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 | 400k |
| 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 | $1.25 |
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 | $10.00 |
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 | $0.125 |
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 | $1.25Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $3.00StreamLake | $10.00Azure |
| $293StreamLake | $750OpenAI list price | |
| 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. | — | 21% |
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% | — |
| Overview | ||
| Company | Moonshot AI | OpenAI |
| Release date | Jun 12 2026 | Aug 7 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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Kimi K2.7 Code and GPT-5 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi K2.7 Code is cheaper on both input and output: $0.95 vs $1.25 per million input tokens, and $4.00 vs $10.00 per million output tokens. GPT-5 shipped 309 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Kimi K2.7 Code) vs 400k (GPT-5). Kimi K2.7 Code is open weight, while GPT-5 is closed.
Direct benchmark comparisons are unavailable — Kimi K2.7 Code and GPT-5 don't publish scores on any of the same benchmarks.