[whatmodel.sh](../../README.md) / Qwen3 Coder 30B-A3B Instruct # Qwen3 Coder 30B-A3B Instruct Alibaba · released 2025-07 · 30.5B params (3.3B active) · apache-2.0 **score 57.4** ±1.1 · #238 of 732 · from 13 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Qwen3 Coder 30B-A3B Instruct among every model on that task or test, worst to best. 
task         vs the others         rank  counts  tests
agent tasks  ▁▁▃▆█●▅▅▄▄▂▁   #182 of 313     59%      8
coding       ▁▁▁▃▅▇●▇▅▄▃▁   #226 of 469     72%      7
agent tasks · #182 of 313 · 59% of its score Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 0528](deepseek-r1-0528.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Jamba 1.5 Large](jamba-1-5-large.md) here, though they rank lower overall. 
test                               result  vs the others in its run    difficulty            counts  why
SWE-bench Verified                  31.8%  ●▄·····▄▄▄▄█ beats 0/7      hard · avg 16%           21%  sharp · at its level · several runs
SWE-bench Multilingual              20.7%  ●·▄·····█▄█▄ beats 0/7      hard · avg 18%           11%  at its level · several runs
Terminal-Bench 2.1                  10.1%  ●······█▄▄█▄ beats 0/7      very hard · avg 7.2%    8.9%  sharp
SWE-bench Pro (split unspecified)   19.8%  ▄···●···▄▄██ beats 1/7      very hard · avg 6.2%    7.3%  sharp
τ-bench Retail (maker)              68.7%  ▁····▃·▂▇●▂▆ beats 19/28    medium · avg 46%        5.4%  at its level · small run
τ-bench Airline (maker)             48.0%  ▁▁▁·▅▄▁█●▁▁▇ beats 19/28    hard · avg 29%          3.9%  at its level · small run
BFCL v3 (maker)                     62.2%  ▁▁▁▁▁▃▅█●▅▄▁ beats 105/171  medium · avg 56%        1.6%  blunt · at its level · small run
SWE-bench Live (maker)              20.7%  ●··········█ beats 0/1                                 —  not counted yet: no other model in the …
coding · #226 of 469 · 72% of its score Beats [Qwen3.5 4B](qwen3-5-4b.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md), [s1.1 32B](s1-1-32b.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty            counts  why
SWE-bench Verified                   31.8%  ●▄·····▄▄▄▄█ beats 0/7    hard · avg 16%           21%  sharp · at its level · several runs
Aider polyglot (225 exercises, tw…   33.3%  ▂▁▁▃▆▃●▆█▃▄▂ beats 28/71  very hard · avg 10%      18%  sharp · at its level · small run
SWE-bench Multilingual               20.7%  ●·▄·····█▄█▄ beats 0/7    hard · avg 18%           11%  at its level · several runs
Luau Leetcode fully solved (Torpe…   38.5%  ▁▁▁▁·●▂▃▅▃█▇ beats 6/49   hard · avg 17%           10%  sharp · at its level
SWE-bench Pro (split unspecified)    19.8%  ▄···●···▄▄██ beats 1/7    very hard · avg 6.2%    7.3%  sharp
SciCode (level unspecified)          18.3%  ●···▄▄█▄·▄·▄ beats 0/7    very hard · avg 13%     4.8%  typical
SWE-bench Live (maker)               20.7%  ●··········█ beats 0/1                               —  not counted yet: no other model in the …
other tests (1) 
test                                result  vs the others in its run  difficulty      counts  why
RobloxQA v2.0 multiple choice (To…   85.0%  ▁▁●▁█▄▃▃▆▂▂▂ beats 6/49   easy · avg 77%    7.6%  typical
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Kwaipilot/KAT-Coder-V2.5-Dev model card         5   41% ▇▇▇▇▇
Qwen/Qwen3-Coder-30B-A3B-Instruct model…        6   41% ▇▇▇▇▇
Roblox LLM Leaderboard                          2   18% ▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 57.4
Licences: Kwaipilot/KAT-Coder-V2.5-Dev model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Qwen/Qwen3-Coder-30B-A3B-Instruct model card ([Apache-2.0](../../licenses/card-qwen-qwen3-coder-30b-a3b-instruct.txt)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt))
Not counted (1) 
source                  test            score  why
Qwen/Qwen3-Coder-30B-…  SWE-bench Live  20.7%  too few models have it yet
## How to run it ### Through an API api (Novita AI) · $0.12/M blended · $0.07 in / $0.27 out per 1M tokens · [novita.ai](https://novita.ai/models) [price source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): USD per 1M text tokens, standard uncached input and output. Model endpoint qwen/qwen3-coder-30b-a3b-instruct. Product: Serverless chat completions. API model: qwen/qwen3-coder-30b-a3b-instruct. Settings: service tier=standard, batch=no, cache=uncached input. Additional/excluded charges: Tools, cache storage, priority service and other modalities excluded.. Currency: USD. · [context source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): provider maximum input tokens; not an intrinsic model maximum or legacy max\_tokens ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Qwen3 Coder 30B-A3B Instruct (Q4_K_M, needs ~18.6 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-GGUF/tree/1f4ceb1041258b3fbfe59e1175d1321c6b41863b)) ```sh llama cli -hf lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q4_K_M ``` Other versions: Q8_0 (~32.5 GB) `llama cli -hf lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-GGUF:Q8_0` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Qwen3 Coder 30B-A3B Instruct (Q4_K_M, needs ~18.6 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-GGUF/tree/1f4ceb1041258b3fbfe59e1175d1321c6b41863b)) ```sh lms get https://huggingface.co/lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q8_0 (~32.5 GB) `lms get https://huggingface.co/lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-GGUF@Q8_0 -y` · 4bit (~17.2 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-4bit -y` · 8bit (~32.4 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-8bit -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Qwen3 Coder 30B-A3B Instruct (Q4_K_M, needs ~18.8 GB, from [ollama.com](https://ollama.com/library/qwen3-coder/tags)) ```sh ollama run qwen3-coder:30b ``` Other versions: Q8_0 (~37.1 GB) `ollama run qwen3-coder:30b-a3b-q8_0` ### MLX (Mac) 1. Install MLX (Mac). Apple silicon Macs only, text only. ```sh # with uv uv tool install mlx-lm # or pip install mlx-lm ``` 2. Run Qwen3 Coder 30B-A3B Instruct (4bit, needs ~17.2 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-4bit/tree/bbecedefd826819d2c6b6465e88ca7b9b8ad3407)) ```sh mlx_lm.chat --model lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-4bit ``` Other versions: 8bit (~32.4 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Qwen3-Coder-30B-A3B-Instruct-MLX-8bit` Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct) ## Published speed measurements Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine. 
