# DeepSeek V4.1 Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning > Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) to DeepSeek V4.1 Flash'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/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning - Markdown: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md (~7,850 tokens) - Slim: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-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/deepseek-v4-1-flash-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 DeepSeek V4.1 Flash's 59 (C), and leads in 4 of 5 scored categories. DeepSeek V4.1 Flash leads on capability. Both do open-weight models you run yourself. - DeepSeek V4.1 Flash: grade C, 59/100, rank #63 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.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 ### DeepSeek V4.1 Flash (C) Good for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. Ahead on: - Capability, 69 against 34 Watch for: 510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine ### 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 50 - Risk, 60 against 50 - Agent interface, 90 against 75 - Documentation and upkeep, 65 against 48 Watch for: Loads with trust_remote_code in every documented runtime command ## Score by category | Category | Weight | DeepSeek V4.1 Flash | Nemotron 3 Nano Omni 30B A3B Reasoning | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 69 | 34 | DeepSeek V4.1 Flash +35 | | Reach | 25% (25 this run) | 50 | 85 | Nemotron 3 Nano Omni 30B A3B Reasoning +35 | | Risk | 20% (20 this run) | 50 | 60 | Nemotron 3 Nano Omni 30B A3B Reasoning +10 | | Agent interface | 10% (10 this run) | 75 | 90 | Nemotron 3 Nano Omni 30B A3B Reasoning +15 | | Documentation and upkeep | 10% (10 this run) | 48 | 65 | Nemotron 3 Nano Omni 30B A3B Reasoning +17 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **59 · C** | **60.7 · C** | | ## Facts side by side | Fact | DeepSeek V4.1 Flash | Nemotron 3 Nano Omni 30B A3B Reasoning | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | DeepSeek | NVIDIA | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-17 | 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 | 2.8/5 (4) | 3/5 (4) | ## Verdicts **DeepSeek V4.1 Flash.** A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories. **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 ### DeepSeek V4.1 Flash 1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path "$SAVE_PATH" --config config.json --interactive 2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command 3. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding 4. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4 5. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75 6. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends 7. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update ### 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, DeepSeek V4.1 Flash or Nemotron 3 Nano Omni 30B A3B Reasoning? Nemotron 3 Nano Omni 30B A3B Reasoning scores 60.7 (C) on agent readiness against DeepSeek V4.1 Flash's 59 (C), and leads in 4 of 5 scored categories. DeepSeek V4.1 Flash leads on capability. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-1-flash", "b": "nemotron-3-nano-omni-30b-a3b-reasoning"}`. 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