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/33B/65B |
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. | 200k | 2k |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 10% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 33.3% | — |
| Overview | ||
| Company | Anthropic | Meta |
| Release date | Mar 4 2024 | Feb 24 2023 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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Claude 3 Haiku and LLaMA 1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 1 shipped 374 days before Claude 3 Haiku, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3 Haiku) vs 2k (LLaMA 1). Claude 3 Haiku is closed, while LLaMA 1 is open weight.
Direct benchmark comparisons are unavailable — Claude 3 Haiku and LLaMA 1 don't publish scores on any of the same benchmarks.