GPT-6 LunavsQwen3.8-Max-0902
GPT-6 Luna | Qwen3.8-Max-0902 | |
|---|---|---|
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 2.4T |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 1.05M | 1M |
| 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. | $0.10 | — |
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. | $0.50 | — |
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. | $0.01 | — |
| Benchmarks | ||
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. | 66.6% | 69.3% |
| BenchmarksPublished by one model only | ||
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 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 | OpenAI | Qwen |
| Release date | Sep 22 2026 | Sep 2 2026 |
| Access | Proprietary | Proprietary |
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Frequently asked questions
Qwen3.8-Max-0902 leads GPT-6 Luna on 1 of the 1 benchmark they both report (DeepSWE 1.1). Only GPT-6 Luna has a verified first-party API price: $0.10 per million input tokens and $0.50 per million output tokens. No pay-as-you-go API rate is tracked for Qwen3.8-Max-0902. Qwen3.8-Max-0902 shipped 20 days before GPT-6 Luna, so benchmark comparisons should account for the intervening progress.
Context windows are 1.05M (GPT-6 Luna) vs 1M (Qwen3.8-Max-0902).
On DeepSWE 1.1, Qwen3.8-Max-0902 leads at 69.3% vs GPT-6 Luna at 66.6%.