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. | 256k | 1M |
| 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. | $0.95 | — |
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. | $4.00 | — |
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. | $0.19 | — |
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. | $0.6712Inceptron | $0.08DekaLLM |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $3.00StreamLake | $0.40DeepInfra |
These models have no shared benchmark scores. | ||
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. | — | 54% |
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. | — | 60.5% |
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. | 31% | — |
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. | 74% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 79.2% |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | — | 36 |
| Overview | ||
| Company | Moonshot AI | NVIDIA |
| Release date | Jun 12 2026 | Mar 11 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
Kimi K2.7 CodevsClaude Haiku 5.5Nemotron 3 SupervsClaude Haiku 5.5Kimi K2.7 CodevsGPT-6.1 SolNemotron 3 SupervsGPT-6.1 SolKimi K2.7 CodevsGemini 4 ArgonNemotron 3 SupervsGemini 4 ArgonKimi K2.7 CodevsMuse Spark 1.3Nemotron 3 SupervsMuse Spark 1.3Kimi K2.7 CodevsGrok 4.7Nemotron 3 SupervsGrok 4.7Kimi K2.7 CodevsDeepSeek-V4.1-FlashNemotron 3 SupervsDeepSeek-V4.1-FlashFrequently asked questions
Kimi K2.7 Code and Nemotron 3 Super don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Kimi K2.7 Code has a verified first-party API price: $0.95 per million input tokens and $4.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Super. Nemotron 3 Super shipped 93 days before Kimi K2.7 Code, so benchmark comparisons should account for the intervening progress.
Kimi K2.7 Code has 1T parameters, while Nemotron 3 Super has 120B. Context windows are 256k (Kimi K2.7 Code) vs 1M (Nemotron 3 Super). Kimi K2.7 Code is open weight, while Nemotron 3 Super is open source.
Direct benchmark comparisons are unavailable — Kimi K2.7 Code and Nemotron 3 Super don't publish scores on any of the same benchmarks.