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. | 400B | 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 |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.35Google | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00Parasail | — |
| $120Parasail · fp8 | — | |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 69.8% | 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. | 29% | — |
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. | 43.4% | — |
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 | 80.5% | — |
MGSMMultilingual math — solving grade-school word problems translated into multiple languages | 92.3% | — |
MathVistaMultimodal reasoning — mathematical reasoning about visual information | 73.7% | — |
MMMU-Pro · standard and vision meanMultimodal reasoning — average across the standard and vision sets | 59.6% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 73.4% | — |
| Overview | ||
| Company | Meta | NVIDIA |
| Release date | Apr 5 2025 | Oct 16 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
LLaMA 4 MaverickvsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2LLaMA 4 MaverickvsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGPT-6.1 TerraLLaMA 4 MaverickvsGemini 4 CarbonNemotron Labs Dory 1.0 4BvsGemini 4 CarbonLLaMA 4 MaverickvsGrok 4.7Nemotron Labs Dory 1.0 4BvsGrok 4.7LLaMA 4 MaverickvsDeepSeek-V4.1-Flash-1017Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017LLaMA 4 MaverickvsMistral Large 4Nemotron Labs Dory 1.0 4BvsMistral Large 4Frequently asked questions
Nemotron Labs Dory 1.0 4B leads LLaMA 4 Maverick on 1 of the 1 benchmark they both report (GPQA Diamond). LLaMA 4 Maverick shipped 559 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
LLaMA 4 Maverick has 400B parameters, while Nemotron Labs Dory 1.0 4B has 4B. Context windows are 1M (LLaMA 4 Maverick) vs 256k (Nemotron Labs Dory 1.0 4B).
On GPQA Diamond, Nemotron Labs Dory 1.0 4B leads at 72.41% vs LLaMA 4 Maverick at 69.8%.