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. | 8B/70B | 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. | 8k | 256k |
| 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% |
| Overview | ||
| Company | Meta | Moonshot AI |
| Release date | Apr 18 2024 | Jun 12 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
LLaMA 3 (8B/70B)vsClaude Haiku 5.5Kimi K2.7 CodevsClaude Haiku 5.5LLaMA 3 (8B/70B)vsGPT-6.1 SolKimi K2.7 CodevsGPT-6.1 SolLLaMA 3 (8B/70B)vsGemini 4 ArgonKimi K2.7 CodevsGemini 4 ArgonLLaMA 3 (8B/70B)vsGrok 4.7Kimi K2.7 CodevsGrok 4.7LLaMA 3 (8B/70B)vsDeepSeek-V4.1-Flash-1017Kimi K2.7 CodevsDeepSeek-V4.1-Flash-1017LLaMA 3 (8B/70B)vsMistral Large 4Kimi K2.7 CodevsMistral Large 4Frequently asked questions
LLaMA 3 (8B/70B) and Kimi K2.7 Code 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 LLaMA 3 (8B/70B). LLaMA 3 (8B/70B) shipped 785 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
LLaMA 3 (8B/70B) has 8B/70B parameters, while Kimi K2.7 Code has 1T. Context windows are 8k (LLaMA 3 (8B/70B)) vs 256k (Kimi K2.7 Code).
Direct benchmark comparisons are unavailable — LLaMA 3 (8B/70B) and Kimi K2.7 Code don't publish scores on any of the same benchmarks.