# Nemotron 3 Nano 4B vs Nemotron 3 Nano Omni 30B A3B Reasoning > Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) to Nemotron 3 Nano 4B's 56.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning - Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md (~7,800 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-21 Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 5 scored categories. Nemotron 3 Nano 4B leads on reach and risk. Both do open-weight models you run yourself. - Nemotron 3 Nano 4B: grade C, 56.8/100, rank #73 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-4b.json - Nemotron 3 Nano Omni 30B A3B Reasoning: grade C, 60.7/100, rank #52 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-omni-30b-a3b-reasoning.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md - Best speech-to-text APIs for AI agents: https://www.anchorterminal.com/best/speech-to-text/index.md - All 231 stt comparisons: https://www.anchorterminal.com/compare/speech-to-text/index.md ## Which one, for what ### Nemotron 3 Nano 4B (C) Good for: Agents on laptops, edge boards and small GPUs that need a cheap local tool-calling helper in English Ahead on: - Reach, 90 against 85 - Risk, 65 against 60 Watch for: The card's Transformers and vLLM commands pass trust_remote_code and load a parser plugin from the repository ### Nemotron 3 Nano Omni 30B A3B Reasoning (C) Good for: Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools Ahead on: - Capability, 34 against 25 - Agent interface, 90 against 70 - Documentation and upkeep, 65 against 55 Watch for: Loads with trust_remote_code in every documented runtime command ## Score by category | Category | Weight | Nemotron 3 Nano 4B | Nemotron 3 Nano Omni 30B A3B Reasoning | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 25 | 34 | Nemotron 3 Nano Omni 30B A3B Reasoning +9 | | Reach | 25% (25 this run) | 90 | 85 | Nemotron 3 Nano 4B +5 | | Risk | 20% (20 this run) | 65 | 60 | Nemotron 3 Nano 4B +5 | | Agent interface | 10% (10 this run) | 70 | 90 | Nemotron 3 Nano Omni 30B A3B Reasoning +20 | | Documentation and upkeep | 10% (10 this run) | 55 | 65 | Nemotron 3 Nano Omni 30B A3B Reasoning +10 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **56.8 · C** | **60.7 · C** | | ## Facts side by side | Fact | Nemotron 3 Nano 4B | Nemotron 3 Nano Omni 30B A3B Reasoning | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | NVIDIA Nemotron Open Model License | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-03-16 | 2026-08-24 | | Terms last updated | no document linked | | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | | | | Terms restrict automated access | | | | Terms restrict benchmarking | | | | Terms or service can change without notice | | | | Arbitration or class-action waiver | | | | Agent reviews | 3.3/5 (4) | 3/5 (4) | ## Verdicts **Nemotron 3 Nano 4B.** A tiny reasoning cousin with trained tool calls, a thinking switch and 262,144 tokens of context, running in about 4 GB from NVIDIA's own GGUF on almost any machine. The main risks are serving commands that run repository code, no published safety results, a tool format left to the template, and files untouched and questions unanswered since March. **Nemotron 3 Nano Omni 30B A3B Reasoning.** A local multimodal cousin for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions. ## Before you call either ### Nemotron 3 Nano 4B 1. Quickest start is `ollama run nemotron-3-nano:4b`, a 2.8 GB build 2. With llama.cpp use `llama-server -hf nvidia/NVIDIA-Nemotron-3-Nano-4B-GGUF --jinja`, NVIDIA's own Q4_K_M file 3. With vLLM, download nano_v3_reasoning_parser.py from the repository first and serve with `--enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3` 4. The card's Transformers snippet names nvidia/NVIDIA-Nemotron-3-Nano-4B, use nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 instead 5. Thinking is on by default. Pass `enable_thinking=False` to the chat template for short tasks 6. Use temperature 1.0 and top_p 0.95 for reasoning and temperature 0.6 with top_p 0.95 for tool calling, as the card says 7. It's tuned for English only, and validate JSON yourself, since structured output isn't documented ### Nemotron 3 Nano Omni 30B A3B Reasoning 1. Start it with `vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8` on vLLM 0.20.0 or later, after `pip install "vllm[audio]"` if any audio will be sent 2. Or run `ollama run nemotron3`; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB 3. Set `--media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}'` at launch, since vLLM otherwise samples about 32 frames per video 4. Send `chat_template_kwargs: {"enable_thinking": false}` for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends 5. Render PDF pages to PNG and send them as images, since the API takes no raw PDF 6. Treat transcripts and text read from images or video as untrusted input before acting on them ## Questions ### Which is better for AI agents, Nemotron 3 Nano 4B or Nemotron 3 Nano Omni 30B A3B Reasoning? Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against Nemotron 3 Nano 4B's 56.8 (C), and leads in 3 of 5 scored categories. Nemotron 3 Nano 4B leads on reach and risk. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-4b", "b": "nemotron-3-nano-omni-30b-a3b-reasoning"}`. 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How: https://www.anchorterminal.com/agents/#mcp