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. | 816B | 4B |
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. | 1M | 256k |
| API pricingUSD per 1M tokens · lower wins | ||
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. | $1.25BaseTen | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $4.25BaseTen | — |
| $463Nebius · fp4 | — | |
| 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% | 18.1% |
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. | — | 59.5% |
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% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 93% | — |
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. | — | 72.41% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 77.94% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 1401 | — |
ZeroBench (tools, pass@5)Visual reasoning — percentage of 100 visual problems solved at least once in five attempts with tools | 50% | — |
SimpleVQA (with tools)Visual reasoning — accuracy on 2,024 English and Chinese visual questions with tools | 73.6% | — |
BabyVision (with tools)Visual reasoning — mean accuracy on 388 visual reasoning questions with tools | 80.2% | — |
HLE-Diamond (no tools)Reasoning — accuracy on the 1,000-question HLE-Diamond reasoning and knowledge subset without tools | 26.1% | — |
| Overview | ||
| Company | Meta | NVIDIA |
| Release date | Oct 15 2026 | Oct 16 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Muse Spark OpenvsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2Muse Spark OpenvsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGPT-6.1 TerraMuse Spark OpenvsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsGemini 4 CarbonMuse Spark OpenvsGrok 4.7Nemotron Labs Dory 1.0 4BvsGrok 4.7Muse Spark OpenvsDeepSeek-V4.1-Flash-1017Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017Muse Spark OpenvsMistral Large 4Nemotron Labs Dory 1.0 4BvsMistral Large 4Frequently asked questions
Muse Spark Open leads Nemotron Labs Dory 1.0 4B on 1 of the 1 benchmark they both report (Terminal-Bench 2.1). Muse Spark Open shipped 1 day before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Muse Spark Open has 816B parameters, while Nemotron Labs Dory 1.0 4B has 4B. Context windows are 1M (Muse Spark Open) vs 256k (Nemotron Labs Dory 1.0 4B).
On Terminal-Bench 2.1, Muse Spark Open leads at 76.4% vs Nemotron Labs Dory 1.0 4B at 18.1%.