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. | — | 32B |
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. | 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. | $2.00 | — |
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. | $8.00 | — |
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. | $0.50 | — |
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. | $2.00Azure | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $8.00Azure | — |
| $800OpenAI list price | — | |
| These models have no shared benchmark scores. | ||
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. | 14% | — |
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. | 54.6% | — |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 63.4% |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Apr 14 2025 | Mar 6 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-4.1vsClaude Haiku 5.5QwQ-32BvsClaude Haiku 5.5GPT-4.1vsGemini 4 ArgonQwQ-32BvsGemini 4 ArgonGPT-4.1vsMuse Spark OpenQwQ-32BvsMuse Spark OpenGPT-4.1vsGrok 4.7QwQ-32BvsGrok 4.7GPT-4.1vsDeepSeek-V4.1-Flash-1017QwQ-32BvsDeepSeek-V4.1-Flash-1017GPT-4.1vsMistral Large 4QwQ-32BvsMistral Large 4Frequently asked questions
GPT-4.1 and QwQ-32B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-4.1 has a verified first-party API price: $2.00 per million input tokens and $8.00 per million output tokens. No pay-as-you-go API rate is tracked for QwQ-32B. QwQ-32B shipped 39 days before GPT-4.1, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (GPT-4.1) vs 128k (QwQ-32B). GPT-4.1 is closed, while QwQ-32B is open weight.
Direct benchmark comparisons are unavailable — GPT-4.1 and QwQ-32B don't publish scores on any of the same benchmarks.