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 | 9B |
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 |
| Overview | ||
| Company | Meta | Z.ai |
| Release date | Aug 24 2023 | Jun 5 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Code LlamavsClaude Haiku 5.5GLM-4-9BvsClaude Haiku 5.5Code LlamavsGPT-6.1 SolGLM-4-9BvsGPT-6.1 SolCode LlamavsGemini 4 ArgonGLM-4-9BvsGemini 4 ArgonCode LlamavsGrok 4.7GLM-4-9BvsGrok 4.7Code LlamavsDeepSeek-V4.1-Flash-1017GLM-4-9BvsDeepSeek-V4.1-Flash-1017Code LlamavsMistral Large 4GLM-4-9BvsMistral Large 4Frequently asked questions
Code Llama and GLM-4-9B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 286 days before GLM-4-9B, so benchmark comparisons should account for the intervening progress.
Code Llama has 7B/13B/34B parameters, while GLM-4-9B has 9B. Context windows are 16k (Code Llama) vs 128k (GLM-4-9B).
Direct benchmark comparisons are unavailable — Code Llama and GLM-4-9B don't publish scores on any of the same benchmarks.