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. | $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. | $2.50 | — |
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. | $0.03 | — |
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. | $0.15Google AI Studio | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.25Google AI Studio | — |
| $185Google 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. | 19% | — |
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 | Qwen | |
| Release date | Apr 17 2025 | Mar 6 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 2.5 FlashvsClaude Haiku 5.5QwQ-32BvsClaude Haiku 5.5Gemini 2.5 FlashvsGPT-6.1 SolQwQ-32BvsGPT-6.1 SolGemini 2.5 FlashvsMuse Spark OpenQwQ-32BvsMuse Spark OpenGemini 2.5 FlashvsGrok 4.7QwQ-32BvsGrok 4.7Gemini 2.5 FlashvsDeepSeek-V4.1-Flash-1017QwQ-32BvsDeepSeek-V4.1-Flash-1017Gemini 2.5 FlashvsMistral Large 4QwQ-32BvsMistral Large 4Frequently asked questions
Gemini 2.5 Flash and QwQ-32B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Gemini 2.5 Flash has a verified first-party API price: $0.30 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for QwQ-32B. QwQ-32B shipped 42 days before Gemini 2.5 Flash, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 2.5 Flash) vs 128k (QwQ-32B). Gemini 2.5 Flash is closed, while QwQ-32B is open weight.
Direct benchmark comparisons are unavailable — Gemini 2.5 Flash and QwQ-32B don't publish scores on any of the same benchmarks.