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 | — |
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 | 400k |
| API pricingUSD per 1M tokens · lower wins | ||
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. | — | $1.25Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $10.00Azure |
| — | $750Azure | |
| 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% | 77.9% |
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% | — |
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. | — | 58.1% |
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 | OpenAI |
| Release date | Oct 16 2026 | Nov 19 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2GPT-5.1-Codex-MaxvsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsGemini 4 CarbonGPT-5.1-Codex-MaxvsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsMuse EmberGPT-5.1-Codex-MaxvsMuse EmberNemotron Labs Dory 1.0 4BvsGrok 4.7GPT-5.1-Codex-MaxvsGrok 4.7Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017GPT-5.1-Codex-MaxvsDeepSeek-V4.1-Flash-1017Nemotron Labs Dory 1.0 4BvsMistral Large 4GPT-5.1-Codex-MaxvsMistral Large 4Frequently asked questions
GPT-5.1-Codex-Max leads Nemotron Labs Dory 1.0 4B on 1 of the 1 benchmark they both report (SWE-Bench Verified). GPT-5.1-Codex-Max shipped 331 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Nemotron Labs Dory 1.0 4B) vs 400k (GPT-5.1-Codex-Max). Nemotron Labs Dory 1.0 4B is open weight, while GPT-5.1-Codex-Max is closed.
On SWE-Bench Verified, GPT-5.1-Codex-Max leads at 77.9% vs Nemotron Labs Dory 1.0 4B at 59.5%.