# Agents-A1-4B vs Nemotron 3 Nano 30B A3B > Nemotron 3 Nano 30B A3B scores 67.1 (B) to Agents-A1-4B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b - Markdown: https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.md (~6,550 tokens) - Slim: https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.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 30B A3B scores 67.1 (B) on agent readiness against Agents-A1-4B's 61 (C), and leads in 3 of 4 scored categories. 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 30B A3B: grade B, 67.1/100, rank #12 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/nemotron-3-nano-30b-a3b.md · JSON https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.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 ## 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 Watch for: The base isn't declared, and the technical report doesn't cover the 4B ### Nemotron 3 Nano 30B A3B (B) Good for: Agents with a 24 to 32 GB GPU or Mac that want a quick tool-calling and reasoning cousin with very long context. Ahead on: - Risk, 60 against 55 - Agent interface, 90 against 70 - Documentation and upkeep, 75 against 43 Watch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository ## Score by category | Category | Weight | Agents-A1-4B | Nemotron 3 Nano 30B A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 90 | 90 | even | | Risk | 20% (20 this run) | 55 | 60 | Nemotron 3 Nano 30B A3B +5 | | Agent interface | 10% (10 this run) | 70 | 90 | Nemotron 3 Nano 30B A3B +20 | | Documentation and upkeep | 10% (10 this run) | 43 | 75 | Nemotron 3 Nano 30B A3B +32 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **67.1 · B** | | ## Facts side by side | Fact | Agents-A1-4B | Nemotron 3 Nano 30B A3B | | --- | --- | --- | | 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 Nemotron Open Model License (custom, Apache-style, 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) | 3.3/5 (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 30B A3B.** A fast hybrid Mamba-Transformer cousin with 3.5B active parameters for reasoning and tool calling over very long inputs, in every major local runtime. A 4-bit build needs about 22 to 23 GB. The main risks are that NVIDIA's documented vLLM path runs repository code, no safety results are published and NVIDIA hasn't answered reports since March. ## 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 30B A3B 1. Start it with `ollama run nemotron-3-nano:30b` (18 GB MLX on a Mac, 24 GB q4_K_M elsewhere) 2. On the reference machine use BF16 with vLLM, after `wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py`, then `vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 --max-model-len 262144 --trust-remote-code --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser-plugin nano_v3_reasoning_parser.py --reasoning-parser nano_v3` 3. NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151 4. Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text 5. Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning 6. Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks 7. Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens 8. In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3 9. Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution 10. Validate JSON output yourself or use the runtime's guided decoding, since none is documented for the model ## Questions ### Which is better for AI agents, Agents-A1-4B or Nemotron 3 Nano 30B A3B? Nemotron 3 Nano 30B A3B scores 67.1 (B) on agent readiness against Agents-A1-4B's 61 (C), and leads in 3 of 4 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "agents-a1-4b", "b": "nemotron-3-nano-30b-a3b"}`. From a terminal: `anchor compare agents-a1-4b nemotron-3-nano-30b-a3b` - Each listing in full: https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json and https://www.anchorterminal.com/api/v1/tools/nemotron-3-nano-30b-a3b.json ## Other comparisons with Agents-A1-4B or Nemotron 3 Nano 30B A3B - [Agents-A1-4B vs Apertus v1.5 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.md) - [Agents-A1-4B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.md) - [Agents-A1-4B vs BigBang-v1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.md) - [Agents-A1-4B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.md) - [Agents-A1-4B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.md) - [Agents-A1-4B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.md) - [Agents-A1-4B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.md) - 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