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. | — | 816B |
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. | — | 1M |
| 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. | 49% | — |
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. | 62.3% | — |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | — | 60% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | — | 76.4% |
MCP AtlasMulti-step tool use — Can the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | — | 89.2% |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | — | 61.8% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 68% | — |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | — | 1401 |
| Overview | ||
| Company | Anthropic | Meta |
| Release date | Feb 24 2025 | Oct 15 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude 3.7 SonnetvsGPT-6.1 SolMuse Spark OpenvsGPT-6.1 SolClaude 3.7 SonnetvsGemini 4 ArgonMuse Spark OpenvsGemini 4 ArgonClaude 3.7 SonnetvsGrok 4.7Muse Spark OpenvsGrok 4.7Claude 3.7 SonnetvsDeepSeek-V4.1-FlashMuse Spark OpenvsDeepSeek-V4.1-FlashClaude 3.7 SonnetvsMistral Large 4Muse Spark OpenvsMistral Large 4Claude 3.7 SonnetvsKimi K3Muse Spark OpenvsKimi K3Frequently asked questions
Claude 3.7 Sonnet and Muse Spark Open don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Claude 3.7 Sonnet shipped 598 days before Muse Spark Open, so benchmark comparisons should account for the intervening progress.
Claude 3.7 Sonnet is closed, while Muse Spark Open is open weight.
Direct benchmark comparisons are unavailable — Claude 3.7 Sonnet and Muse Spark Open don't publish scores on any of the same benchmarks.