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. | 9.6% | 59.5% |
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | 74.3% | — |
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 | 54.9% | — |
HellaSwagReasoning — commonsense completion of short scenarios | 86.5% | — |
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. | 78.9% | — |
MGSMMultilingual math — solving grade-school word problems translated into multiple languages | 82.6% | — |
GSM8KGrade-school math — Grade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | 86.2% | — |
MathVistaMultimodal reasoning — mathematical reasoning about visual information | 58.4% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 56.1% | — |
| Overview | ||
| Company | NVIDIA | |
| Release date | May 14 2024 | Oct 16 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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Nemotron Labs Dory 1.0 4B leads Gemini 1.5 Flash on 1 of the 1 benchmark they both report (SWE-Bench Verified). Gemini 1.5 Flash shipped 885 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 1.5 Flash) vs 256k (Nemotron Labs Dory 1.0 4B). Gemini 1.5 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 1.5 Flash at 9.6%.