{ "model_id": "google/gemini-4-argon", "provider_id": "google", "name": "Gemini 4 Argon", "description": "Google's frontier model for deep work on complex, real-world tasks across coding, finance, legal, cybersecurity and science. The first model in the Gemini 4 family.", "task": "Text Generation", "tags": [ "LLM", "Multimodal", "Reasoning", "Agentic", "Coding" ], "context_length": 1048576, "max_output_tokens": 65536, "schema_version": "1.0.0", "pricing": { "Input tokens (per 1M)": 2, "Cached input tokens (per 1M)": 0.1, "Output tokens (per 1M)": 10 }, "examples": [ { "name": "Simple Question", "description": "Basic generateContent request", "input": { "contents": [ { "parts": [ { "text": "What are the key differences between optimistic and pessimistic concurrency control in distributed systems?" } ], "role": "user" } ] }, "output": { "text": "In distributed systems, concurrency control mechanisms ensure that multiple nodes can access and modify shared data simultaneously without violating data integrity (ACID properties). \n\nThe choice between **Pessimistic Concurrency Control (PCC)** and **Optimistic Concurrency Control (OCC)** comes down to a fundamental philosophical question: *How likely is it that two nodes will try to change the same data at the exact same time?*\n\nHere are the key differences between the two approaches in a distributed context.\n\n---\n\n### 1. Core Philosophy\n* **Pessimistic (PCC):** Assumes conflicts are **frequent**. It prevents conflicts before they happen by blocking other transactions from accessing the data currently being used. Philosophy: *\"Ask for permission.\"*\n* **Optimistic (OCC):** Assumes conflicts are **rare**. It allows transactions to proceed without locking resources and only checks for conflicts right before committing the data. Philosophy: *\"Ask for forgiveness.\"*\n\n### 2. Mechanism of Action\n* **Pessimistic:** Uses **Distributed Locks** (e.g., Two-Phase Locking, or 2PL). \n 1. A node requests a lock on a record from a lock manager (or via consensus).\n 2. Once acquired, it reads, modifies, and writes the data.\n 3. It releases the lock. Other nodes must wait in a queue until the lock is released.\n* **Optimistic:** Uses **Timestamps or Version Numbers** (e.g., Multi-Version Concurrency Control, or MVCC).\n 1. **Read Phase:** A node reads the data and its version number into a local workspace.\n 2. **Modify Phase:** The node makes changes locally.\n 3. **Validate & Write Phase:** The node checks if the shared data\u2019s version number has changed since the Read Phase. If it hasn't, it commits and increments the version. If it *has* changed (a conflict occurred), the transaction is aborted and must be retried.\n\n### 3. Impact of Network Latency (The Distributed Factor)\nIn a single-node database, locks take microseconds. In a distributed system, locks require network round-trips, which take milliseconds. This magnifies the differences:\n* **Pessimistic:** Holding a lock over a network is expensive. If Node A locks a row and experiences a network delay, Nodes B, C, and D are completely blocked. This drastically reduces system throughput.\n* **Optimistic:** Because there are no locks, nodes do not wait on network round-trips from other nodes to read or process data. Network overhead is only incurred during the final validation phase, leading to much higher throughput in low-conflict scenarios.\n\n### 4. Failure Modes: Deadlocks vs. Starvation\n* **Pessimistic (Deadlocks):** Because nodes hold locks while waiting for other locks, **distributed deadlocks** are a major risk (Node A waits for Node B, while Node B waits for Node A). Detecting and resolving deadlocks across a network is complex and resource-intensive. Furthermore, if a node crashes while holding a lock, the system stalls until a timeout occurs.\n* **Optimistic (Starvation/Live-locks):** OCC is deadlock-free because no locks are held. However, it suffers from **wasted work**. If contention is high, transactions will constantly fail validation and roll back. A long-running transaction might be repeatedly aborted by shorter transactions, leading to \"starvation\" (never being able to complete).\n\n### 5. Performance Under Contention\n* **High Contention (Many writes to the same data):** **PCC wins.