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. | 100k | 16k |
| 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. | 58.7% | — |
GSM8KGrade-school math — Grade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | 86.7% | — |
| Overview | ||
| Company | Anthropic | Meta |
| Release date | Aug 9 2023 | Aug 24 2023 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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Claude Instant 1.2 and Code Llama don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Claude Instant 1.2 shipped 15 days before Code Llama, so benchmark comparisons should account for the intervening progress.
Context windows are 100k (Claude Instant 1.2) vs 16k (Code Llama). Claude Instant 1.2 is closed, while Code Llama is open weight.
Direct benchmark comparisons are unavailable — Claude Instant 1.2 and Code Llama don't publish scores on any of the same benchmarks.