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. | 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. | 16k | 256k |
| API pricingUSD per 1M tokens · lower wins | ||
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. | — | $0.60Google |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.50Google |
| — | $245Novita · bf16 | |
| 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. | — | 71.3% |
| Overview | ||
| Company | Meta | Moonshot AI |
| Release date | Jan 29 2024 | Nov 6 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Code Llama 70BvsClaude Haiku 5.5Kimi K2 ThinkingvsClaude Haiku 5.5Code Llama 70BvsGPT-6.1 SolKimi K2 ThinkingvsGPT-6.1 SolCode Llama 70BvsGemini 4 ArgonKimi K2 ThinkingvsGemini 4 ArgonCode Llama 70BvsGrok 4.7Kimi K2 ThinkingvsGrok 4.7Code Llama 70BvsDeepSeek-V4.1-Flash-1017Kimi K2 ThinkingvsDeepSeek-V4.1-Flash-1017Code Llama 70BvsMistral Large 4Kimi K2 ThinkingvsMistral Large 4Frequently asked questions
Code Llama 70B and Kimi K2 Thinking don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama 70B shipped 647 days before Kimi K2 Thinking, so benchmark comparisons should account for the intervening progress.
Code Llama 70B has 70B parameters, while Kimi K2 Thinking has 1T. Context windows are 16k (Code Llama 70B) vs 256k (Kimi K2 Thinking).
Direct benchmark comparisons are unavailable — Code Llama 70B and Kimi K2 Thinking don't publish scores on any of the same benchmarks.