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 | 128k |
| 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. | — | $1.10 |
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. | — | $4.40 |
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. | — | $0.55 |
| — | $440OpenAI list price | |
| These models have no shared benchmark scores. | ||
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. | — | 60% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 1401 | — |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Oct 15 2026 | Sep 12 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Muse Spark OpenvsClaude Haiku 5.5o1-minivsClaude Haiku 5.5Muse Spark OpenvsGemini 4 Argono1-minivsGemini 4 ArgonMuse Spark OpenvsGrok 4.7o1-minivsGrok 4.7Muse Spark OpenvsDeepSeek-V4.1-Flasho1-minivsDeepSeek-V4.1-FlashMuse Spark OpenvsMistral Large 4o1-minivsMistral Large 4Muse Spark OpenvsKimi K3o1-minivsKimi K3Frequently asked questions
Muse Spark Open and o1-mini don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only o1-mini has a verified first-party API price: $1.10 per million input tokens and $4.40 per million output tokens. No pay-as-you-go API rate is tracked for Muse Spark Open. o1-mini shipped 763 days before Muse Spark Open, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Muse Spark Open) vs 128k (o1-mini). Muse Spark Open is open weight, while o1-mini is closed.
Direct benchmark comparisons are unavailable — Muse Spark Open and o1-mini don't publish scores on any of the same benchmarks.