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. | — | 480B |
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. | 2M | 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. | — | $1.00 |
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. | — | $5.00 |
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. | — | $0.22Google |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.00DeepInfra |
| — | $110DeepInfra · turbo · fp4 | |
| 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. | — | 20% |
| Overview | ||
| Company | Qwen | |
| Release date | Sep 24 2024 | Jul 22 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 1.5 Pro-002vsClaude Haiku 5.5Qwen3-CodervsClaude Haiku 5.5Gemini 1.5 Pro-002vsGPT-6.1 SolQwen3-CodervsGPT-6.1 SolGemini 1.5 Pro-002vsMuse Spark OpenQwen3-CodervsMuse Spark OpenGemini 1.5 Pro-002vsGrok 4.7Qwen3-CodervsGrok 4.7Gemini 1.5 Pro-002vsDeepSeek-V4.1-Flash-1017Qwen3-CodervsDeepSeek-V4.1-Flash-1017Gemini 1.5 Pro-002vsMistral Large 4Qwen3-CodervsMistral Large 4Frequently asked questions
Gemini 1.5 Pro-002 and Qwen3-Coder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3-Coder has a verified first-party API price: $1.00 per million input tokens and $5.00 per million output tokens. No pay-as-you-go API rate is tracked for Gemini 1.5 Pro-002. Gemini 1.5 Pro-002 shipped 301 days before Qwen3-Coder, so benchmark comparisons should account for the intervening progress.
Context windows are 2M (Gemini 1.5 Pro-002) vs 256k (Qwen3-Coder). Gemini 1.5 Pro-002 is closed, while Qwen3-Coder is open weight.
Direct benchmark comparisons are unavailable — Gemini 1.5 Pro-002 and Qwen3-Coder don't publish scores on any of the same benchmarks.