling","reasoning","serverless"],"pricing":{"inputPerM":5500,"outputPerM":22000},"source":"public-provider-conf"},"vision":false},{"id":"minimax/minimax-m2.1","name":"Minimax M2.1","display_name":"Minimax M2.1","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":204800,"output":131072},"metadata":{"description":"MiniMax M2.1 is a cutting-edge AI model designed to revolutionize how developers build software. With enhanced multi-language programming support, it excels in generating high-quality code across popular languages like Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, and JavaScript.\n\nKey improvements include:\n\n22% faster response times and 30% lower token consumption for efficient workflows.\nSeamless integration with leading development frameworks (Claude Code, Droid Factory AI, BlackBox, etc.).\nFull-stack development capabilities, from mobile (Android/iOS) to web and 3D interactive prototyping.\nOptimized performance-to-cost ratio, making AI-assisted development more accessible.\nWhether you're a software engineer, app developer, or tech innovator, M2.1 empowers smarter coding with industry-leading AI.","endpoints":["chat/completions","anthropic"],"features":["function-calling","structured-outputs","reasoning","serverless"],"pricing":{"inputPerM":3000,"outputPerM":12000},"source":"public-provider-conf"},"vision":false,"extra_capabilities":{"reasoning":{"supported":true}}},{"id":"minimax/minimax-m2.5","name":"MiniMax M2.5","display_name":"MiniMax M2.5","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":204800,"output":131072},"metadata":{"description":"MiniMax M2.1 is a cutting-edge AI model designed to revolutionize how developers build software. With enhanced multi-language programming support, it excels in generating high-quality code across popular languages like Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, and JavaScript.\n\nKey improvements include:\n\n22% faster response times and 30% lower token consumption for efficient workflows.\nSeamless integration with leading development frameworks (Claude Code, Droid Factory AI, BlackBox, etc.).\nFull-stack development capabilities, from mobile (Android/iOS) to web and 3D interactive prototyping.\nOptimized performance-to-cost ratio, making AI-assisted development more accessible.\nWhether you're a software engineer, app developer, or tech innovator, M2.1 empowers smarter coding with industry-leading AI.","endpoints":["chat/completions","anthropic"],"features":["function-calling","structured-outputs","reasoning","serverless"],"pricing":{"inputPerM":3000,"outputPerM":12000},"source":"public-provider-conf"},"vision":false,"extra_capabilities":{"reasoning":{"supported":true}}},{"id":"minimax/minimax-m2.7-highspeed","name":"MiniMax M2.7-highspeed","display_name":"MiniMax M2.7-highspeed","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":204800,"output":131072},"metadata":{"description":"MiniMax M2.7-highspeed is an accelerated SOTA model engineered for scenarios demanding extreme efficiency. It perfectly inherits the core intelligence and robust digital workspace capabilities of the standard M2.7.\nIn real-world software engineering, M2.7 excels by independently driving end-to-end project delivery while efficiently handling advanced tasks such as log analysis, bug troubleshooting, code security, and machine learning. In the professional workspace, it boasts the highest open-source GDPval-AA score (1495 ELO). It delivers high-fidelity, complex editing and multi-turn revisions across the Office suite (Excel, PPT, Word), elevating task execution to industry-leading standards.\nBuilt for complex environment interactions, M2.7 maintains an impressive 97% skill-following rate even with complex, long-context tool calls (>2000 tokens).  Beyond its robust productivity, M2.7 breaks the \"cold tool\" stereotype of traditional models. With exceptional identity retention and high emotional intelligence (EQ),","endpoints":["chat/completions","anthropic"],"features":["function-calling","structured-outputs","reasoning","serverless"],"pricing":{"inputPerM":6000,"outputPerM":24000},"source":"public-provider-conf"},"vision":false,"extra_capabilities":{"reasoning":{"supported":true,"interleaved":true,"summaries":true,"visibility":"summary","continuation":["thinking_blocks"]}}},{"id":"minimax/minimax-m2.5-highspeed","name":"MiniMax M2.5-highspeed","display_name":"MiniMax M2.5-highspeed","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":204800,"output":131100},"metadata":{"description":"MiniMax M2.5-highspeed is