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/70B | 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. | 8k | 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% |
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. | 81.7% | — |
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 | 50.4% | — |
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. | 82% | — |
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. | 93% | — |
| Overview | ||
| Company | Meta | NVIDIA |
| Release date | Apr 18 2024 | Oct 16 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
LLaMA 3 (8B/70B)vsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2LLaMA 3 (8B/70B)vsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGPT-6.1 TerraLLaMA 3 (8B/70B)vsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsGemini 4 CarbonLLaMA 3 (8B/70B)vsGrok 4.7Nemotron Labs Dory 1.0 4BvsGrok 4.7LLaMA 3 (8B/70B)vsDeepSeek-V4.1-Flash-1017Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017LLaMA 3 (8B/70B)vsMistral Large 4Nemotron Labs Dory 1.0 4BvsMistral Large 4Frequently asked questions
LLaMA 3 (8B/70B) 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. LLaMA 3 (8B/70B) shipped 911 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
LLaMA 3 (8B/70B) has 8B/70B parameters, while Nemotron Labs Dory 1.0 4B has 4B. Context windows are 8k (LLaMA 3 (8B/70B)) vs 256k (Nemotron Labs Dory 1.0 4B).
Direct benchmark comparisons are unavailable — LLaMA 3 (8B/70B) and Nemotron Labs Dory 1.0 4B don't publish scores on any of the same benchmarks.