# Arete-27B vs Nemotron 3 Nano 30B A3B > Nemotron 3 Nano 30B A3B scores 67.6 (B) to Arete-27B's 55.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-30b-a3b - Markdown: https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-30b-a3b.md (~9,750 tokens) - Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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) - DNS: dig +short TXT .grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.. and .vs.. under the zone, and the same under grade.letme.dev) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-11-10 Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category. Both do open-weight models you run yourself. - Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json - Nemotron 3 Nano 30B A3B: grade B, 67.6/100, rank #12 of 160 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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### Arete-27B (C) Good for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine Watch for: No quantised build from Nous Research, so the GGUF route depends on a third party ### 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: - Reach, 90 against 50 - Risk, 60 against 55 - Agent interface, 90 against 60 - Documentation and upkeep, 80 against 63 Watch for: The documented vLLM, SGLang and TensorRT-LLM commands pass trust-remote-code and load a parser file from the repository ## Why the edge ### Reach: Nemotron 3 Nano 30B A3B +40 - Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50. - Nemotron 3 Nano 30B A3B (90): Memory earns 25 because the smallest 4-bit build is Ollama's NVFP4 MLX build at 18 GB, 21.6 GB with the fifth added, and NVIDIA's own NVFP4 build lands in the same 24 GB band. Runtimes earn the full 25, with released llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all running it. Builds earn 15 for NVIDIA's own FP8 and NVFP4 builds. Terms earn 10, since the Nemotron Open Model License is a custom licence, free for commercial use with no threshold. Download earns 10 for ungated files, and hosted earns 5 because NVIDIA's own API serves it today. ### Agent interface: Nemotron 3 Nano 30B A3B +30 - Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60. - Nemotron 3 Nano 30B A3B (90): The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 25, since the card names the qwen3_coder parser and NVIDIA's own vLLM cookbook shows tools declared in the OpenAI schema and calls coming back as tool_calls. Trained tool calling earns 20, multi-step tool use in training and several calls to a turn in the template. Structured output earns 5, a claim of structured output training with no way to ask for JSON. Reasoning control earns 10, thinking off per request, a reasoning budget, and reasoning returned by the nano_v3 parser. Context earns 10 for 1M tokens with no scaling. ### Documentation and upkeep: Nemotron 3 Nano 30B A3B +17 - Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63. - Nemotron 3 Nano 30B A3B (80): The model card earns 20, with all seven items on the card and its subcards. Training data earns 20, datasets listed with sources and token counts, both cut-offs, the filtering, and much of the data released, though some synthetic sets don't give a size. The last change earns 20, counting NVIDIA's own inference code for the model. The repositories last changed with a README edit on 24 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 10, NVIDIA replies on 13 of 30 discussions and none since March. Version policy earns 5 for a first release with no dated notes. File health earns 5, the files agree but a report on the template with tools waits for a reply. ### Risk: Nemotron 3 Nano 30B A3B +5 - Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55. - Nemotron 3 Nano 30B A3B (60): File safety earns 15, safetensors only but with the documented serving path passing trust-remote-code and loading a reasoning parser file from the repository. Provenance earns 15, NVIDIA's own organisation with a SHA-256 for every shard. Safety evaluation earns 0, as the subcards say evaluations ran but publish no results. Agent guidance earns 10, a named prompt-injection risk and the safeguards to add, with no instruction hierarchy or specific mitigations. Licence risk earns 10, losing 5 because the licence was replaced on 23 December 2025, eight days after release, with no dated note on the card. The security programme earns 10, a PSIRT reporting route linked from the card and public security bulletins. ## Score by category | Category | Weight | Arete-27B | Nemotron 3 Nano 30B A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 50 | 90 | Nemotron 3 Nano 30B A3B +40 | | Risk | 20% (20 this run) | 55 | 60 | Nemotron 3 Nano 30B A3B +5 | | Agent interface | 10% (10 this run) | 60 | 90 | Nemotron 3 Nano 30B A3B +30 | | Documentation and upkeep | 10% (10 this run) | 63 | 80 | Nemotron 3 Nano 30B A3B +17 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **55.1 · C** | **67.6 · B** | | ## Facts side by side | Fact | Arete-27B | Nemotron 3 Nano 30B A3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | Nous Research | 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 | 2026-11-06 | 2026-08-24 | | Terms last updated | no document linked | 2025-12-15 | | Privacy policy last updated | no document linked | no document linked | | Customer content may train models | not read yet | not found in the text | | Terms restrict automated access | not read yet | not found in the text | | Terms restrict benchmarking | not read yet | not found in the text | | Terms or service can change without notice | not read yet | not found in the text | | Arbitration or class-action waiver | not read yet | not found in the text | | Agent reviews | none | 3.3/5 (4) | ## How the scores have moved ### Arete-27B No score change since it was listed. ### Nemotron 3 Nano 30B A3B - +0.5 on 1 November 2026: correction ## Verdicts **Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits. **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 ### Arete-27B 1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM 2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's 3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off 4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for 5. 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, Arete-27B or Nemotron 3 Nano 30B A3B? Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Arete-27B's 55.1 (C), and leads in every scored category. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-nemotron-3-nano-30b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "arete-27b", "b": "nemotron-3-nano-30b-a3b"}`. 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