an accelerated SOTA model engineered for scenarios demanding extreme efficiency. It perfectly inherits the core intelligence and robust digital workspace capabilities of the standard M2.5—including its 80.2% score on SWE-Bench Verified, seamless manipulation of Office documents, and versatility in cross-software collaboration. With zero compromise on reasoning precision or logical depth, the Highspeed version delivers ultra-low latency inference through rigorous engineering optimization. This means you get more than just an intelligent assistant capable of planning and self-optimization; you gain a \"high-velocity engine\" that responds to high-frequency calls and processes complex document streams in near real-time, making it ideal for latency-sensitive interactive applications and large-scale automated pipelines.","endpoints":["chat/completions","anthropic"],"features":["function-calling","structured-outputs","reasoning","serverless"],"pricing":{"inputPerM":6000,"outputPerM":24000},"source":"public-provider-conf"},"vision":false,"extra_capabilities":{"reasoning":{"supported":true}}},{"id":"mistralai/mistral-7b-instruct","name":"Mistral 7B Instruct","display_name":"Mistral 7B Instruct","type":"chat","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":32768,"output":8192},"metadata":{"description":"A high-performing, industry-standard 7.3B parameter model, with optimizations for speed and context length.","endpoints":["chat/completions","completions"],"features":["serverless"],"pricing":{"inputPerM":290,"outputPerM":590},"source":"public-provider-conf"},"vision":false},{"id":"gryphe/mythomax-l2-13b","name":"Mythomax L2 13B","display_name":"Mythomax L2 13B","type":"chat","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":4096,"output":4096},"metadata":{"description":"The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to have resulted in a model that exceeds at both, confirming my theory. (More details to be released at a later time).","endpoints":["chat/completions","completions"],"features":["serverless"],"pricing":{"inputPerM":900,"outputPerM":900},"source":"public-provider-conf"},"vision":false},{"id":"nova-2-Lite","name":"nova-2-Lite","display_name":"nova-2-Lite","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text","image"],"output":["text"]},"limit":{"context":1000000,"output":1000000},"metadata":{"endpoints":["chat/completions","anthropic"],"features":["function-calling","structured-outputs","serverless"],"pricing":{"inputPerM":3000,"outputPerM":25000},"source":"public-provider-conf"},"vision":true},{"id":"qwen/qwen-2.5-72b-instruct","name":"Qwen 2.5 72B Instruct","display_name":"Qwen 2.5 72B Instruct","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":32000,"output":8192},"metadata":{"description":"Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters.","endpoints":["chat/completions","completions"],"features":["function-calling","structured-outputs","serverless"],"pricing":{"inputPerM":3800,"outputPerM":4000},"source":"public-provider-conf"},"vision":false},{"id":"qwen/qwen-mt-plus","name":"Qwen MT Plus","display_name":"Qwen MT Plus","type":"chat","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":4096,"output":2048},"metadata":{"description":"Qwen-MT is a large language model optimized for machine translation, built upon the foundation of the Tongyi Qianwen model. It supports translation across 92 languages — including Chinese, English, Japanese, Korean, French, Spanish, German, Thai, Indonesian, Vietnamese, Arabic, and more — enabling seamless multilingual communication.","endpoints":["chat/completions"],"features":["serverless"],"pricing":{"inputPerM":2500,"outputPerM":7500},"source":"public-provider-conf"},"vision":false},{"id":"qwen/qwen2.5-7b-instruct","name":"Qwen2.5 7B Instruct","display_name":"Qwen2.5 7B Instruct","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":32000,"output":32000},"metadata":{"description":"Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. 