Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 12B | — |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 128k | 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. | — | $0.15 |
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. | — | $0.60 |
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. | — | $0.075 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.018DekaLLM | $0.15Azure |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.027Io Net | $0.60Azure |
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. | — | 2% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 40.2% |
| Overview | ||
| Company | Mistral | OpenAI |
| Release date | Jul 18 2024 | Jul 18 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
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Mistral NeMo and GPT-4o mini don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-4o mini has a verified first-party API price: $0.15 per million input tokens and $0.60 per million output tokens. No pay-as-you-go API rate is tracked for Mistral NeMo. Both models were released on the same day — Jul 18 2024.
Context windows are 128k (Mistral NeMo) vs 128k (GPT-4o mini). Mistral NeMo is open weight, while GPT-4o mini is closed.
Direct benchmark comparisons are unavailable — Mistral NeMo and GPT-4o mini don't publish scores on any of the same benchmarks.