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. | 1M | 256k |
| 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. | 21.4% | 59.5% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 62.1% | 72.41% |
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. | 15% | — |
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. | 35.1% | — |
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% |
MMLU-ProKnowledge — accuracy on the ten-option MMLU-Pro benchmark | 76.4% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 70.7% | — |
| Overview | ||
| Company | NVIDIA | |
| Release date | Dec 11 2024 | Oct 16 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 2.0 FlashvsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2Gemini 2.0 FlashvsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGPT-6.1 TerraGemini 2.0 FlashvsMuse EmberNemotron Labs Dory 1.0 4BvsMuse EmberGemini 2.0 FlashvsGrok 4.7Nemotron Labs Dory 1.0 4BvsGrok 4.7Gemini 2.0 FlashvsDeepSeek-V4.1-Flash-1017Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017Gemini 2.0 FlashvsMistral Large 4Nemotron Labs Dory 1.0 4BvsMistral Large 4Frequently asked questions
Nemotron Labs Dory 1.0 4B leads Gemini 2.0 Flash on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). Gemini 2.0 Flash shipped 674 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 2.0 Flash) vs 256k (Nemotron Labs Dory 1.0 4B). Gemini 2.0 Flash is closed, while Nemotron Labs Dory 1.0 4B is open weight.
On SWE-Bench Verified, Nemotron Labs Dory 1.0 4B leads at 59.5% vs Gemini 2.0 Flash at 21.4%. On GPQA Diamond, Nemotron Labs Dory 1.0 4B leads at 72.41% vs Gemini 2.0 Flash at 62.1%.