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. | 2M | 256k |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 59.1% | 72.41% |
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% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | 34.3% | — |
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% |
MATHReasoning — accuracy on the original competition mathematics test set | 86.5% | — |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 77.94% |
MMLU-ProKnowledge — accuracy on the ten-option MMLU-Pro benchmark | 75.8% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 65.9% | — |
| Overview | ||
| Company | NVIDIA | |
| Release date | Sep 24 2024 | Oct 16 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 1.5 Pro-002vsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2Gemini 1.5 Pro-002vsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGPT-6.1 TerraGemini 1.5 Pro-002vsMuse EmberNemotron Labs Dory 1.0 4BvsMuse EmberGemini 1.5 Pro-002vsGrok 4.7Nemotron Labs Dory 1.0 4BvsGrok 4.7Gemini 1.5 Pro-002vsDeepSeek-V4.1-Flash-1017Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017Gemini 1.5 Pro-002vsMistral Large 4Nemotron Labs Dory 1.0 4BvsMistral Large 4Frequently asked questions
Nemotron Labs Dory 1.0 4B leads Gemini 1.5 Pro-002 on 1 of the 1 benchmark they both report (GPQA Diamond). Gemini 1.5 Pro-002 shipped 752 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Context windows are 2M (Gemini 1.5 Pro-002) vs 256k (Nemotron Labs Dory 1.0 4B). Gemini 1.5 Pro-002 is closed, while Nemotron Labs Dory 1.0 4B is open weight.
On GPQA Diamond, Nemotron Labs Dory 1.0 4B leads at 72.41% vs Gemini 1.5 Pro-002 at 59.1%.