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. | 397B | 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 | 32k |
| 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. | $0.40 | — |
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. | $1.60 | — |
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. | $0.32Alibaba | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.28Alibaba | — |
| $128Alibaba | — | |
| Benchmarks | ||
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. | 89.6% | 50% |
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. | 77.7% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 34.7% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 73.3% | — |
| Overview | ||
| Company | Qwen | Qwen |
| Release date | Jun 1 2026 | Nov 28 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Qwen3.7-PlusvsClaude Haiku 5.5QwQ-32B-PreviewvsClaude Haiku 5.5Qwen3.7-PlusvsGPT-6.1 SolQwQ-32B-PreviewvsGPT-6.1 SolQwen3.7-PlusvsGemini 4 ArgonQwQ-32B-PreviewvsGemini 4 ArgonQwen3.7-PlusvsMuse Spark OpenQwQ-32B-PreviewvsMuse Spark OpenQwen3.7-PlusvsGrok 4.7QwQ-32B-PreviewvsGrok 4.7Qwen3.7-PlusvsDeepSeek-V4.1-Flash-1017QwQ-32B-PreviewvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Qwen3.7-Plus leads QwQ-32B-Preview on 1 of the 1 benchmark they both report (LiveCodeBench). Only Qwen3.7-Plus has a verified first-party API price: $0.40 per million input tokens and $1.60 per million output tokens. No pay-as-you-go API rate is tracked for QwQ-32B-Preview. QwQ-32B-Preview shipped 550 days before Qwen3.7-Plus, so benchmark comparisons should account for the intervening progress.
Qwen3.7-Plus has 397B parameters, while QwQ-32B-Preview has 32B. Context windows are 1M (Qwen3.7-Plus) vs 32k (QwQ-32B-Preview). Qwen3.7-Plus is closed, while QwQ-32B-Preview is open weight.
On LiveCodeBench, Qwen3.7-Plus leads at 89.6% vs QwQ-32B-Preview at 50%.