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More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. - Long-context Support up to 128K tokens and can generate up to 8K tokens. - Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.","endpoints":["chat/completions","completions"],"features":["function-calling","structured-outputs","serverless"],"pricing":{"inputPerM":700,"outputPerM":700},"source":"public-provider-conf"},"vision":false},{"id":"qwen/qwen2.5-vl-72b-instruct","name":"Qwen2.5 VL 72B Instruct","display_name":"Qwen2.5 VL 72B Instruct","type":"chat","reasoning":{"supported":false},"tool_call":false,"modalities":{"input":["text","image","video"],"output":["text"]},"limit":{"context":32768,"output":32768},"metadata":{"description":"Qwen2.5-VL, the latest vision-language model in the Qwen2.5 series, delivers enhanced multimodal capabilities including advanced visual comprehension for object/text recognition, chart/layout analysis, and agent-based dynamic tool orchestration. It processes long-form videos (>1 hour) with key event detection while enabling precise spatial annotation through bounding boxes or coordinate points. The model specializes in structured data extraction from scanned documents (invoices, tables, etc.) and achieves state-of-the-art performance across multimodal benchmarks encompassing image understanding, temporal video analysis, and agent task evaluations.","endpoints":["chat/completions","completions"],"features":["serverless"],"pricing":{"inputPerM":8000,"outputPerM":8000},"source":"public-provider-conf"},"vision":true},{"id":"qwen/qwen3-30b-a3b-fp8","name":"Qwen3 30B A3B","display_name":"Qwen3 30B A3B","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":false,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":40960,"output":20000},"metadata":{"description":"Achieves effective integration of inference and non-inference modes, allowing seamless switching between modes during conversations. Its inference capability matches that of QwQ-32B with a smaller parameter size, and its general capabilities significantly surpass those of Qwen2.5-14B, reaching the state-of-the-art (SOTA) level among models of the same scale.","endpoints":["chat/completions","completions"],"features":["reasoning","serverless"],"pricing":{"inputPerM":900,"outputPerM":4500},"source":"public-provider-conf"},"vision":false},{"id":"qwen/qwen3-32b-fp8","name":"Qwen3 32B","display_name":"Qwen3 32B","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":false,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":40960,"output":20000},"metadata":{"description":"Achieves effective integration of inference and non-inference modes, allowing seamless switching between modes during conversations. Its inference capability matches that of QwQ-32B with a smaller parameter size, and its general capabilities significantly surpass those of Qwen2.5-14B, reaching the state-of-the-art (SOTA) level among models of the same scale.","endpoints":["chat/completions","completions"],"features":["reasoning","serverless"],"pricing":{"inputPerM":1000,"outputPerM":4500},"source":"public-provider-conf"},"vision":false},{"id":"qwen/qwen3-235b-a22b-fp8","name":"Qwen3 235B A22B","display_name":"Qwen3 235B A22B","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":false,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":40960,"output":20000},"metadata":{"description":"Achieves effective integration of inference and non-inference modes, enabling seamless switching between modes during conversations. The model's inference capability significantly surpasses that of QwQ, and its general capabilities exceed those of Qwen2.5-72B-Instruct, reaching the state-of-the-art (SOTA) level among models of the same scale.","endpoints":["chat/completions","completions"],"features":["reasoning","serverless"],"pricing":{"inputPerM":2000,"outputPerM":8000},"source":"public-provider-conf"},"vision":false},{"id":"qwen/qwen3-235b-a22b-thinking-2507","name":"Qwen3 235B A22b Thinking 2507","display_name":"Qwen3 235B A22b Thinking 2507","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":131072,"output":131072},"metadata":{"description":"The Qwen3-235B-A22B-Thinking-2507 represents the newest thinking-enabled model in the Qwen3 series, delivering groundbreaking improvements in reasoning capabilities. This advanced AI demonstrates significantly enhanced performance across logical reasoning, mathematics, scientific analysis, coding tasks, and academic benchmarks - matching or even surpassing human-expert level performance to achieve state-of-the-art results among open-source thinking models. Beyond its exceptional reasoning skills, the model shows markedly better general capabilities including more precise instruction following, sophisticated tool usage, highly natural text generation, and improved alignment with human preferences. It also features enhanced 256K long-context understanding, allowing it to maintain coherence and depth across extended documents and complex