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 | — |
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 | 1M |
| These models have no shared benchmark scores. | ||
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | 53% | — |
| Overview | ||
| Company | Meta | Qwen |
| Release date | Jan 29 2024 | Nov 11 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Code Llama 70BvsClaude Fable 5.2Qwen4-Max PreviewvsClaude Fable 5.2Code Llama 70BvsGPT-6.1 TerraQwen4-Max PreviewvsGPT-6.1 TerraCode Llama 70BvsGemini 4 CarbonQwen4-Max PreviewvsGemini 4 CarbonCode Llama 70BvsGrok 4.7Qwen4-Max PreviewvsGrok 4.7Code Llama 70BvsDeepSeek-V4.1-Flash-1017Qwen4-Max PreviewvsDeepSeek-V4.1-Flash-1017Code Llama 70BvsMistral Large 4Qwen4-Max PreviewvsMistral Large 4Frequently asked questions
Code Llama 70B and Qwen4-Max Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama 70B shipped 1017 days before Qwen4-Max Preview, so benchmark comparisons should account for the intervening progress.
Context windows are 16k (Code Llama 70B) vs 1M (Qwen4-Max Preview). Code Llama 70B is open weight, while Qwen4-Max Preview is closed.
Direct benchmark comparisons are unavailable — Code Llama 70B and Qwen4-Max Preview don't publish scores on any of the same benchmarks.