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. | 8B | 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 |
| These models have no shared benchmark scores. | ||
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% |
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 | 35.9% | — |
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% |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | 68.1% | — |
MGSMMultilingual math — solving grade-school word problems translated into multiple languages | 70.5% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 50.3% | — |
| Overview | ||
| Company | NVIDIA | |
| Release date | Oct 3 2024 | Oct 16 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 1.5 Flash-8BvsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2Gemini 1.5 Flash-8BvsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGPT-6.1 TerraGemini 1.5 Flash-8BvsMuse EmberNemotron Labs Dory 1.0 4BvsMuse EmberGemini 1.5 Flash-8BvsGrok 4.7Nemotron Labs Dory 1.0 4BvsGrok 4.7Gemini 1.5 Flash-8BvsDeepSeek-V4.1-Flash-1017Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017Gemini 1.5 Flash-8BvsMistral Large 4Nemotron Labs Dory 1.0 4BvsMistral Large 4Frequently asked questions
Gemini 1.5 Flash-8B and Nemotron Labs Dory 1.0 4B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 1.5 Flash-8B shipped 743 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Gemini 1.5 Flash-8B has 8B parameters, while Nemotron Labs Dory 1.0 4B has 4B. Context windows are 1M (Gemini 1.5 Flash-8B) vs 256k (Nemotron Labs Dory 1.0 4B). Gemini 1.5 Flash-8B is closed, while Nemotron Labs Dory 1.0 4B is open weight.
Direct benchmark comparisons are unavailable — Gemini 1.5 Flash-8B and Nemotron Labs Dory 1.0 4B don't publish scores on any of the same benchmarks.