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. | 76.2% | 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. | 91.9% | 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. | 48% | — |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | 43.3% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 50% | — |
LiveCodeBench ProCompetitive programming — Elo rating on competitive programming problems in LiveCodeBench Pro | 2439 | — |
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% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | 54.2% | — |
APEX-AgentsExpert agentic work — expert-level agentic work tasks (AI Productivity Index) | 18.4% | — |
MCP Atlas · original harnessMulti-step tool use — pass rate under the original tool-use harness, before Scale changed the judge and retry handling in April 2026 | 54.1% | — |
τ²-bench · retailAgentic tasks — task success in the retail domain of the second-generation customer-service evaluation | 85.3% | — |
τ²-bench · telecomAgentic tasks — task success in the telecom domain of the second-generation customer-service evaluation | 98% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 59.2% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 37.5% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 45.8% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 31.1% | — |
AIME 2025Reasoning — single-answer accuracy on the 2025 invitational mathematics exam without tools | 95% | — |
IFBenchInstruction following — Tests whether the AI can follow detailed instructions and satisfy multiple constraints at once. Higher is better. | — | 77.94% |
SimpleQA VerifiedKnowledge — accuracy on the vetted factual questions in SimpleQA Verified | 72.1% | — |
MMMLUKnowledge — average accuracy on multilingual translations of MMLU | 91.8% | — |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | 81.4% | — |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 81% | — |
MRCR v2 (8-needle) · 128k averageLong context — Tests whether the AI can find specific details buried inside a very long document (around 128k tokens — roughly a long book). Higher is better. | 77% | — |
MRCR v2 (8-needle) · 1M pointwiseLong context — Tests whether the AI can find specific details buried inside an enormous document (around 1 million tokens — many books). Higher is better. | 26.3% | — |
| Overview | ||
| Company | NVIDIA | |
| Release date | Nov 18 2025 | Oct 16 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 3.0 ProvsClaude Fable 5.2Nemotron Labs Dory 1.0 4BvsClaude Fable 5.2Gemini 3.0 ProvsGPT-6.1 TerraNemotron Labs Dory 1.0 4BvsGPT-6.1 TerraGemini 3.0 ProvsMuse EmberNemotron Labs Dory 1.0 4BvsMuse EmberGemini 3.0 ProvsGrok 4.7Nemotron Labs Dory 1.0 4BvsGrok 4.7Gemini 3.0 ProvsDeepSeek-V4.1-Flash-1017Nemotron Labs Dory 1.0 4BvsDeepSeek-V4.1-Flash-1017Gemini 3.0 ProvsMistral Large 4Nemotron Labs Dory 1.0 4BvsMistral Large 4Frequently asked questions
Gemini 3.0 Pro leads Nemotron Labs Dory 1.0 4B on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). Gemini 3.0 Pro shipped 332 days before Nemotron Labs Dory 1.0 4B, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 3.0 Pro) vs 256k (Nemotron Labs Dory 1.0 4B). Gemini 3.0 Pro is closed, while Nemotron Labs Dory 1.0 4B is open weight.
On SWE-Bench Verified, Gemini 3.0 Pro leads at 76.2% vs Nemotron Labs Dory 1.0 4B at 59.5%. On GPQA Diamond, Gemini 3.0 Pro leads at 91.9% vs Nemotron Labs Dory 1.0 4B at 72.41%.