** Although nodes have to wait in line, every transaction that gets a lock is guaranteed to succeed. In OCC, high contention leads to a \"retry storm,\" where the network is flooded with aborted transactions trying to run again.\n* **Low Contention (Read-heavy, or writes to different data):** **OCC wins.** Transactions fly through the system without the heavy overhead of acquiring and releasing distributed locks. \n\n---\n\n### Summary Comparison Table\n\n| Feature | Pessimistic Concurrency (PCC) | Optimistic Concurrency (OCC) |\n| :--- | :--- | :--- |\n| **Conflict Assumption** | Conflicts are likely. | Conflicts are rare. |\n| **When is conflict detected?** | When requesting access to data. | At the very end, before committing. |\n| **Conflict Resolution** | Block/Wait until the lock is free. | Abort/Rollback and retry the transaction. |\n| **Primary Overhead** | Lock management and network wait times. | Wasted compute/network on rollbacks. |\n| **Major Risk** | Distributed Deadlocks & Node Crashes. | Transaction Starvation & Retry Storms. |\n| **Best Workload** | Write-heavy, high contention. | Read-heavy, low contention. |\n\n### Real-World Distributed Examples\n* **Pessimistic:** Financial ledgers (preventing double-spending), ticket/inventory booking systems (where 1,000 users try to buy the last concert ticket simultaneously). *Tech: Google Cloud Spanner (uses Paxos and 2PL for writes), Apache ZooKeeper (for distributed locking).*\n* **Optimistic:** Content management systems, Wikipedia, e-commerce product catalogs, and most microservices APIs (using HTTP `ETags` for optimistic locking). *Tech: CockroachDB (uses Timestamp Ordering), Amazon DynamoDB (supports conditional writes/optimistic locking).*" }, "raw_response": { "candidates": [ { "content": { "role": "model", "parts": [ { "text": "In distributed systems, concurrency control mechanisms ensure that multiple nodes can access and modify shared data simultaneously without violating data integrity (ACID properties). \n\nThe choice between **Pessimistic Concurrency Control (PCC)** and **Optimistic Concurrency Control (OCC)** comes down to a fundamental philosophical question: *How likely is it that two nodes will try to change the same data at the exact same time?*\n\nHere are the key differences between the two approaches in a distributed context.\n\n---\n\n### 1. Core Philosophy\n* **Pessimistic (PCC):** Assumes conflicts are **frequent**. It prevents conflicts before they happen by blocking other transactions from accessing the data currently being used. Philosophy: *\"Ask for permission.\"*\n* **Optimistic (OCC):** Assumes conflicts are **rare**. It allows transactions to proceed without locking resources and only checks for conflicts right before committing the data. Philosophy: *\"Ask for forgiveness.\"*\n\n### 2. Mechanism of Action\n* **Pessimistic:** Uses **Distributed Locks** (e.g., Two-Phase Locking, or 2PL). \n 1. A node requests a lock on a record from a lock manager (or via consensus).\n 2. Once acquired, it reads, modifies, and writes the data.\n 3. It releases the lock. Other nodes must wait in a queue until the lock is released.\n* **Optimistic:** Uses **Timestamps or Version Numbers** (e.g., Multi-Version Concurrency Control, or MVCC).\n 1. **Read Phase:** A node reads the data and its version number into a local workspace.\n 2. **Modify Phase:** The node makes changes locally.\n 3. **Validate & Write Phase:** The node checks if the shared data\u2019s version number has changed since the Read Phase. If it hasn't, it commits and increments the version. If it *has* changed (a conflict occurred), the transaction is aborted and must be retried.\n\n### 3. Impact of Network Latency (The Distributed Factor)\nIn a single-node database, locks take microseconds. In a distributed system, locks require network round-trips, which take milliseconds. This magnifies the differences:\n* **Pessimistic:** Holding a lock over a network is expensive. If Node A locks a row and experiences a network delay, Nodes B, C, and D are completely blocked. This drastically reduces system throughput.\n* **Optimistic:** Because there are no locks, nodes do not wait on network round-trips from other nodes to read or process data. Network overhead is only incurred during the final validation phase, leading to much higher throughput in low-conflict scenarios.