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 | 120B |
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 | 1M |
| Benchmarks | ||
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% | 66.7% |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 51.78% |
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. | — | 71.1% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 67.72% |
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% | — |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 88.22% |
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% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 78% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 89.52% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 70.86% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 1401 | — |
| Overview | ||
| Company | Meta | NVIDIA |
| Release date | Oct 15 2026 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Muse Spark OpenvsClaude Haiku 5.5Nemotron 3.5 Super VLvsClaude Haiku 5.5Muse Spark OpenvsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolMuse Spark OpenvsGemini 4 ArgonNemotron 3.5 Super VLvsGemini 4 ArgonMuse Spark OpenvsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7Muse Spark OpenvsDeepSeek-V4.1-FlashNemotron 3.5 Super VLvsDeepSeek-V4.1-FlashMuse Spark OpenvsMistral Large 4Nemotron 3.5 Super VLvsMistral Large 4Frequently asked questions
Muse Spark Open leads Nemotron 3.5 Super VL on 1 of the 1 benchmark they both report (Terminal-Bench 2.1). Both models were released on the same day — Oct 15 2026.
Muse Spark Open has 816B parameters, while Nemotron 3.5 Super VL has 120B. Context windows are 1M (Muse Spark Open) vs 1M (Nemotron 3.5 Super VL).
On Terminal-Bench 2.1, Muse Spark Open leads at 76.4% vs Nemotron 3.5 Super VL at 66.7%.