Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 2.8T | 4B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 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. | $4.00 | — |
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. | $20.00 | — |
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. | $0.20 | — |
| $1,800Moonshot AI list price | — | |
| 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. | — | 59.5% |
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. | — | 18.1% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 72.41% |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 77.94% |
| Overview | ||
| Company | Moonshot AI | NVIDIA |
| Release date | Oct 23 2026 | Oct 16 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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Kimi K3.1 Pro Highspeed and Nemotron Labs Dory 1.0 4B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Kimi K3.1 Pro Highspeed has a verified first-party price for an available API: $4.00 per million input tokens and $20.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for Nemotron Labs Dory 1.0 4B. Nemotron Labs Dory 1.0 4B shipped 7 days before Kimi K3.1 Pro Highspeed, so benchmark comparisons should account for the intervening progress.
Kimi K3.1 Pro Highspeed has 2.8T parameters, while Nemotron Labs Dory 1.0 4B has 4B. Context windows are 1M (Kimi K3.1 Pro Highspeed) vs 256k (Nemotron Labs Dory 1.0 4B). Kimi K3.1 Pro Highspeed is closed, while Nemotron Labs Dory 1.0 4B is open weight.
Direct benchmark comparisons are unavailable — Kimi K3.1 Pro Highspeed and Nemotron Labs Dory 1.0 4B don't publish scores on any of the same benchmarks.