Compare AI models
| Specifications | ||
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 400k | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $2.50 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $7.50 |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $1.475Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $4.425Alibaba |
| — | $516Alibaba | |
| Benchmarks | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 45% | 72% |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 60.6% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 80.4% |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 46% | — |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 91.6% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 69.7% |
MCP AtlasMulti-step tool use — Can the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | — | 76.4% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 41.4% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 92.4% |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Dec 18 2025 | May 20 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
GPT-5.2-CodexvsClaude Fable 5.2Qwen3.7-MaxvsClaude Fable 5.2GPT-5.2-CodexvsGemini 4 CarbonQwen3.7-MaxvsGemini 4 CarbonGPT-5.2-CodexvsMuse EmberQwen3.7-MaxvsMuse EmberGPT-5.2-CodexvsGrok 4.7Qwen3.7-MaxvsGrok 4.7GPT-5.2-CodexvsDeepSeek-V4.1-Flash-1017Qwen3.7-MaxvsDeepSeek-V4.1-Flash-1017GPT-5.2-CodexvsMistral Large 4Qwen3.7-MaxvsMistral Large 4Frequently asked questions
Qwen3.7-Max leads GPT-5.2-Codex on 1 of the 1 benchmark they both report (BullshitBench v2). Only Qwen3.7-Max has a verified first-party price for an available API: $2.50 per million input tokens and $7.50 per million output tokens. No currently available pay-as-you-go API rate is tracked for GPT-5.2-Codex. GPT-5.2-Codex shipped 153 days before Qwen3.7-Max, so benchmark comparisons should account for the intervening progress.
Context windows are 400k (GPT-5.2-Codex) vs 1M (Qwen3.7-Max).
On BullshitBench v2, Qwen3.7-Max leads at 72% vs GPT-5.2-Codex at 45%.