\n\n### 4. Failure Modes: Deadlocks vs. Starvation\n* **Pessimistic (Deadlocks):** Because nodes hold locks while waiting for other locks, **distributed deadlocks** are a major risk (Node A waits for Node B, while Node B waits for Node A). Detecting and resolving deadlocks across a network is complex and resource-intensive. Furthermore, if a node crashes while holding a lock, the system stalls until a timeout occurs.\n* **Optimistic (Starvation/Live-locks):** OCC is deadlock-free because no locks are held. However, it suffers from **wasted work**. If contention is high, transactions will constantly fail validation and roll back. A long-running transaction might be repeatedly aborted by shorter transactions, leading to \"starvation\" (never being able to complete).\n\n### 5. Performance Under Contention\n* **High Contention (Many writes to the same data):** **PCC wins.** Although nodes have to wait in line, every transaction that gets a lock is guaranteed to succeed. In OCC, high contention leads to a \"retry storm,\" where the network is flooded with aborted transactions trying to run again.\n* **Low Contention (Read-heavy, or writes to different data):** **OCC wins.** Transactions fly through the system without the heavy overhead of acquiring and releasing distributed locks. \n\n---\n\n### Summary Comparison Table\n\n| Feature | Pessimistic Concurrency (PCC) | Optimistic Concurrency (OCC) |\n| :--- | :--- | :--- |\n| **Conflict Assumption** | Conflicts are likely. | Conflicts are rare. |\n| **When is conflict detected?** | When requesting access to data. | At the very end, before committing. |\n| **Conflict Resolution** | Block/Wait until the lock is free. | Abort/Rollback and retry the transaction. |\n| **Primary Overhead** | Lock management and network wait times. | Wasted compute/network on rollbacks. |\n| **Major Risk** | Distributed Deadlocks & Node Crashes. | Transaction Starvation & Retry Storms. |\n| **Best Workload** | Write-heavy, high contention. | Read-heavy, low contention. |\n\n### Real-World Distributed Examples\n* **Pessimistic:** Financial ledgers (preventing double-spending), ticket/inventory booking systems (where 1,000 users try to buy the last concert ticket simultaneously). *Tech: Google Cloud Spanner (uses Paxos and 2PL for writes), Apache ZooKeeper (for distributed locking).*\n* **Optimistic:** Content management systems, Wikipedia, e-commerce product catalogs, and most microservices APIs (using HTTP `ETags` for optimistic locking). *Tech: CockroachDB (uses Timestamp Ordering), Amazon DynamoDB (supports conditional writes/optimistic locking).*", "thoughtSignature": "AY89a1+B/ZlSIwm2seo+oToVuzFZ5gjCKXjrIWKdFu30/x3yV+dRnCO9o7n92KLTY3Q+yNN4+qz1hJ/uneonSSCnmL+wmT8GAGquO9yknltULvbJEQ==" } ] }, "finishReason": "STOP" } ], "usageMetadata": { "promptTokenCount": 46, "candidatesTokenCount": 1151, "totalTokenCount": 1197, "trafficType": "ON_DEMAND", "promptTokensDetails": [ { "modality": "TEXT", "tokenCount": 46 } ], "candidatesTokensDetails": [ { "modality": "TEXT", "tokenCount": 1151 } ] }, "modelVersion": "gemini-4-argon", "createTime": "2026-10-13T17:07:24.709810Z", "responseId": "TGXOarKpK_2p8sYPuKPGoQI" }, "code_snippets": [ { "label": "typescript", "language": "typescript", "code": "const response = await env.AI.run(\n 'google/gemini-4-argon',\n {\n contents: [\n {\n parts: [\n {\n text: 'What are the key differences between optimistic and pessimistic concurrency control in distributed systems?',\n },\n ],\n role: 'user',\n },\n ],\n },\n)\nconsole.log(response)" }, { "label": "curl", "language": "bash", "code": "curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \\\n --header \"Authorization: Bearer $CLOUDFLARE_API_TOKEN\" \\\n --header \"Content-Type: application/json\" \\\n --data '{\n \"model\": \"google/gemini-4-argon\",\n \"input\": {\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"What are the key differences between optimistic and pessimistic concurrency control in distributed systems?\"\n }\n ],\n \"role\": \"user\"\n }\n ]\n }\n}'" } ] }, { "name": "Agentic Coding", "description": "Coding task with a system instruction", "input": { "contents": [ { "parts": [ { "text": "Write a Python function that merges two sorted lists into one sorted list." } ], "role": "user" } ], "systemInstruction": { "parts": [ { "text": "You are an expert software engineering agent. Write concise, correct, well-documented code." } ] } }, "output": { "text": "Here are the two best ways to merge two sorted lists in Python: one using the standard library (recommended for production) and one using the classic two-pointer algorithm (common in interviews).