# Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3.8-27B > Qwen3.8-27B scores 68.6 (B) to Nemotron 3 Nano Omni 30B A3B Reasoning's 60.7 (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-qwen3-8-27b - Markdown: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-8-27b.md (~7,600 tokens) - Slim: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-8-27b.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-qwen3-8-27b.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 Qwen3.8-27B scores 68.6 (B) on agent readiness against Nemotron 3 Nano Omni 30B A3B Reasoning's 60.7 (C), and leads in 2 of 5 scored categories. Nemotron 3 Nano Omni 30B A3B Reasoning leads on risk, agent interface and documentation and upkeep. 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 - Qwen3.8-27B: grade B, 68.6/100, rank #10 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.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: - Risk, 60 against 55 - Agent interface, 90 against 70 - Documentation and upkeep, 65 against 36 Watch for: Loads with trust_remote_code in every documented runtime command ### Qwen3.8-27B (B) Good for: Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence. Ahead on: - Capability, 70 against 34 - Reach, 90 against 85 Watch for: No safety results, prompt-injection guidance or security contact ## Score by category | Category | Weight | Nemotron 3 Nano Omni 30B A3B Reasoning | Qwen3.8-27B | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 34 | 70 | Qwen3.8-27B +36 | | Reach | 25% (25 this run) | 85 | 90 | Qwen3.8-27B +5 | | Risk | 20% (20 this run) | 60 | 55 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 | | Agent interface | 10% (10 this run) | 90 | 70 | Nemotron 3 Nano Omni 30B A3B Reasoning +20 | | 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** | **68.6 · B** | | ## Facts side by side | Fact | Nemotron 3 Nano Omni 30B A3B Reasoning | Qwen3.8-27B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | NVIDIA | Alibaba (Qwen team) | | 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) | Apache-2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-08-24 | 2026-08-14 | | 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) | 3/5 (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. **Qwen3.8-27B.** A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and five providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template. ## 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 ### Qwen3.8-27B 1. Quickest start is `ollama run qwen3.8`, about 18 GB of files 2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, since FP8 on gfx1151 isn't documented 3. Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed 4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, and read the reasoning from `reasoning_content` 5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error 6. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without 7. Preserved thinking keeps earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions 8. Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts 9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented ## Questions ### Which is better for AI agents, Nemotron 3 Nano Omni 30B A3B Reasoning or Qwen3.8-27B? Qwen3.8-27B scores 68.6 (B) on agent readiness against Nemotron 3 Nano Omni 30B A3B Reasoning's 60.7 (C), and leads in 2 of 5 scored categories. Nemotron 3 Nano Omni 30B A3B Reasoning leads on risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-qwen3-8-27b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "nemotron-3-nano-omni-30b-a3b-reasoning", "b": "qwen3-8-27b"}`. 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How: https://www.anchorterminal.com/agents/#mcp