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. | $1.00 | $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. | $5.00 | $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. | $0.10 | — |
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. | $1.00Amazon 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. | $5.00Amazon 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. | 77% | 2% |
ProgramBenchProgram reconstruction — The AI receives a working program and its documentation, then builds a replacement from scratch without the original source code, internet access or decompilation. The score is the percentage of 200 programs that pass every behavioral test. We record each model's best published mini-SWE-agent result, including higher reasoning efforts where available. Partial test-pass rates and almost-solved programs do not count toward this score. Equal scores share a rank here; the official board also uses partial progress to break ties. Higher is better. | 0% | — |
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. | 73.3% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 73% | — |
OSWorld 2.1 (offline)Agentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Version 2.1, in its offline setting. Scores are not comparable with version 2.0. Higher is better. | 15.7% | — |
| Overview | ||
| Company | Anthropic | Mistral |
| Release date | Oct 15 2025 | Dec 2 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Haiku 4.5vsGPT-6.1 SolMistral Large 3vsGPT-6.1 SolClaude Haiku 4.5vsGemini 4 ArgonMistral Large 3vsGemini 4 ArgonClaude Haiku 4.5vsMuse Spark 1.3Mistral Large 3vsMuse Spark 1.3Claude Haiku 4.5vsGrok 4.7Mistral Large 3vsGrok 4.7Claude Haiku 4.5vsDeepSeek-V4.1-FlashMistral Large 3vsDeepSeek-V4.1-FlashClaude Haiku 4.5vsKimi K3Mistral Large 3vsKimi K3Frequently asked questions
Claude Haiku 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 $1.00 per million input tokens, and $1.50 vs $5.00 per million output tokens. Claude Haiku 4.5 shipped 48 days before Mistral Large 3, so benchmark comparisons should account for the intervening progress.
Claude Haiku 4.5 is closed, while Mistral Large 3 is open weight.
On BullshitBench v2, Claude Haiku 4.5 leads at 77% vs Mistral Large 3 at 2%.