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. | 7B/13B/34B | 72B |
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 | 128k |
| 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. | — | 60.4% |
| Overview | ||
| Company | Meta | Moonshot AI |
| Release date | Aug 24 2023 | Jun 17 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Code LlamavsClaude Haiku 5.5Kimi-DevvsClaude Haiku 5.5Code LlamavsGPT-6.1 SolKimi-DevvsGPT-6.1 SolCode LlamavsGemini 4 ArgonKimi-DevvsGemini 4 ArgonCode LlamavsGrok 4.7Kimi-DevvsGrok 4.7Code LlamavsDeepSeek-V4.1-Flash-1017Kimi-DevvsDeepSeek-V4.1-Flash-1017Code LlamavsMistral Large 4Kimi-DevvsMistral Large 4Frequently asked questions
Code Llama and Kimi-Dev don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 663 days before Kimi-Dev, so benchmark comparisons should account for the intervening progress.
Code Llama has 7B/13B/34B parameters, while Kimi-Dev has 72B. Context windows are 16k (Code Llama) vs 128k (Kimi-Dev).
Direct benchmark comparisons are unavailable — Code Llama and Kimi-Dev don't publish scores on any of the same benchmarks.