Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 80B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 200k | 256k |
| 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. | $15.00 | — |
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. | $60.00 | — |
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. | $7.50 | — |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.12Parasail |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.80Parasail |
| $6,000OpenAI list price | $64.00Parasail · bf16 | |
| These models have no shared benchmark scores. | ||
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% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 75.7% | — |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Dec 5 2024 | Feb 3 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
o1vsClaude Haiku 5.5Qwen3-Coder-NextvsClaude Haiku 5.5o1vsGemini 4 CarbonQwen3-Coder-NextvsGemini 4 Carbono1vsMuse Spark OpenQwen3-Coder-NextvsMuse Spark Openo1vsGrok 4.7Qwen3-Coder-NextvsGrok 4.7o1vsDeepSeek-V4.1-Flash-1017Qwen3-Coder-NextvsDeepSeek-V4.1-Flash-1017o1vsMistral Large 4Qwen3-Coder-NextvsMistral Large 4Frequently asked questions
o1 and Qwen3-Coder-Next don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only o1 has a verified first-party price for an available API: $15.00 per million input tokens and $60.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Qwen3-Coder-Next. o1 shipped 425 days before Qwen3-Coder-Next, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (o1) vs 256k (Qwen3-Coder-Next). o1 is closed, while Qwen3-Coder-Next is open weight.
Direct benchmark comparisons are unavailable — o1 and Qwen3-Coder-Next don't publish scores on any of the same benchmarks.