\n\n### 1. The Pythonic Way (Recommended)\nPython\u2019s built-in `heapq.merge` is specifically designed for this. It is implemented in C, making it fast and memory-efficient.\n\n```python\nimport heapq\nfrom typing import List, TypeVar\n\nT = TypeVar(\"T\")\n\n\ndef merge_sorted_lists(list1: List[T], list2: List[T]) -> List[T]:\n \"\"\"Merges two sorted lists into a single sorted list using heapq.\n\n Time Complexity: O(n + m)\n Space Complexity: O(n + m) to store the resulting list.\n \"\"\"\n return list(heapq.merge(list1, list2))\n\n\n# Example usage:\n# print(merge_sorted_lists([1, 3, 5], [2, 4, 6])) # Output: [1, 2, 3, 4, 5, 6]\n```\n\n### 2. The Algorithmic Way (Two-Pointer Approach)\nIf you are in a coding interview and cannot use built-in libraries, use the two-pointer approach.\n\n```python\nfrom typing import List, TypeVar\n\nT = TypeVar(\"T\")\n\n\ndef merge_sorted_lists_manual(list1: List[T], list2: List[T]) -> List[T]:\n \"\"\"Merges two sorted lists into a single sorted list using two pointers.\n\n Args:\n list1: The first sorted list.\n list2: The second sorted list.\n\n Returns:\n A new sorted list containing all elements from list1 and list2.\n\n Time Complexity: O(n + m) where n and m are the lengths of the lists.\n Space Complexity: O(n + m) for the output list.\n \"\"\"\n merged = []\n i, j = 0, 0\n len1, len2 = len(list1), len(list2)\n\n # Traverse both lists and append the smaller element\n while i < len1 and j < len2:\n if list1[i] <= list2[j]:\n merged.append(list1[i])\n i += 1\n else:\n merged.append(list2[j])\n j += 1\n\n # Append any remaining elements (only one of these will execute)\n merged.extend(list1[i:])\n merged.extend(list2[j:])\n\n return merged\n\n\n# Example usage:\n# print(merge_sorted_lists_manual([1, 3, 5], [2, 4, 6, 8])) # Output: [1, 2, 3, 4, 5, 6, 8]\n```\n\n*Note: For very small lists, `sorted(list1 + list2)` is also very fast in Python because Python's built-in Timsort algorithm is optimized to detect already-sorted subsections, giving it $O(n + m)$ performance in this specific case.*" }, "raw_response": { "candidates": [ { "content": { "role": "model", "parts": [ { "text": "Here are the two best ways to merge two sorted lists in Python: one using the standard library (recommended for production) and one using the classic two-pointer algorithm (common in interviews).\n\n### 1. The Pythonic Way (Recommended)\nPython\u2019s built-in `heapq.merge` is specifically designed for this. It is implemented in C, making it fast and memory-efficient.\n\n```python\nimport heapq\nfrom typing import List, TypeVar\n\nT = TypeVar(\"T\")\n\n\ndef merge_sorted_lists(list1: List[T], list2: List[T]) -> List[T]:\n \"\"\"Merges two sorted lists into a single sorted list using heapq.\n\n Time Complexity: O(n + m)\n Space Complexity: O(n + m) to store the resulting list.\n \"\"\"\n return list(heapq.merge(list1, list2))\n\n\n# Example usage:\n# print(merge_sorted_lists([1, 3, 5], [2, 4, 6])) # Output: [1, 2, 3, 4, 5, 6]\n```\n\n### 2. The Algorithmic Way (Two-Pointer Approach)\nIf you are in a coding interview and cannot use built-in libraries, use the two-pointer approach.\n\n```python\nfrom typing import List, TypeVar\n\nT = TypeVar(\"T\")\n\n\ndef merge_sorted_lists_manual(list1: List[T], list2: List[T]) -> List[T]:\n \"\"\"Merges two sorted lists into a single sorted list using two pointers.\n\n Args:\n list1: The first sorted list.\n list2: The second sorted list.\n\n Returns:\n A new sorted list containing all elements from list1 and list2.\n\n Time Complexity: O(n + m) where n and m are the lengths of the lists.\n Space Complexity: O(n + m) for the output list.\n \"\"\"\n merged = []\n i, j = 0, 0\n len1, len2 = len(list1), len(list2)\n\n # Traverse both lists and append the smaller element\n while i < len1 and j < len2:\n if list1[i] <= list2[j]:\n merged.append(list1[i])\n i += 1\n else:\n merged.append(list2[j])\n j += 1\n\n # Append any remaining elements (only one of these will execute)\n merged.extend(list1[i:])\n merged.extend(list2[j:])\n\n return merged\n\n\n# Example usage:\n# print(merge_sorted_lists_manual([1, 3, 5], [2, 4, 6, 8])) # Output: [1, 2, 3, 4, 5, 6, 8]\n```\n\n*Note: For very small lists, `sorted(list1 + list2)` is also very fast in Python because Python's built-in Timsort algorithm is optimized to detect already-sorted subsections, giving it $O(n + m)$ performance in this specific case.