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. | — | 8B |
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 | 256k |
| 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. | — | $0.15 |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.15Mistral |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.15Mistral |
| — | $37.50Mistral list price | |
| 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. | 46% | — |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 33.4% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 59.4% | — |
| Overview | ||
| Company | Anthropic | Mistral |
| Release date | Jun 20 2024 | Dec 2 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude 3.5 SonnetvsGPT-6.1 SolMinistral 3 8B-25-12vsGPT-6.1 SolClaude 3.5 SonnetvsGemini 4 ArgonMinistral 3 8B-25-12vsGemini 4 ArgonClaude 3.5 SonnetvsMuse Spark OpenMinistral 3 8B-25-12vsMuse Spark OpenClaude 3.5 SonnetvsGrok 4.7Ministral 3 8B-25-12vsGrok 4.7Claude 3.5 SonnetvsDeepSeek-V4.1-Flash-1017Ministral 3 8B-25-12vsDeepSeek-V4.1-Flash-1017Claude 3.5 SonnetvsKimi K3Ministral 3 8B-25-12vsKimi K3Frequently asked questions
Claude 3.5 Sonnet and Ministral 3 8B-25-12 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Ministral 3 8B-25-12 has a verified first-party API price: $0.15 per million input tokens and $0.15 per million output tokens. No pay-as-you-go API rate is tracked for Claude 3.5 Sonnet. Claude 3.5 Sonnet shipped 530 days before Ministral 3 8B-25-12, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3.5 Sonnet) vs 256k (Ministral 3 8B-25-12). Claude 3.5 Sonnet is closed, while Ministral 3 8B-25-12 is open weight.
Direct benchmark comparisons are unavailable — Claude 3.5 Sonnet and Ministral 3 8B-25-12 don't publish scores on any of the same benchmarks.