GPT-4.1 nanovsQwen2.5-Coder
GPT-4.1 nano | Qwen2.5-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. | — | 32B |
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 | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
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. | $0.40 | — |
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 | $0.66Cloudflare |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.40Azure | $1.00Cloudflare |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Apr 14 2025 | Nov 12 2024 |
| Access | Proprietary | Open Weight |
Other comparisons
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Frequently asked questions
GPT-4.1 nano and Qwen2.5-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-4.1 nano has a verified first-party API price: $0.10 per million input tokens and $0.40 per million output tokens. No pay-as-you-go API rate is tracked for Qwen2.5-Coder. Qwen2.5-Coder shipped 153 days before GPT-4.1 nano, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (GPT-4.1 nano) vs 128k (Qwen2.5-Coder). GPT-4.1 nano is proprietary, while Qwen2.5-Coder is open weight.
Direct benchmark comparisons are unavailable — GPT-4.1 nano and Qwen2.5-Coder don't publish scores on any of the same benchmarks.