Compare AI models
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $2.50 | $1.00 |
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 | $2.00 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.20 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. 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 third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $4.425Alibaba | — |
| Benchmarks | ||
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% | 70.8% |
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. | 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% | — |
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 | Qwen | SpaceXAI |
| Release date | May 20 2026 | Aug 28 2025 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Qwen3.7-MaxvsClaude Sonnet 5.5Grok Code Fast 1vsClaude Sonnet 5.5Qwen3.7-MaxvsGPT-6.1 SolGrok Code Fast 1vsGPT-6.1 SolQwen3.7-MaxvsGemini 4 ArgonGrok Code Fast 1vsGemini 4 ArgonQwen3.7-MaxvsMuse Spark 1.3Grok Code Fast 1vsMuse Spark 1.3Qwen3.7-MaxvsDeepSeek-V4.1-FlashGrok Code Fast 1vsDeepSeek-V4.1-FlashQwen3.7-MaxvsMistral Large 4Grok Code Fast 1vsMistral Large 4Frequently asked questions
Qwen3.7-Max leads Grok Code Fast 1 on 1 of the 1 benchmark they both report (SWE-Bench Verified). Grok Code Fast 1 is cheaper on both input and output: $1.00 vs $2.50 per million input tokens, and $2.00 vs $7.50 per million output tokens. Figures are base-tier rates. Grok Code Fast 1 shipped 265 days before Qwen3.7-Max, so benchmark comparisons should account for the intervening progress.
Published specifications for these two models are limited — see each model page for the latest details.
On SWE-Bench Verified, Qwen3.7-Max leads at 80.4% vs Grok Code Fast 1 at 70.8%.