*", "thoughtSignature": "AY89a1+/LrK2jXxLmBaHprRBVeDNnI475SQ66hmgm0NAESde+zMoKWM25WR7J+tZyCu7245dEioqKBKXMg/oF8/iXUehMAw9jEXuwL4MyN9GhOjFXw==" } ] }, "finishReason": "STOP" } ], "usageMetadata": { "promptTokenCount": 68, "candidatesTokenCount": 715, "totalTokenCount": 783, "trafficType": "ON_DEMAND", "promptTokensDetails": [ { "modality": "TEXT", "tokenCount": 68 } ], "candidatesTokensDetails": [ { "modality": "TEXT", "tokenCount": 715 } ] }, "modelVersion": "gemini-4-argon", "createTime": "2026-10-13T17:07:38.420541Z", "responseId": "WmXOar3VGZ2skPsPzIu6wAY" }, "code_snippets": [ { "label": "typescript", "language": "typescript", "code": "const response = await env.AI.run(\n 'google/gemini-4-argon',\n {\n contents: [\n {\n parts: [\n { text: 'Write a Python function that merges two sorted lists into one sorted list.' },\n ],\n role: 'user',\n },\n ],\n systemInstruction: {\n parts: [\n {\n text: 'You are an expert software engineering agent. Write concise, correct, well-documented code.',\n },\n ],\n },\n },\n)\nconsole.log(response)" }, { "label": "curl", "language": "bash", "code": "curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \\\n --header \"Authorization: Bearer $CLOUDFLARE_API_TOKEN\" \\\n --header \"Content-Type: application/json\" \\\n --data '{\n \"model\": \"google/gemini-4-argon\",\n \"input\": {\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"Write a Python function that merges two sorted lists into one sorted list.\"\n }\n ],\n \"role\": \"user\"\n }\n ],\n \"systemInstruction\": {\n \"parts\": [\n {\n \"text\": \"You are an expert software engineering agent. Write concise, correct, well-documented code.\"\n }\n ]\n }\n }\n}'" } ] }, { "name": "Multi-turn Conversation", "description": "Continuing a multi-step coding conversation, typical of an agentic loop", "input": { "contents": [ { "parts": [ { "text": "I have a bug where my recursive fibonacci function is too slow for n=40." } ], "role": "user" }, { "parts": [ { "text": "That's expected with naive recursion since it recomputes overlapping subproblems exponentially. You can fix it with memoization or an iterative approach. Which would you like to see?" } ], "role": "model" }, { "parts": [ { "text": "Show me the memoized version." } ], "role": "user" } ] }, "output": { "text": "Here is how to fix it using memoization. Since you didn't specify a language, here are examples in Python and JavaScript (let me know if you are using something else).\n\n### Python\n\nIn Python, the easiest way is to use the built-in `@lru_cache` (or `@cache` in Python 3.9+) decorator. It automatically stores the results of previous function calls.\n\n```python\nfrom functools import lru_cache\n\n\n@lru_cache(maxsize=None)\ndef fib(n):\n if n <= 1:\n return n\n return fib(n - 1) + fib(n - 2)\n\n\nprint(fib(40)) # Runs instantly: 102334155\n```\n\n**Manual approach (without imports):**\nYou can also pass a dictionary to store the results manually.\n\n```python\ndef fib(n, memo={}):\n if n in memo:\n return memo[n]\n if n <= 1:\n return n\n\n memo[n] = fib(n - 1, memo) + fib(n - 2, memo)\n return memo[n]\n\n\nprint(fib(40)) # 102334155\n```\n\n### JavaScript\n\nIn JavaScript, you can pass an object (`memo`) as a default parameter to cache the results.\n\n```javascript\nfunction fib(n, memo = {}) {\n if (n in memo) return memo[n];\n if (n <= 1) return n;\n \n memo[n] = fib(n - 1, memo) + fib(n - 2, memo);\n return memo[n];\n}\n\nconsole.log(fib(40)); // Runs instantly: 102334155\n```\n\n### Why this fixes the bug:\n* **Before (Naive):** Calculating `fib(40)` required roughly **331 million** function calls ($O(2^n)$ time complexity) because it recalculated `fib(38)`, `fib(37)`, etc., millions of times.\n* **After (Memoized):** Each number's Fibonacci value is calculated only once and then retrieved from memory. Calculating `fib(40)` now takes only **79** function calls ($O(n)$ time complexity)." }, "raw_response": { "candidates": [ { "content": { "role": "model", "parts": [ { "text": "Here is how to fix it using memoization. Since you didn't specify a language, here are examples in Python and JavaScript (let me know if you are using something else).