hardware                      precision          speed  engine and settings
NVIDIA DGX Spark (GB10)       Q8_0         61.06 tok/s  llama.cpp 11fb327bf (7941), CUDA …
NVIDIA DGX Spark (GB10)       Q8_0         54.77 tok/s  llama.cpp 11fb327bf (7941), CUDA …
NVIDIA DGX Spark (GB10)       Q8_0         30.21 tok/s  llama.cpp 11fb327bf (7941), CUDA …
Apple M2 Ultra (GPU core co…  Q8_0         78.97 tok/s  llama.cpp b828e18c7 (7948), Metal…
Apple M2 Ultra (GPU core co…  Q8_0         71.18 tok/s  llama.cpp b828e18c7 (7948), Metal…
Apple M2 Ultra (GPU core co…  Q8_0         45.26 tok/s  llama.cpp b828e18c7 (7948), Metal…
NVIDIA DGX Spark (GB10)       Q8_0         48.02 tok/s  llama.cpp 11fb327bf (7941), CUDA …
NVIDIA DGX Spark (GB10)       Q8_0         40.23 tok/s  llama.cpp 11fb327bf (7941), CUDA …
Apple M2 Ultra (GPU core co…  Q8_0         65.11 tok/s  llama.cpp b828e18c7 (7948), Metal…
Apple M2 Ultra (GPU core co…  Q8_0         56.87 tok/s  llama.cpp b828e18c7 (7948), Metal…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   94.64 tok/s  llama.cpp c841aee (build_number 1…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S      100.99 tok/s  llama.cpp 0eca4d490 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   99.55 tok/s  llama.cpp 0eca4d490 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S       98.48 tok/s  llama.cpp 4fc4ec554 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   97.01 tok/s  llama.cpp 4fc4ec554 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S       98.12 tok/s  llama.cpp cb295bf59 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   96.53 tok/s  llama.cpp cb295bf59 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S        94.2 tok/s  llama.cpp ac4cddeb0 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   92.84 tok/s  llama.cpp 2016bf2 (build_number 1…
AMD Ryzen AI Max+ 395 (Stri…  IQ4_XS       90.44 tok/s  llama.cpp 1fd5f4803 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S       98.51 tok/s  llama.cpp b81c2cdd7 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S       95.55 tok/s  llama.cpp de6f727aa (build_number…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S       93.85 tok/s  llama.cpp d4c8e2c29 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S       97.23 tok/s  llama.cpp 6b4e4bd58 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S       96.27 tok/s  llama.cpp 192d8ae (build_number 1…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   96.76 tok/s  llama.cpp 2496f9c14 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL    92.6 tok/s  llama.cpp 6b4e4bd58 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   94.15 tok/s  llama.cpp 192d8ae (build_number 1…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S       97.22 tok/s  llama.cpp b81c2cdd7 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   96.15 tok/s  llama.cpp 2496f9c14 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   73.69 tok/s  llama.cpp b1c70e2e5 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  Q4_K_S       98.02 tok/s  llama.cpp 98d5e8ba8 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   97.08 tok/s  llama.cpp 98d5e8ba8 (build_number…
AMD Ryzen AI Max+ 395 (Stri…  UD-Q4_K_XL   87.11 tok/s  llama.cpp b8460, Vulkan (RADV) · …
AMD Ryzen AI Max+ 395 (Stri…  Q8_0          51.4 tok/s  Ollama 0.20.4, Ollama Vulkan RADV…
AMD Ryzen AI Max+ 395 (Stri…  Q8_0          51.3 tok/s  Ollama 0.20.4, Ollama Vulkan RADV…
Intel Raptor Lake-P [Iris X…  q4_k_m        3.18 tok/s  ollama (version not recorded) · l…
Intel Raptor Lake-P [Iris X…  q4_k_m        3.21 tok/s  ollama (version not recorded) · l…
Each speed links to its source, which has the full settings. [interactive version](https://whatmodel.sh/model/qwen3-coder-30b-a3b) · [JSON](https://whatmodel.sh/api?format=json&all=1)