Kimi K2.5vsNemotron 3 Ultra
Kimi K2.5 | Nemotron 3 Ultra | |
|---|---|---|
| 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 | 550B |
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. | 256k | — |
| 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. | $0.60 | — |
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. | $3.00 | — |
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. | $0.10 | — |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.45DeepInfra | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $2.25DeepInfra | — |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 87.6% | 86.7% |
| BenchmarksPublished by one model only | ||
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. | 52% | — |
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. | 76.8% | — |
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. | 21% | — |
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. | 85% | — |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | — | 48 |
| Overview | ||
| Company | Moonshot AI | NVIDIA |
| Release date | Jan 27 2026 | Jun 4 2026 |
| Access | Open Weight | Open Source |
Other comparisons
Frequently asked questions
Kimi K2.5 leads Nemotron 3 Ultra on 1 of the 1 benchmark they both report (GPQA Diamond). Only Kimi K2.5 has a verified first-party API price: $0.60 per million input tokens and $3.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Ultra. Kimi K2.5 shipped 128 days before Nemotron 3 Ultra, so benchmark comparisons should account for the intervening progress.
Kimi K2.5 has 1T parameters, while Nemotron 3 Ultra has 550B. Kimi K2.5 is open weight, while Nemotron 3 Ultra is open source.
On GPQA Diamond, Kimi K2.5 leads at 87.6% vs Nemotron 3 Ultra at 86.7%.
Kimi K2.5 was released by Moonshot AI on Jan 27 2026.
Nemotron 3 Ultra was released by NVIDIA on Jun 4 2026.
Kimi K2.5 leads on GPQA Diamond — Kimi K2.5 87.6% vs Nemotron 3 Ultra 86.7%.
Only Kimi K2.5 has a verified first-party API price: $0.60 per million input tokens and $3.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Ultra. Rates are pay-as-you-go API prices verified on August 18, 2026.
Kimi K2.5 is an open weight model released by Moonshot AI. Nemotron 3 Ultra is an open source model released by NVIDIA.