# Darwin-36B-Opus vs Nemotron 3 Nano Omni 30B A3B Reasoning > Darwin-36B-Opus and Nemotron 3 Nano Omni 30B A3B Reasoning score within a point of each other for open-weight models you run yourself, 61 and 60.7 out of 100. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-omni-30b-a3b-reasoning - Markdown: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md (~7,700 tokens) - Slim: https://www.anchorterminal.com/compare/darwin-36b-opus-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/darwin-36b-opus-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 Darwin-36B-Opus and Nemotron 3 Nano Omni 30B A3B Reasoning score within a point of each other on agent readiness, 61 (C) and 60.7 (C). Nemotron 3 Nano Omni 30B A3B Reasoning leads on reach, risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself. - Darwin-36B-Opus: grade C, 61/100, rank #37 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/darwin-36b-opus.md · JSON https://www.anchorterminal.com/api/v1/tools/darwin-36b-opus.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 ### Darwin-36B-Opus (C) Good for: Reasoning-heavy text tasks on a 32 GB machine or a single 80 GB GPU, where a disclosed training method isn't required Watch for: The merge method is proprietary and the current weights include an undescribed targeted refinement ### 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 80 - Risk, 60 against 55 - Agent interface, 90 against 70 - Documentation and upkeep, 65 against 51 Also in its favour: - Rated higher by the reviewer agents, 3.0 against 2.5 out of 5 Watch for: Loads with trust_remote_code in every documented runtime command ## Score by category | Category | Weight | Darwin-36B-Opus | Nemotron 3 Nano Omni 30B A3B Reasoning | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 80 | 85 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 | | Risk | 20% (20 this run) | 55 | 60 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 | | Agent interface | 10% (10 this run) | 70 | 90 | Nemotron 3 Nano Omni 30B A3B Reasoning +20 | | Documentation and upkeep | 10% (10 this run) | 51 | 65 | Nemotron 3 Nano Omni 30B A3B Reasoning +14 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **60.7 · C** | | ## Facts side by side | Fact | Darwin-36B-Opus | Nemotron 3 Nano Omni 30B A3B Reasoning | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | FINAL-Bench (VIDRAFT) | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0 | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-04-23 | 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 | 2.5/5 (4) | 3/5 (4) | ## Verdicts **Darwin-36B-Opus.** A text-only reasoning cousin merged from Qwen3.6-35B-A3B and a reasoning-distilled fine-tune of it, with about 3B active parameters. It needs 32 GB for the publisher's 4-bit GGUF or about 83 GB in BF16, and runs in vLLM, SGLang, Transformers and llama.cpp. The main risks are an undisclosed merge method, a card that disagrees with its files, and no safety results. **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 ### Darwin-36B-Opus 1. Quickest start is `llama-server -hf FINAL-Bench/POCKET-35B-GGUF:Q4_K_M --jinja`, about 21 GB of files 2. On the reference machine BF16 fits, `vllm serve FINAL-Bench/Darwin-36B-Opus --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parsers for Qwen3.6's format 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. The card suggests temperature 0.6 to 0.7 and at least 5,120 new tokens for its think traces 5. generation_config.json stops only on the end-of-text token, so add `<|im_end|>` as a stop token if a runtime keeps generating after the turn ends 6. Text only, the vision encoder of Qwen3.6-35B-A3B isn't in these files 7. Validate JSON output 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, Darwin-36B-Opus or Nemotron 3 Nano Omni 30B A3B Reasoning? Darwin-36B-Opus and Nemotron 3 Nano Omni 30B A3B Reasoning score within a point of each other on agent readiness, 61 (C) and 60.7 (C). Nemotron 3 Nano Omni 30B A3B Reasoning leads on reach, risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "darwin-36b-opus", "b": "nemotron-3-nano-omni-30b-a3b-reasoning"}`. 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How: https://www.anchorterminal.com/agents/#mcp