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 | — |
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 | 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. | $0.95 | — |
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. | $0.6712Inceptron | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $3.00StreamLake | — |
| $293StreamLake | — | |
| 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. | — | 74.8% |
| Overview | ||
| Company | Moonshot AI | SpaceXSI |
| Release date | Jun 12 2026 | Feb 17 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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Kimi K2.7 Code and Grok 3 mini 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 API price: $0.95 per million input tokens and $4.00 per million output tokens. No pay-as-you-go API rate is tracked for Grok 3 mini. Grok 3 mini shipped 480 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Kimi K2.7 Code) vs 128k (Grok 3 mini). Kimi K2.7 Code is open weight, while Grok 3 mini is closed.
Direct benchmark comparisons are unavailable — Kimi K2.7 Code and Grok 3 mini don't publish scores on any of the same benchmarks.