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/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. | 4k | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.10 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $4.40 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.55 |
| — | $440OpenAI list price | |
| These models have no shared benchmark scores. | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 60% |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Jul 18 2023 | Sep 12 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Frequently asked questions
LLaMA 2 and o1-mini don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only o1-mini has a verified first-party API price: $1.10 per million input tokens and $4.40 per million output tokens. No pay-as-you-go API rate is tracked for LLaMA 2. LLaMA 2 shipped 422 days before o1-mini, so benchmark comparisons should account for the intervening progress.
Context windows are 4k (LLaMA 2) vs 128k (o1-mini). LLaMA 2 is open weight, while o1-mini is closed.
Direct benchmark comparisons are unavailable — LLaMA 2 and o1-mini don't publish scores on any of the same benchmarks.