# Agents-A1-4B vs Nemotron 3 Nano Omni 30B A3B Reasoning > Agents-A1-4B 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/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning - Markdown: https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md (~7,600 tokens) - Slim: https://www.anchorterminal.com/compare/agents-a1-4b-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/agents-a1-4b-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 Agents-A1-4B 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 risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself. - Agents-A1-4B: grade C, 61/100, rank #34 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.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 ### Agents-A1-4B (C) Good for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence Ahead on: - Reach, 90 against 85 Watch for: The base isn't declared, and the technical report doesn't cover the 4B ### 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 43 Watch for: Loads with trust_remote_code in every documented runtime command ## Score by category | Category | Weight | Agents-A1-4B | Nemotron 3 Nano Omni 30B A3B Reasoning | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 85 | Agents-A1-4B +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) | 43 | 65 | Nemotron 3 Nano Omni 30B A3B Reasoning +22 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **60.7 · C** | | ## Facts side by side | Fact | Agents-A1-4B | Nemotron 3 Nano Omni 30B A3B Reasoning | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | InternScience (Shanghai AI Laboratory) | 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 | 2026-07-14 | 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 | 3/5 (4) | 3/5 (4) | ## Verdicts **Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none. **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 ### Agents-A1-4B 1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector 2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work 3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14 4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them 5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention 6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding 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, Agents-A1-4B or Nemotron 3 Nano Omni 30B A3B Reasoning? Agents-A1-4B 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 risk, agent interface and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "agents-a1-4b", "b": "nemotron-3-nano-omni-30b-a3b-reasoning"}`. 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