\n\n### Python\n\nIn Python, the easiest way is to use the built-in `@lru_cache` (or `@cache` in Python 3.9+) decorator. It automatically stores the results of previous function calls.\n\n```python\nfrom functools import lru_cache\n\n\n@lru_cache(maxsize=None)\ndef fib(n):\n if n <= 1:\n return n\n return fib(n - 1) + fib(n - 2)\n\n\nprint(fib(40)) # Runs instantly: 102334155\n```\n\n**Manual approach (without imports):**\nYou can also pass a dictionary to store the results manually.\n\n```python\ndef fib(n, memo={}):\n if n in memo:\n return memo[n]\n if n <= 1:\n return n\n\n memo[n] = fib(n - 1, memo) + fib(n - 2, memo)\n return memo[n]\n\n\nprint(fib(40)) # 102334155\n```\n\n### JavaScript\n\nIn JavaScript, you can pass an object (`memo`) as a default parameter to cache the results.\n\n```javascript\nfunction fib(n, memo = {}) {\n if (n in memo) return memo[n];\n if (n <= 1) return n;\n \n memo[n] = fib(n - 1, memo) + fib(n - 2, memo);\n return memo[n];\n}\n\nconsole.log(fib(40)); // Runs instantly: 102334155\n```\n\n### Why this fixes the bug:\n* **Before (Naive):** Calculating `fib(40)` required roughly **331 million** function calls ($O(2^n)$ time complexity) because it recalculated `fib(38)`, `fib(37)`, etc., millions of times.\n* **After (Memoized):** Each number's Fibonacci value is calculated only once and then retrieved from memory. Calculating `fib(40)` now takes only **79** function calls ($O(n)$ time complexity).", "thoughtSignature": "AY89a1/uBOBhQ0qUKNEsYwE4H9cWHXsKJeFhfnYkTwwTCLxszJv1hhQupgjY78MG1tiB7lewI70d3pD8vELzR5yam/Fy84daQ/NRt0Y1wlj/ZWq11A==" } ] }, "finishReason": "STOP" } ], "usageMetadata": { "promptTokenCount": 101, "candidatesTokenCount": 522, "totalTokenCount": 623, "trafficType": "ON_DEMAND", "promptTokensDetails": [ { "modality": "TEXT", "tokenCount": 101 } ], "candidatesTokensDetails": [ { "modality": "TEXT", "tokenCount": 522 } ] }, "modelVersion": "gemini-4-argon", "createTime": "2026-10-13T17:07:44.524175Z", "responseId": "YGXOao__H8mn8sYPvI7UuAU" }, "code_snippets": [ { "label": "typescript", "language": "typescript", "code": "const response = await env.AI.run(\n 'google/gemini-4-argon',\n {\n contents: [\n {\n parts: [{ text: 'I have a bug where my recursive fibonacci function is too slow for n=40.' }],\n role: 'user',\n },\n {\n parts: [\n {\n text: \"That's expected with naive recursion since it recomputes overlapping subproblems exponentially. You can fix it with memoization or an iterative approach. Which would you like to see?\",\n },\n ],\n role: 'model',\n },\n { parts: [{ text: 'Show me the memoized version.' }], role: 'user' },\n ],\n },\n)\nconsole.log(response)" }, { "label": "curl", "language": "bash", "code": "curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \\\n --header \"Authorization: Bearer $CLOUDFLARE_API_TOKEN\" \\\n --header \"Content-Type: application/json\" \\\n --data '{\n \"model\": \"google/gemini-4-argon\",\n \"input\": {\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"I have a bug where my recursive fibonacci function is too slow for n=40.\"\n }\n ],\n \"role\": \"user\"\n },\n {\n \"parts\": [\n {\n \"text\": \"That'\\''s expected with naive recursion since it recomputes overlapping subproblems exponentially. You can fix it with memoization or an iterative approach. Which would you like to see?\"\n }\n ],\n \"role\": \"model\"\n },\n {\n \"parts\": [\n {\n \"text\": \"Show me the memoized version.\"\n }\n ],\n \"role\": \"user\"\n }\n ]\n }\n}'" } ] }, { "name": "Spatial Reasoning", "description": "A reasoning-heavy puzzle with the thinking level set to HIGH", "input": { "contents": [ { "parts": [ { "text": "A cube is painted red on all faces, then cut into 27 equal smaller cubes. How many of the smaller cubes have exactly two red faces?" } ], "role": "user" } ], "generationConfig": { "thinkingConfig": { "thinkingLevel": "HIGH" } } }, "output": { "text": "There are **12** smaller cubes that have exactly two red faces.\n\nHere is the breakdown of why:\n\nSince the large cube is cut into 27 equal smaller cubes, it is divided into a **3 x 3 x 3 grid** (just like a standard Rubik's Cube). \n\nThe smaller cubes can be categorized by their position on the original large cube:\n* **3 red faces (Corners):** A cube has 8 corners, so there are **8** of these.