GPT-4.1 nanovsQwen3-Coder-Next
GPT-4.1 nano | Qwen3-Coder-Next | |
|---|---|---|
| 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. | — | 80B |
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. | 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. | $0.10 | $0.30 |
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. | $0.40 | $1.50 |
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. | $0.025 | — |
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. | $0.10Azure | $0.12Parasail |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.40Azure | $0.80Parasail |
| BenchmarksPublished by one model only | ||
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. | — | 44.3% |
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. | — | 70.6% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 62.8% |
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. | — | 36.2% |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Apr 14 2025 | Feb 3 2026 |
| Access | Proprietary | Open Weight |
Other comparisons
GPT-4.1 nanovsClaude Fable 5.1Qwen3-Coder-NextvsClaude Fable 5.1GPT-4.1 nanovsGemini 3.8 FlashQwen3-Coder-NextvsGemini 3.8 FlashGPT-4.1 nanovsMuse Spark 1.3Qwen3-Coder-NextvsMuse Spark 1.3GPT-4.1 nanovsGrok 4.6Qwen3-Coder-NextvsGrok 4.6GPT-4.1 nanovsDeepSeek-V4.1-FlashQwen3-Coder-NextvsDeepSeek-V4.1-FlashGPT-4.1 nanovsMistral Medium 3.5Qwen3-Coder-NextvsMistral Medium 3.5
Frequently asked questions
GPT-4.1 nano and Qwen3-Coder-Next don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-4.1 nano is cheaper on both input and output: $0.10 vs $0.30 per million input tokens, and $0.40 vs $1.50 per million output tokens. Figures are base-tier rates. GPT-4.1 nano shipped 295 days before Qwen3-Coder-Next, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (GPT-4.1 nano) vs 256k (Qwen3-Coder-Next). GPT-4.1 nano is proprietary, while Qwen3-Coder-Next is open weight.
Direct benchmark comparisons are unavailable — GPT-4.1 nano and Qwen3-Coder-Next don't publish scores on any of the same benchmarks.