GPT-6 LunavsQwen3-Coder
GPT-6 Luna | Qwen3-Coder | |
|---|---|---|
| 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. | — | 480B |
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 | 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. | $0.10 | $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. | $0.50 | $5.00 |
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. | $0.01 | — |
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. | — | $0.22Google |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $1.00DeepInfra |
| BenchmarksPublished by one model only | ||
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. | — | 20% |
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% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Sep 22 2026 | Jul 22 2025 |
| Access | Proprietary | Open Weight |
Other comparisons
GPT-6 LunavsClaude Opus 5.5Qwen3-CodervsClaude Opus 5.5GPT-6 LunavsGemini 3.8 FlashQwen3-CodervsGemini 3.8 FlashGPT-6 LunavsMuse Spark 1.3Qwen3-CodervsMuse Spark 1.3GPT-6 LunavsGrok 4.7Qwen3-CodervsGrok 4.7GPT-6 LunavsDeepSeek-V4.1-FlashQwen3-CodervsDeepSeek-V4.1-FlashGPT-6 LunavsMistral Medium 3.5Qwen3-CodervsMistral Medium 3.5
Frequently asked questions
GPT-6 Luna and Qwen3-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-6 Luna is cheaper on both input and output: $0.10 vs $1.00 per million input tokens, and $0.50 vs $5.00 per million output tokens. Figures are base-tier rates. Qwen3-Coder shipped 427 days before GPT-6 Luna, so benchmark comparisons should account for the intervening progress.
Context windows are 1.05M (GPT-6 Luna) vs 256k (Qwen3-Coder). GPT-6 Luna is proprietary, while Qwen3-Coder is open weight.
Direct benchmark comparisons are unavailable — GPT-6 Luna and Qwen3-Coder don't publish scores on any of the same benchmarks.