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. | 1T | 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. | 128k | 1M |
| 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. | $0.57Novita | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $2.30Novita | — |
| $229Novita · fp8 | — | |
| Benchmarks | ||
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. | 65.8% | 71.1% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 75.1% | 89.52% |
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. | 10% | — |
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 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% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 4.7% | — |
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 | Jul 11 2025 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Kimi K2vsClaude Haiku 5.5Nemotron 3.5 Super VLvsClaude Haiku 5.5Kimi K2vsGPT-6.1 SolNemotron 3.5 Super VLvsGPT-6.1 SolKimi K2vsGemini 4 ArgonNemotron 3.5 Super VLvsGemini 4 ArgonKimi K2vsMuse Spark OpenNemotron 3.5 Super VLvsMuse Spark OpenKimi K2vsGrok 4.7Nemotron 3.5 Super VLvsGrok 4.7Kimi K2vsDeepSeek-V4.1-FlashNemotron 3.5 Super VLvsDeepSeek-V4.1-FlashFrequently asked questions
Nemotron 3.5 Super VL leads Kimi K2 on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). Kimi K2 shipped 461 days before Nemotron 3.5 Super VL, so benchmark comparisons should account for the intervening progress.
Kimi K2 has 1T parameters, while Nemotron 3.5 Super VL has 120B. Context windows are 128k (Kimi K2) vs 1M (Nemotron 3.5 Super VL).
On SWE-Bench Verified, Nemotron 3.5 Super VL leads at 71.1% vs Kimi K2 at 65.8%. On GPQA Diamond, Nemotron 3.5 Super VL leads at 89.52% vs Kimi K2 at 75.1%.