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. | 74B | 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. | — | 128k |
| 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 every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $0.60Z.AI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $2.20Z.AI |
| — | $230Z.ai list price | |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 86.43% | 79.1% |
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. | — | 8% |
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. | — | 64.2% |
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. | 50.98% | — |
MCP AtlasMulti-step tool use — Can the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | 52.2% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 66.75% | — |
| Overview | ||
| Company | NVIDIA | Z.ai |
| Release date | Oct 15 2026 | Jul 28 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5GLM-4.5vsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsGPT-6.1 SolGLM-4.5vsGPT-6.1 SolNemotron 3.5 Bolt 74BvsGemini 4 ArgonGLM-4.5vsGemini 4 ArgonNemotron 3.5 Bolt 74BvsMuse Spark OpenGLM-4.5vsMuse Spark OpenNemotron 3.5 Bolt 74BvsGrok 4.7GLM-4.5vsGrok 4.7Nemotron 3.5 Bolt 74BvsDeepSeek-V4.1-FlashGLM-4.5vsDeepSeek-V4.1-FlashFrequently asked questions
Nemotron 3.5 Bolt 74B leads GLM-4.5 on 1 of the 1 benchmark they both report (GPQA Diamond). Only GLM-4.5 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 3.5 Bolt 74B. GLM-4.5 shipped 444 days before Nemotron 3.5 Bolt 74B, so benchmark comparisons should account for the intervening progress.
Nemotron 3.5 Bolt 74B has 74B parameters, while GLM-4.5 has 355B.
On GPQA Diamond, Nemotron 3.5 Bolt 74B leads at 86.43% vs GLM-4.5 at 79.1%.