o1-minivsQwen3-Coder
o1-mini | 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. | 128k | 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. | $1.10 | $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. | $4.40 | $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. | $0.55 | — |
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. | — | $1.55Novita |
| 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% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 60% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Sep 12 2024 | Jul 22 2025 |
| Access | Proprietary | Open Weight |
Other comparisons
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Frequently asked questions
o1-mini and Qwen3-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen3-Coder is cheaper on input: $1.00 vs $1.10 per million tokens. o1-mini is cheaper on output: $4.40 vs $5.00 per million tokens. Figures are base-tier rates. o1-mini shipped 313 days before Qwen3-Coder, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (o1-mini) vs 256k (Qwen3-Coder). o1-mini is proprietary, while Qwen3-Coder is open weight.
Direct benchmark comparisons are unavailable — o1-mini and Qwen3-Coder don't publish scores on any of the same benchmarks.