# Granite 4.2 8B vs Nemotron 3 Nano 30B A3B > Nemotron 3 Nano 30B A3B scores 67.6 (B) to Granite 4.2 8B'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/granite-4-2-8b-vs-nemotron-3-nano-30b-a3b - Markdown: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-30b-a3b.md (~10,600 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 Granite 4.2 8B's 61 (C), and leads in 2 of 4 scored categories. Granite 4.2 8B leads on reach and documentation and upkeep. Both do open-weight models you run yourself. - Granite 4.2 8B: grade C, 61/100, rank #66 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-8b.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 ### Granite 4.2 8B (C) Good for: Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 16 GB machine Ahead on: - Reach, 95 against 90 - Documentation and upkeep, 92 against 80 Watch for: No safety results published for it ### 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 85 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 ### Documentation and upkeep: Granite 4.2 8B +12 - Granite 4.2 8B (92): Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (8B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 8B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 20, the last commit, on 20 October 2026, replaced model.safetensors.index.json (titled "granite-4.2 index file fix"), 21 days before this check. Responsiveness 20, Of the 5 discussions that aren't pull requests (13 September to 5 November 2026), 4 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 8B stays in its own repository and 4.2 came out in new ones. File health 10, Config, template and card agree on the architecture, the 131,072-token context and the thinking switch. 17 + 20 + 20 + 20 + 5 + 10 = 92. - 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. ### Reach: Granite 4.2 8B +5 - Granite 4.2 8B (95): Memory 30, IBM's own 4-bit MLX build granite-4.2-8b-q4-mlx is 4.95 GB, and 4.95 GB × 1.2 = 5.9 GB, within 8 GB. Its Q4_0 GGUF is 5.06 GB and Ollama's library build granite4.2:8b (5.3 GB) fits 8 GB too. Runtimes 25, Released versions run the granite architecture in llama.cpp (granite, b12242), vLLM (GraniteForCausalLM in the v0.33.0 registry), SGLang (granite.py at v0.5.23), Transformers (v5.22.2) and Ollama (its own granite4.2 library entry, 8b tag). Builds 15, IBM publishes FP8, MXFP4 and NVFP4 safetensors builds, a GGUF repository with Q4_0 to Q8_0 and BF16 files, and 4-bit, 8-bit and BF16 MLX builds, so quantised builds at two precisions or more. Terms 15, Apache 2.0, an OSI-approved licence, with no use policy or other terms added on the card. Download 10, the Hub API reports gated false for the BF16, FP8, MXFP4, NVFP4, GGUF and MLX repositories, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps one inference provider (DeepInfra, live), short of the two the line needs. 30 + 25 + 15 + 15 + 10 + 0 = 95. - 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. ### Risk: Nemotron 3 Nano 30B A3B +5 - Granite 4.2 8B (55): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 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. ### Agent interface: Nemotron 3 Nano 30B A3B +5 - Granite 4.2 8B (85): Chat template 20, chat_template.jinja in the repository, with roles, tools, tool responses, the thinking switch and low effort. Tool-calling format 25, the card has tool-calling sections for vLLM and SGLang, declares tools with OpenAI's function schema and names the parser to use, qwen3_coder, which both runtimes ship in the released versions read. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so it renders several calls in one turn, and the card names tool calling and agentic workflows as what it's best for. Structured output 0, the card doesn't document JSON or JSON Schema output or how to ask for it. Reasoning control 10, the card documents thinking on by default, non-thinking and low-effort modes per request through the template (enable_thinking and low_effort), with the reasoning returned apart from the answer. Context 10, 131,072 tokens natively per the card and config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85. - 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. ## Score by category | Category | Weight | Granite 4.2 8B | Nemotron 3 Nano 30B A3B | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 95 | 90 | Granite 4.2 8B +5 | | Risk | 20% (20 this run) | 55 | 60 | Nemotron 3 Nano 30B A3B +5 | | Agent interface | 10% (10 this run) | 85 | 90 | Nemotron 3 Nano 30B A3B +5 | | Documentation and upkeep | 10% (10 this run) | 92 | 80 | Granite 4.2 8B +12 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **61 · C** | **67.6 · B** | | ## Facts side by side | Fact | Granite 4.2 8B | Nemotron 3 Nano 30B A3B | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | IBM | 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 | 2026-10-20 | 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 ### Granite 4.2 8B No score change since it was listed. ### Nemotron 3 Nano 30B A3B - +0.5 on 1 November 2026: correction ## Verdicts **Granite 4.2 8B.** IBM's mid-size reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 16 GB machine, is signed, plain Apache 2.0, and comes with a disclosure of its training data. No safety results are published and structured output isn't documented. **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 ### Granite 4.2 8B 1. Start it with `ollama run granite4.2:8b`, or `llama-server -hf ibm-granite/granite-4.2-8b-GGUF:Q4_K_M --jinja` 2. With vLLM 0.32.0 or later leave out `--reasoning-parser-plugin`, since the parser is built in, and use `--tool-call-parser qwen3_coder` 3. Thinking is on by default. Pass `enable_thinking` false to turn it off or `low_effort` true for short reasoning 4. The context is 131,072 tokens. The card's 512K extension isn't documented 5. Validate JSON output yourself, since structured output isn't documented 6. IBM recommends Granite Guardian beside it to flag risky inputs and outputs ### 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, Granite 4.2 8B or Nemotron 3 Nano 30B A3B? Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Granite 4.2 8B's 61 (C), and leads in 2 of 4 scored categories. Granite 4.2 8B leads on reach and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-30b-a3b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-8b-vs-nemotron-3-nano-30b-a3b.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "granite-4-2-8b", "b": "nemotron-3-nano-30b-a3b"}`. 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