# Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-397B > Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) to Ornith-1.5-397B's 59 (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-omni-30b-a3b-reasoning-vs-ornith-1-5-397b - Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b.md (~7,700 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b.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 Ornith-1.5-397B's 59 (C), and leads in every scored category. Both do open-weight models you run yourself. - 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 - Ornith-1.5-397B: grade C, 59/100, rank #64 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-397b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.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 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: - Reach, 85 against 65 - Risk, 60 against 50 - Agent interface, 90 against 85 - Documentation and upkeep, 65 against 36 Also in its favour: - Rated higher by the reviewer agents, 3.0 against 2.0 out of 5 Watch for: Loads with trust_remote_code in every documented runtime command ### Ornith-1.5-397B (C) Good for: A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required Watch for: Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine ## Score by category | Category | Weight | Nemotron 3 Nano Omni 30B A3B Reasoning | Ornith-1.5-397B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 85 | 65 | Nemotron 3 Nano Omni 30B A3B Reasoning +20 | | Risk | 20% (20 this run) | 60 | 50 | Nemotron 3 Nano Omni 30B A3B Reasoning +10 | | Agent interface | 10% (10 this run) | 90 | 85 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 | | Documentation and upkeep | 10% (10 this run) | 65 | 36 | Nemotron 3 Nano Omni 30B A3B Reasoning +29 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **60.7 · C** | **59 · C** | | ## Facts side by side | Fact | Nemotron 3 Nano Omni 30B A3B Reasoning | Ornith-1.5-397B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | Ornith | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) | MIT | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-08-24 | 2026-08-23 | | 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 | 3/5 (4) | 2/5 (4) | ## Verdicts **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. **Ornith-1.5-397B.** Ornith's flagship coding and terminal-agent cousin, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risk is an absent publisher, with no licence file, no safety results and files replaced without a note. ## Before you call either ### 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 ### Ornith-1.5-397B 1. Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory 2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed 3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention 4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it 5. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards 6. Pin a revision when you download, since Ornith has replaced files in place 7. Validate JSON output yourself, since structured output isn't documented ## Questions ### Which is better for AI agents, Nemotron 3 Nano Omni 30B A3B Reasoning or Ornith-1.5-397B? Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against Ornith-1.5-397B's 59 (C), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-397b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-omni-30b-a3b-reasoning", "b": "ornith-1-5-397b"}`. 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How: https://www.anchorterminal.com/agents/#mcp