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 | 30B |
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 | $0.045Wafer |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $3.00StreamLake | $0.13Wafer |
These models have no shared benchmark scores. | ||
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. | — | 51.6% |
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. | — | 75.4% |
| Overview | ||
| Company | Moonshot AI | NVIDIA |
| Release date | Jun 12 2026 | Aug 11 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
Kimi K2.7 CodevsClaude Haiku 5.5Nemotron 3.5 LightningvsClaude Haiku 5.5Kimi K2.7 CodevsGPT-6.1 SolNemotron 3.5 LightningvsGPT-6.1 SolKimi K2.7 CodevsGemini 4 ArgonNemotron 3.5 LightningvsGemini 4 ArgonKimi K2.7 CodevsMuse Spark 1.3Nemotron 3.5 LightningvsMuse Spark 1.3Kimi K2.7 CodevsGrok 4.7Nemotron 3.5 LightningvsGrok 4.7Kimi K2.7 CodevsDeepSeek-V4.1-FlashNemotron 3.5 LightningvsDeepSeek-V4.1-FlashFrequently asked questions
Kimi K2.7 Code and Nemotron 3.5 Lightning 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 3.5 Lightning. Kimi K2.7 Code shipped 60 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Kimi K2.7 Code has 1T parameters, while Nemotron 3.5 Lightning has 30B. Context windows are 256k (Kimi K2.7 Code) vs 1M (Nemotron 3.5 Lightning). Kimi K2.7 Code is open weight, while Nemotron 3.5 Lightning is open source.
Direct benchmark comparisons are unavailable — Kimi K2.7 Code and Nemotron 3.5 Lightning don't publish scores on any of the same benchmarks.