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. | 4B | 355B |
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. | 256k | 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 | ||
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% | 64.2% |
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% | 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% |
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% | — |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | 77.94% | — |
| Overview | ||
| Company | NVIDIA | Z.ai |
| Release date | Oct 16 2026 | Jul 28 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2GLM-4.5vsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsGPT-6.1 TerraGLM-4.5vsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGemini 4 CarbonGLM-4.5vsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsMuse EmberGLM-4.5vsMuse EmberNemotron Labs Dory 1.0 4BvsGrok 4.7GLM-4.5vsGrok 4.7Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017GLM-4.5vsDeepSeek-V4.1-Flash-1017Frequently asked questions
GLM-4.5 leads Nemotron Labs Dory 1.0 4B on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). Only GLM-4.5 has a verified first-party price for an available API: $0.60 per million input tokens and $2.20 per million output tokens. No currently available pay-as-you-go API rate is tracked for Nemotron Labs Dory 1.0 4B. GLM-4.5 shipped 445 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Nemotron Labs Dory 1.0 4B has 4B parameters, while GLM-4.5 has 355B. Context windows are 256k (Nemotron Labs Dory 1.0 4B) vs 128k (GLM-4.5).
On SWE-Bench Verified, GLM-4.5 leads at 64.2% vs Nemotron Labs Dory 1.0 4B at 59.5%. On GPQA Diamond, GLM-4.5 leads at 79.1% vs Nemotron Labs Dory 1.0 4B at 72.41%.