Compare AI models
| 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. | $5.00 | $0.50 |
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. | $25.00 | $1.50 |
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. | $0.50 | — |
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. | $5.00Amazon Bedrock | $0.50Mistral |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $25.00Amazon Bedrock | $1.50Mistral |
| Benchmarks | ||
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. | 90% | 2% |
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. | 80.9% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 30.8% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 87% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 66.3% | — |
threejsevalCommunity preference (Three.js) — Every model gets the same prompt — "the Eiffel Tower", "a glass fishbowl", "a robot arm picking toys into a box" — and builds a 3D scene in Three.js. Real people then see two scenes side by side, names hidden, and vote for the one they prefer. The votes become a chess-style Elo rating on threejseval.com, averaged across all the prompts. It measures whether the scene looks and moves right to a human eye, not whether the code passes a test. Higher is better. | 1147 | — |
| Overview | ||
| Company | Anthropic | Mistral |
| Release date | Nov 24 2025 | Dec 2 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Opus 4.5vsGPT-6.1 SolMistral Large 3vsGPT-6.1 SolClaude Opus 4.5vsGemini 4 ArgonMistral Large 3vsGemini 4 ArgonClaude Opus 4.5vsMuse Spark 1.3Mistral Large 3vsMuse Spark 1.3Claude Opus 4.5vsGrok 4.7Mistral Large 3vsGrok 4.7Claude Opus 4.5vsDeepSeek-V4.1-FlashMistral Large 3vsDeepSeek-V4.1-FlashClaude Opus 4.5vsKimi K3Mistral Large 3vsKimi K3Frequently asked questions
Claude Opus 4.5 leads Mistral Large 3 on 1 of the 1 benchmark they both report (BullshitBench v2). Mistral Large 3 is cheaper on both input and output: $0.50 vs $5.00 per million input tokens, and $1.50 vs $25.00 per million output tokens. Claude Opus 4.5 shipped 8 days before Mistral Large 3, so benchmark comparisons should account for the intervening progress.
Claude Opus 4.5 is closed, while Mistral Large 3 is open weight.
On BullshitBench v2, Claude Opus 4.5 leads at 90% vs Mistral Large 3 at 2%.