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 |
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. | 1M | 16k |
| Overview | ||
| Company | Meta | |
| Release date | Feb 5 2025 | Aug 24 2023 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 2.0 Flash-LitevsClaude Haiku 5.5Code LlamavsClaude Haiku 5.5Gemini 2.0 Flash-LitevsGPT-6.1 SolCode LlamavsGPT-6.1 SolGemini 2.0 Flash-LitevsGrok 4.7Code LlamavsGrok 4.7Gemini 2.0 Flash-LitevsDeepSeek-V4.1-Flash-1017Code LlamavsDeepSeek-V4.1-Flash-1017Gemini 2.0 Flash-LitevsMistral Large 4Code LlamavsMistral Large 4Gemini 2.0 Flash-LitevsKimi K3Code LlamavsKimi K3Frequently asked questions
Gemini 2.0 Flash-Lite and Code Llama don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 531 days before Gemini 2.0 Flash-Lite, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 2.0 Flash-Lite) vs 16k (Code Llama). Gemini 2.0 Flash-Lite is closed, while Code Llama is open weight.
Direct benchmark comparisons are unavailable — Gemini 2.0 Flash-Lite and Code Llama don't publish scores on any of the same benchmarks.