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. | 48B | 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 |
| These models have no shared benchmark scores. | ||
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% |
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% |
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. | — | 66.7% |
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% |
| Overview | ||
| Company | Moonshot AI | NVIDIA |
| Release date | Oct 30 2025 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Kimi LinearvsClaude Haiku 5.5Nemotron 3.5 Super VLvsClaude Haiku 5.5Kimi LinearvsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolKimi LinearvsGemini 4 ArgonNemotron 3.5 Super VLvsGemini 4 ArgonKimi LinearvsMuse Spark OpenNemotron 3.5 Super VLvsMuse Spark OpenKimi LinearvsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7Kimi LinearvsDeepSeek-V4.1-FlashNemotron 3.5 Super VLvsDeepSeek-V4.1-FlashFrequently asked questions
Kimi Linear and Nemotron 3.5 Super VL don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi Linear shipped 350 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Kimi Linear has 48B parameters, while Nemotron 3.5 Super VL has 120B.
Direct benchmark comparisons are unavailable — Kimi Linear and Nemotron 3.5 Super VL don't publish scores on any of the same benchmarks.