discussions.","endpoints":["chat/completions","completions","anthropic"],"features":["function-calling","structured-outputs","reasoning","serverless"],"pricing":{"inputPerM":3000,"outputPerM":30000},"source":"public-provider-conf"},"vision":false,"extra_capabilities":{"reasoning":{"supported":true,"interleaved":true,"summaries":true,"visibility":"summary","continuation":["thinking_blocks"]}}},{"id":"qwen/qwen3-coder-480b-a35b-instruct","name":"Qwen3 Coder 480B A35B Instruct","display_name":"Qwen3 Coder 480B A35B Instruct","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":262144,"output":65536},"metadata":{"description":"Qwen3-Coder-480B-A35B-Instruct is a cutting-edge open coding model from Qwen, matching Claude Sonnet’s performance in agentic programming, browser automation, and core development tasks. With native 256K context (extendable to 1M tokens via YaRN), it excels at repository-scale analysis and features specialized function-call support for platforms like Qwen Code and CLINE—making it ideal for complex, real-world development workflows.","endpoints":["chat/completions","completions","anthropic"],"features":["function-calling","structured-outputs","serverless"],"pricing":{"inputPerM":3800,"outputPerM":15500},"source":"public-provider-conf"},"vision":false},{"id":"qwen/qwen3-coder-next","name":"Qwen3 Coder Next FP8","display_name":"Qwen3 Coder Next FP8","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":262144,"output":65536},"metadata":{"description":"Qwen3-Coder-Next is an open-weight language model specifically engineered for coding agents and local development environments. This highly efficient model delivers exceptional performance with only 3B activated parameters out of 80B total parameters, achieving results comparable to models with 10-20x more active parameters while maintaining remarkable cost-effectiveness for agent deployment. Through its sophisticated training methodology, Qwen3-Coder-Next excels in advanced agentic capabilities including long-horizon reasoning, complex tool usage, and robust recovery from execution failures, ensuring reliable performance across dynamic coding tasks. The model's versatility is further enhanced by its 256k context length and adaptability to various scaffold templates, enabling seamless integration with diverse CLI/IDE platforms such as Claude Code, Qwen Code, Qoder, Kilo, Trae, and Cline, making it an ideal solution for comprehensive development environments.","endpoints":["chat/completions","completion","anthropic"],"features":["function-calling","structured-outputs","serverless"],"pricing":{"inputPerM":2000,"outputPerM":15000},"source":"public-provider-conf"},"vision":false},{"id":"qwen/qwen3-next-80b-a3b-instruct","name":"Qwen3 Next 80B A3B Instruct","display_name":"Qwen3 Next 80B A3B Instruct","type":"chat","reasoning":{"supported":false},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":65536,"output":65536},"metadata":{"description":"Qwen3-Next uses a highly sparse MoE design: 80B total parameters, but only ~3B activated per inference step. Experiments show that, with global load balancing, increasing total expert parameters while keeping activated experts fixed steadily reduces training loss.Compared to Qwen3’s MoE (128 total experts, 8 routed), Qwen3-Next expands to 512 total experts, combining 10 routed experts + 1 shared expert — maximizing resource usage without hurting performance. The Qwen3-Next-80B-A3B-Instruct performs comparably to our flagship model Qwen3-235B-A22B-Instruct-2507, and shows clear advantages in tasks requiring ultra-long context (up to 256K tokens).","endpoints":["chat/completions","completions","anthropic"],"features":["function-calling","structured-outputs","serverless"],"pricing":{"inputPerM":1500,"outputPerM":15000},"source":"public-provider-conf"},"vision":false},{"id":"qwen/qwen3-next-80b-a3b-thinking","name":"Qwen3 Next 80B A3B Thinking","display_name":"Qwen3 Next 80B A3B Thinking","type":"chat","reasoning":{"supported":true,"default":true},"tool_call":true,"modalities":{"input":["text"],"output":["text"]},"limit":{"context":65536,"output":65536},"metadata":{"description":"Qwen3-Next uses a highly sparse MoE design: 80B total parameters, but only ~3B activated per inference step. Experiments show that, with global load balancing, increasing total expert parameters while keeping activated experts fixed steadily reduces training loss.Compared to Qwen3’s MoE (128 total experts, 8 routed), Qwen3-Next expands to 512 total experts, combining 10 routed experts + 1 shared expert — maximizing resource usage without hurting performance. 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