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. | 1T | 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. | 256k | 32k |
| 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. | $0.95 | — |
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. | $4.00 | — |
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. | $0.19 | — |
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. | $0.60CoreWeave | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $3.00CoreWeave | — |
| $270CoreWeave · int4 | — | |
| These models have no shared benchmark scores. | ||
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 31% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 74% | — |
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. | — | 50% |
| Overview | ||
| Company | Moonshot AI | Qwen |
| Release date | Jun 12 2026 | Nov 28 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Kimi K2.7 CodevsClaude Haiku 5.5QwQ-32B-PreviewvsClaude Haiku 5.5Kimi K2.7 CodevsGPT-6.1 TerraQwQ-32B-PreviewvsGPT-6.1 TerraKimi K2.7 CodevsGemini 4 CarbonQwQ-32B-PreviewvsGemini 4 CarbonKimi K2.7 CodevsMuse Spark OpenQwQ-32B-PreviewvsMuse Spark OpenKimi K2.7 CodevsGrok 4.7QwQ-32B-PreviewvsGrok 4.7Kimi K2.7 CodevsDeepSeek-V4.1-Flash-1017QwQ-32B-PreviewvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Kimi K2.7 Code and QwQ-32B-Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Kimi K2.7 Code has a verified first-party price for an available API: $0.95 per million input tokens and $4.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for QwQ-32B-Preview. QwQ-32B-Preview shipped 561 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
Kimi K2.7 Code has 1T parameters, while QwQ-32B-Preview has 32B. Context windows are 256k (Kimi K2.7 Code) vs 32k (QwQ-32B-Preview).
Direct benchmark comparisons are unavailable — Kimi K2.7 Code and QwQ-32B-Preview don't publish scores on any of the same benchmarks.