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. | 120B | 744B |
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. | 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. | — | $1.00 |
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. | — | $3.20 |
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. | — | $0.20 |
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. | $0.08DekaLLM | $0.60GMICloud |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.40DeepInfra | $1.92GMICloud |
| Benchmarks | ||
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% | 28% |
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% | 77.8% |
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% | 86% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 73.3% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 56.2% |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 75.9% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 50.4% |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | 36 | — |
| Overview | ||
| Company | NVIDIA | Z.ai |
| Release date | Mar 11 2026 | Feb 12 2026 |
| Access | Open Source | Open Weight |
| Model details | View model | View model |
Other comparisons
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GLM-5 leads Nemotron 3 Super on 2 of the 3 benchmarks they both report (BullshitBench v2, SWE-Bench Verified, GPQA Diamond). Only GLM-5 has a verified first-party API price: $1.00 per million input tokens and $3.20 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3 Super. GLM-5 shipped 27 days before Nemotron 3 Super, so benchmark comparisons should account for the intervening progress.
Nemotron 3 Super has 120B parameters, while GLM-5 has 744B. Nemotron 3 Super is open source, while GLM-5 is open weight.
On BullshitBench v2, Nemotron 3 Super leads at 54% vs GLM-5 at 28%. On SWE-Bench Verified, GLM-5 leads at 77.8% vs Nemotron 3 Super at 60.5%. On GPQA Diamond, GLM-5 leads at 86% vs Nemotron 3 Super at 79.2%.