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. | 1.8B/3.25B | 70B |
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. | 32k | 16k |
| Overview | ||
| Company | Meta | |
| Release date | Dec 6 2023 | Jan 29 2024 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 1.0 NanovsClaude Haiku 5.5Code Llama 70BvsClaude Haiku 5.5Gemini 1.0 NanovsGPT-6.1 SolCode Llama 70BvsGPT-6.1 SolGemini 1.0 NanovsGrok 4.7Code Llama 70BvsGrok 4.7Gemini 1.0 NanovsDeepSeek-V4.1-Flash-1017Code Llama 70BvsDeepSeek-V4.1-Flash-1017Gemini 1.0 NanovsMistral Large 4Code Llama 70BvsMistral Large 4Gemini 1.0 NanovsKimi K3Code Llama 70BvsKimi K3Frequently asked questions
Gemini 1.0 Nano and Code Llama 70B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 1.0 Nano shipped 54 days before Code Llama 70B, so benchmark comparisons should account for the intervening progress.
Gemini 1.0 Nano has 1.8B/3.25B parameters, while Code Llama 70B has 70B. Context windows are 32k (Gemini 1.0 Nano) vs 16k (Code Llama 70B). Gemini 1.0 Nano is closed, while Code Llama 70B is open weight.
Direct benchmark comparisons are unavailable — Gemini 1.0 Nano and Code Llama 70B don't publish scores on any of the same benchmarks.