\n* **2 red faces (Edges):** These are the cubes in the middle of each edge, between the corners. A cube has 12 edges, and since it is a 3x3x3 grid, there is 1 middle cube per edge. (12 edges \u00d7 1 cube = **12**).\n* **1 red face (Centers):** These are in the very center of each of the 6 faces. (6 faces \u00d7 1 center cube = **6**).\n* **0 red faces (Core):** This is the single cube hidden in the very center of the large cube (**1**).\n\n*(Total: 8 + 12 + 6 + 1 = 27 cubes)*" }, "raw_response": { "candidates": [ { "content": { "role": "model", "parts": [ { "text": "There are **12** smaller cubes that have exactly two red faces.\n\nHere is the breakdown of why:\n\nSince the large cube is cut into 27 equal smaller cubes, it is divided into a **3 x 3 x 3 grid** (just like a standard Rubik's Cube). \n\nThe smaller cubes can be categorized by their position on the original large cube:\n* **3 red faces (Corners):** A cube has 8 corners, so there are **8** of these.\n* **2 red faces (Edges):** These are the cubes in the middle of each edge, between the corners. A cube has 12 edges, and since it is a 3x3x3 grid, there is 1 middle cube per edge. (12 edges \u00d7 1 cube = **12**).\n* **1 red face (Centers):** These are in the very center of each of the 6 faces. (6 faces \u00d7 1 center cube = **6**).\n* **0 red faces (Core):** This is the single cube hidden in the very center of the large cube (**1**).\n\n*(Total: 8 + 12 + 6 + 1 = 27 cubes)*", "thoughtSignature": "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" } ] }, "finishReason": "STOP" } ], "usageMetadata": { "promptTokenCount": 62, "candidatesTokenCount": 266, "totalTokenCount": 1152, "trafficType": "ON_DEMAND", "promptTokensDetails": [ { "modality": "TEXT", "tokenCount": 62 } ], "candidatesTokensDetails": [ { "modality": "TEXT", "tokenCount": 266 } ], "thoughtsTokenCount": 824 }, "modelVersion": "gemini-4-argon", "createTime": "2026-10-13T17:07:50.028403Z", "responseId": "ZmXOavPdAbyt8sYPi8rYkAE" }, "code_snippets": [ { "label": "typescript", "language": "typescript", "code": "const response = await env.AI.run(\n 'google/gemini-4-argon',\n {\n contents: [\n {\n parts: [\n {\n text: 'A cube is painted red on all faces, then cut into 27 equal smaller cubes. How many of the smaller cubes have exactly two red faces?',\n },\n ],\n role: 'user',\n },\n ],\n generationConfig: { thinkingConfig: { thinkingLevel: 'HIGH' } },\n },\n)\nconsole.log(response)" }, { "label": "curl", "language": "bash", "code": "curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run \\\n --header \"Authorization: Bearer $CLOUDFLARE_API_TOKEN\" \\\n --header \"Content-Type: application/json\" \\\n --data '{\n \"model\": \"google/gemini-4-argon\",\n \"input\": {\n \"contents\": [\n {\n \"parts\": [\n {\n \"text\": \"A cube is painted red on all faces, then cut into 27 equal smaller cubes. How many of the smaller cubes have exactly two red faces?\"\n }\n ],\n \"role\": \"user\"\n }\n ],\n \"generationConfig\": {\n \"thinkingConfig\": {\n \"thinkingLevel\": \"HIGH\"\n }\n }\n }\n}'" } ] } ], "default_example": null, "code_snippets": [], "supports_async": false, "zdr": true, "zdr_comment": null, "external_info": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/gemini/4-argon", "terms": "https://ai.google.dev/gemini-api/terms", "cover_image_url": null, "metadata": { "Default thinking level": "medium", "Output per request": "65,536 tokens (up to 1,048,576 with long decodes)" }, "request_formats": [ "chat-completions" ], "providers": null, "provider_details": [ { "id": "google-vertex-ai", "pricing": { "input_tokens": 2, "input_cached_tokens": 0.1, "output_tokens": 10 } } ], "banner": null, "created_at": "2026-10-14 03:14:34", "schema": { "input": { "$schema": "https://json-schema.org/draft/2020-12/schema", "oneOf": [ { "type": "object", "properties": { "contents": { "type": "array", "items": { "type": "object", "properties": { "role": { "type": "string", "enum": [ "user", "model" ] }, "parts": { "type": "array", "items": { "type": "object", "properties": { "text": { "type": "string" } }, "additionalProperties": {} } } }, "required": [ "parts" ], "additionalProperties": {} } }, "systemInstruction": { "type": "object", "properties": { "parts": { "type": "array", "items": { "type": "object", "properties": { "text": { "type": "string" } }, "additionalProperties": {} } } }, "required": [ "parts" ], "additionalProperties": {} }, "generationConfig": { "type": "object", "properties": { "temperature": { "type": "number" }, "topP": { "type": "number" }, "topK": { "type": "number" }, "maxOutputTokens": { "type": "number" }, "candidateCount": { "type": "number" }, "stopSequences": { "type": "array", "items": { "type": "string" } }, "responseMimeType": { "type": "string" } }, "additionalProperties": {} }, "safetySettings": { "type": "array", "items": { "type": "object", "properties": { "category": { "type": "string" }, "threshold": { "type": "string" } }, "required": [ "category", "threshold" ], "additionalProperties": {} } }, "tools": { "type": "array", "items": {} }, "toolConfig": {} }, "required": [ "contents" ], "additionalProperties": {}, "title": "Generate Content" }, { "type": "object", "properties": { "messages": { "type": "array", "items": { "type": "object", "properties": { "role": { "type": "string", "enum": [ "system", "developer", "user", "assistant", "tool" ] }, "content": { "anyOf": [ { "type": "string" }, { "type": "null" }, { "type": "array", "items": {} } ] } }, "required": [ "role" ], "additionalProperties": {} } }, "temperature": { "type": "number", "minimum": 0, "maximum": 2 }, "max_tokens": { "type": "number", "exclusiveMinimum": 0 }, "max_completion_tokens": { "type": "number", "exclusiveMinimum": 0 }, "top_p": { "type": "number", "minimum": 0, "maximum": 1 }, "frequency_penalty": { "type": "number", "minimum": -2, "maximum": 2 }, "presence_penalty": { "type": "number", "minimum": -2, "maximum": 2 }, "stream": { "type": "boolean" }, "stream_options": { "type": "object", "properties": { "include_usage": { "type": "boolean" } }, "additionalProperties": false }, "tools": { "type": "array", "items": {} }, "tool_choice": {}, "response_format": {}, "modalities": { "type": "array", "items": { "type": "string", "enum": [ "text", "audio" ] } }, "audio": { "type": "object", "properties": { "voice": { "type": "string", "enum": [ "alloy", "ash", "ballad", "coral", "echo", "sage", "shimmer", "verse" ] }, "format": { "type": "string", "enum": [ "wav", "mp3", "flac", "opus", "pcm16" ] } }, "additionalProperties": false }, "reasoning_effort": { "description": "Optional reasoning control; availability and accepted values are model-dependent.", "type": [ "string", "null" ] } }, "required": [ "messages" ], "additionalProperties": {}, "title": "Chat Completions" } ] }, "output": { "$schema": "https://json-schema.org/draft/2020-12/schema", "oneOf": [ { "type": "object", "properties": { "candidates": { "type": "array", "items": {} }, "usageMetadata": { "type": "object", "properties": { "promptTokenCount": { "type": "number" }, "candidatesTokenCount": { "type": "number" }, "totalTokenCount": { "type": "number" } }, "additionalProperties": {} }, "modelVersion": { "type": "string" } }, "additionalProperties": {}, "title": "Generate Content" }, { "type": "object", "properties": { "id": { "type": "string" }, "object": { "type": "string" }, "created": { "type": "number" }, "model": { "type": "string" }, "choices": { "type": "array", "items": { "type": "object", "properties": { "index": { "type": "number" }, "message": { "type": "object", "properties": { "role": { "type": "string" }, "content": { "type": [ "string", "null" ] }, "tool_calls": { "anyOf": [ { "type": "array", "items": {} }, { "type": "null" } ] }, "audio": { "anyOf": [ { "type": "object", "properties": { "id": { "type": "string" }, "data": { "type": "string" }, "expires_at": { "type": "number" }, "transcript": { "type": "string" } }, "additionalProperties": {} }, { "type": "null" } ] } }, "required": [ "role" ], "additionalProperties": {} }, "finish_reason": { "type": [ "string", "null" ] } }, "required": [ "index", "message", "finish_reason" ], "additionalProperties": {} } }, "usage": { "type": "object", "properties": { "prompt_tokens": { "type": "number" }, "completion_tokens": { "type": "number" }, "total_tokens": { "type": "number" } }, "required": [ "prompt_tokens", "completion_tokens", "total_tokens" ], "additionalProperties": {} } }, "required": [ "id", "object", "created", "model", "choices" ], "additionalProperties": {}, "title": "Chat Completions" } ] } } }