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. | — | 47B |
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 | 32k |
| 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 | — |
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 | — |
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 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. | $5.00Amazon Bedrock | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $25.00Amazon Bedrock | — |
| $2,250Anthropic 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. | 90% | — |
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. | 1153 | — |
| Overview | ||
| Company | Anthropic | Mistral |
| Release date | Nov 24 2025 | Dec 8 2023 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Opus 4.5vsGPT-6.1 SolMixtral 8×7BvsGPT-6.1 SolClaude Opus 4.5vsGemini 4 ArgonMixtral 8×7BvsGemini 4 ArgonClaude Opus 4.5vsMuse Spark OpenMixtral 8×7BvsMuse Spark OpenClaude Opus 4.5vsGrok 4.7Mixtral 8×7BvsGrok 4.7Claude Opus 4.5vsDeepSeek-V4.1-Flash-1017Mixtral 8×7BvsDeepSeek-V4.1-Flash-1017Claude Opus 4.5vsKimi K3Mixtral 8×7BvsKimi K3Frequently asked questions
Claude Opus 4.5 and Mixtral 8×7B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Claude Opus 4.5 has a verified first-party API price: $5.00 per million input tokens and $25.00 per million output tokens. No pay-as-you-go API rate is tracked for Mixtral 8×7B. Mixtral 8×7B shipped 717 days before Claude Opus 4.5, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude Opus 4.5) vs 32k (Mixtral 8×7B). Claude Opus 4.5 is closed, while Mixtral 8×7B is open weight.
Direct benchmark comparisons are unavailable — Claude Opus 4.5 and Mixtral 8×7B don't publish scores on any of the same benchmarks.