Nemotron 3 NanovsGLM-4.6
Nemotron 3 Nano | GLM-4.6 | |
|---|---|---|
| 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. | 30B | 355B |
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. | — | 200k |
| 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. | — | $2.20 |
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. | — | $0.11 |
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. | — | $0.43Venice |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | — | $1.75Venice |
| BenchmarksPublished by one model only | ||
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. | — | 68% |
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. | 68.3% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 73% | — |
| Overview | ||
| Company | NVIDIA | Z.ai |
| Release date | Dec 15 2025 | Sep 30 2025 |
| Access | Open Source | Open Weight |
Other comparisons
Frequently asked questions
Nemotron 3 Nano and GLM-4.6 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GLM-4.6 has a verified first-party API price: $0.60 per million input tokens and $2.20 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Nano. GLM-4.6 shipped 76 days before Nemotron 3 Nano, so benchmark comparisons should account for the intervening progress.
Nemotron 3 Nano has 30B parameters, while GLM-4.6 has 355B. Nemotron 3 Nano is open source, while GLM-4.6 is open weight.
Direct benchmark comparisons are unavailable — Nemotron 3 Nano and GLM-4.6 don't publish scores on any of the same benchmarks.
Nemotron 3 Nano was released by NVIDIA on Dec 15 2025.
GLM-4.6 was released by Z.ai on Sep 30 2025.
Only GLM-4.6 has a verified first-party API price: $0.60 per million input tokens and $2.20 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Nano. Rates are pay-as-you-go API prices verified on August 18, 2026.
Nemotron 3 Nano is an open source model released by NVIDIA. GLM-4.6 is an open weight model released by Z.ai.