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 | 106B |
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. | — | $0.20 |
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. | — | $1.10 |
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. | — | $0.03 |
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. | — | $0.13Novita |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $0.85Novita |
| — | $68.50Novita · bf16 | |
| 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% | 59.8% |
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% | — |
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 | 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-4.5-AirvsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsGPT-6.1 TerraGLM-4.5-AirvsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGemini 4 CarbonGLM-4.5-AirvsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsMuse EmberGLM-4.5-AirvsMuse EmberNemotron Labs Dory 1.0 4BvsGrok 4.7GLM-4.5-AirvsGrok 4.7Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017GLM-4.5-AirvsDeepSeek-V4.1-Flash-1017Frequently asked questions
GLM-4.5-Air leads Nemotron Labs Dory 1.0 4B on 1 of the 1 benchmark they both report (SWE-Bench Verified). Only GLM-4.5-Air has a verified first-party price for an available API: $0.20 per million input tokens and $1.10 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-Air 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-Air has 106B. Context windows are 256k (Nemotron Labs Dory 1.0 4B) vs 128k (GLM-4.5-Air).
On SWE-Bench Verified, GLM-4.5-Air leads at 59.8% vs Nemotron Labs Dory 1.0 4B at 59.5%.