Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 2.4T | — |
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. | 1M | 256k |
| 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. | — | $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. | — | $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 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. | $2.00Alibaba | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $6.00Alibaba | — |
| $700Alibaba | $300SpaceXSI list price | |
| These models have no shared benchmark scores. | ||
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. | — | 70.8% |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 69.3% | — |
NL2Repo-BenchRepo-level code generation — Tests whether the AI can turn a natural-language requirement into working code across an entire repository, not just produce a single function or patch. Higher is better. | 64.9% | — |
QwenSWEBench V2Software engineering — Qwen's in-house coding benchmark, second version, built around complex real-world software-engineering tasks. Scores are not comparable with the first version. Higher is better. | 70% | — |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | 26.97% | — |
Terminal-Bench 3.0Agentic terminal coding — command-line task completion (v3.0, much harder task set) | 29% | — |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | 64% | — |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | 76.1% | — |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | 73.3% | — |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | 50.8% | — |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 82.7% | — |
| Overview | ||
| Company | Qwen | SpaceXSI |
| Release date | Sep 2 2026 | Aug 28 2025 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Qwen3.8-Max-0902vsClaude Haiku 5.5Grok Code Fast 1vsClaude Haiku 5.5Qwen3.8-Max-0902vsGPT-6.1 SolGrok Code Fast 1vsGPT-6.1 SolQwen3.8-Max-0902vsGemini 4 ArgonGrok Code Fast 1vsGemini 4 ArgonQwen3.8-Max-0902vsMuse Spark OpenGrok Code Fast 1vsMuse Spark OpenQwen3.8-Max-0902vsDeepSeek-V4.1-Flash-1017Grok Code Fast 1vsDeepSeek-V4.1-Flash-1017Qwen3.8-Max-0902vsMistral Large 4Grok Code Fast 1vsMistral Large 4Frequently asked questions
Qwen3.8-Max-0902 and Grok Code Fast 1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Grok Code Fast 1 has a verified first-party API price: $1.00 per million input tokens and $2.00 per million output tokens. No pay-as-you-go API rate is tracked for Qwen3.8-Max-0902. Grok Code Fast 1 shipped 370 days before Qwen3.8-Max-0902, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Qwen3.8-Max-0902) vs 256k (Grok Code Fast 1).
Direct benchmark comparisons are unavailable — Qwen3.8-Max-0902 and Grok Code Fast 1 don't publish scores on any of the same benchmarks.