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 | 397B |
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 | 1M |
| 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. | $0.95 | $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. | $4.00 | $1.60 |
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. | $0.6712Inceptron | $0.32Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $3.00StreamLake | $1.28Alibaba |
| $293StreamLake | $128Alibaba | |
| These models have no shared benchmark scores. | ||
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% |
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. | — | 89.6% |
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 | Moonshot AI | Qwen |
| Release date | Jun 12 2026 | Jun 1 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Kimi K2.7 CodevsClaude Haiku 5.5Qwen3.7-PlusvsClaude Haiku 5.5Kimi K2.7 CodevsGPT-6.1 SolQwen3.7-PlusvsGPT-6.1 SolKimi K2.7 CodevsGemini 4 ArgonQwen3.7-PlusvsGemini 4 ArgonKimi K2.7 CodevsMuse Spark OpenQwen3.7-PlusvsMuse Spark OpenKimi K2.7 CodevsGrok 4.7Qwen3.7-PlusvsGrok 4.7Kimi K2.7 CodevsDeepSeek-V4.1-Flash-1017Qwen3.7-PlusvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Kimi K2.7 Code and Qwen3.7-Plus don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen3.7-Plus is cheaper on both input and output: $0.40 vs $0.95 per million input tokens, and $1.60 vs $4.00 per million output tokens. Figures are base-tier rates. Qwen3.7-Plus shipped 11 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
Kimi K2.7 Code has 1T parameters, while Qwen3.7-Plus has 397B. Context windows are 256k (Kimi K2.7 Code) vs 1M (Qwen3.7-Plus). Kimi K2.7 Code is open weight, while Qwen3.7-Plus is closed.
Direct benchmark comparisons are unavailable — Kimi K2.7 Code and Qwen3.7-Plus don't publish scores on any of the same benchmarks.