Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Nemotron 3 Nano Omni 30B A3B Reasoning
Nemotron 3 Nano Omni 30B A3B Reasoning and Granite 4.2 30B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Granite 4.2 30B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
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Which one, for what
Best for Agents that want a documented, signed, Apache 2.0 reasoning model with tool calling on a 24 GB card
Ahead on
- Reach, 90 against 85
- Documentation and upkeep, 87 against 70
Watch for
No safety results published for it
Nemotron 3 Nano Omni 30B A3B Reasoning C
Best 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 85
Watch for
Loads with trust_remote_code in every documented runtime command
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Documentation and upkeep Granite 4.2 30B +17
Granite 4.2 30B 87
Model card 17, Intended use (best for), limitations (ethical considerations and limitations), architecture, parameters (30B), context length (128K native) and 12 tested languages are on the card. Training data 20, IBM's disclosure file for the 30B lists the datasets used in pre-training and post-training as machine-readable metadata, and the card describes the three training stages. Last change 15, the last commit to the repository was on 4 September 2026, 67 days before this check. Responsiveness 20, Of the 4 discussions that aren't pull requests (28 August to 3 November 2026), 3 have a reply from an ibm-granite member. Version policy 5, Granite 4.1 30B 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 + 15 + 20 + 5 + 10 = 87.
Nemotron 3 Nano Omni 30B A3B Reasoning 70
The card covers all seven fields, reading the 2019 to 2025 data collection period as the cut-off (20). Training data is listed with sources and sizes by dataset and one set is published (20). The Hub files last changed on 24 and 25 August 2026, and NVIDIA's TensorRT-LLM, which the card names for serving, changed the Nemotron-H model code on 14 October 2026, 20 days back (20). None of the 11 questions across the three repositories has a reply (0). It's a first release with no version policy stated (5). The shipped Jinja template and the tokenizer config's template differ, and reports about the template and a modeling file wait unanswered (5). 20 + 20 + 20 + 0 + 5 + 5 = 70.
Reach Granite 4.2 30B +5
Granite 4.2 30B 90
Memory 25, IBM's own 4-bit MLX build granite-4.2-30b-q4-mlx is 16.47 GB, and 16.47 GB × 1.2 = 19.8 GB, which fits 24 GB but not 16 GB. Its Q4_0 GGUF is 16.58 GB and Ollama's library build granite4.2:30b 18 GB, the same band. 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, 30b 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. 25 + 25 + 15 + 15 + 10 + 0 = 90.
Nemotron 3 Nano Omni 30B A3B Reasoning 85
Memory earns 20 because the smallest 4-bit builds, Ollama's NVFP4 at 21 GB and NVIDIA's NVFP4 at 22.4 GB, need about 25 to 27 GB with the fifth added, which fits 32 GB but not 24. Runtimes reach the cap of 25 with released code in vLLM, SGLang and llama.cpp and library entries in Ollama and LM Studio. NVIDIA's FP8 and NVFP4 builds give builds the full 15. The NVIDIA Open Model Agreement is a custom licence with free commercial use, so terms get 10. The ungated repositories give download the full 10, and NVIDIA's own API serving it gives hosted too its 5.
Risk Nemotron 3 Nano Omni 30B A3B Reasoning +5
Granite 4.2 30B 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 Omni 30B A3B Reasoning 60
File safety gets 15, safetensors only but loaded with trust_remote_code. Provenance gets 15 for files in NVIDIA's own organisation with a SHA-256 for every shard. The agreement has no use policy, threshold or change clause and the grant is irrevocable, so licence risk keeps 15. NVIDIA's PSIRT form on the card and its public bulletins give the security programme 10. Agent guidance gets 5 for general cautions, and safety evaluation scores 0 because the subcards name the safety tests without publishing results.
Agent interface Nemotron 3 Nano Omni 30B A3B Reasoning +5
Granite 4.2 30B 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 Omni 30B A3B Reasoning 90
A Jinja template ships in the repository (20). The card names the qwen3_coder parser and NVIDIA's cookbooks show tools declared with the OpenAI schema and tool_calls read back (25). The template renders several calls per turn and the card says it supports tool calling (20). JSON output is claimed with no word on how to ask for it (5). Thinking turns off per request, a budget caps it and the parser returns the reasoning apart (10). The context is 262,144 tokens, trained in (10).
Score by category
| Category | Weight this run | Granite 4.2 30B | Nemotron 3 Nano Omni 30B A3B Reasoning | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 85 | Granite 4.2 30B +5 |
| Risk | 20%20 | 55 | 60 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 |
| Agent interface | 10%10 | 85 | 90 | Nemotron 3 Nano Omni 30B A3B Reasoning +5 |
| Documentation and upkeep | 10%10 | 87 | 70 | Granite 4.2 30B +17 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61.2/100 |
Facts side by side
| Fact | Granite 4.2 30B | Nemotron 3 Nano Omni 30B A3B Reasoning |
|---|---|---|
| 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 Open Model Agreement (custom, commercial use allowed, not an OSI licence) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-04 | 2026-08-24 |
| Terms last updated | no document linked | no date given |
| 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 | 3/5 (4) |
How the scores have moved
Granite 4.2 30B
No score change since it was listed.
Verdicts
Granite 4.2 30B
IBM's largest reasoning cousin, with the same strengths as the 3B. It runs in every runtime we count from IBM's own builds on a 24 GB card, 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 Omni 30B A3B Reasoning
A local multimodal model 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
Granite 4.2 30B
- Start it with
ollama run granite4.2:30b, orllama-server -hf ibm-granite/granite-4.2-30b-GGUF:Q4_K_M --jinja - 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 - Thinking is on by default. Pass
enable_thinkingfalse to turn it off orlow_efforttrue for short reasoning - The context is 131,072 tokens. The card's 512K extension isn't documented
- Validate JSON output yourself, since structured output isn't documented
- IBM recommends Granite Guardian beside it to flag risky inputs and outputs
Nemotron 3 Nano Omni 30B A3B Reasoning
- 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 fp8on vLLM 0.20.0 or later, afterpip install "vllm[audio]"if any audio will be sent - 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 - Set
--media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}'at launch, since vLLM otherwise samples about 32 frames per video - 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 - Render PDF pages to PNG and send them as images, since the API takes no raw PDF
- Treat transcripts and text read from images or video as untrusted input before acting on them
Questions
Which is better for AI agents, Granite 4.2 30B or Nemotron 3 Nano Omni 30B A3B Reasoning?
Nemotron 3 Nano Omni 30B A3B Reasoning and Granite 4.2 30B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). Granite 4.2 30B leads on reach and documentation and upkeep.
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- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-4B-Instruct
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-VL-8B-Instruct
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwopus3.8-27B-Flash-V2
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Recursive Flint
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Ripple-65B Preview
- Nemotron 3 Nano Omni 30B A3B Reasoning vs SenseNova-Loop-9B-Preview
- Nemotron 3 Nano Omni 30B A3B Reasoning vs SmolLM3-3B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Spark-X2.6-4B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Step 3.7 Flash
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Swift 1.5 Qwen3.8-Flash-Next
- Nemotron 3 Nano Omni 30B A3B Reasoning vs ThinkingCap (Qwen3.8 27B)
- Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-2B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs WeVisOne-VL-4B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs ZAYA1-8B
- Cohere Transcribe 03-2026 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Granite Speech 5.0 2B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Ming-3.0-asr-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Ming-3.0-audio-flash vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Ming-3.0-audio-tiny vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Nemotron 3.5 ASR Streaming 0.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Nemotron 3.5 Transcribe 30B-A4B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Parakeet TDT 0.6B v3
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Parakeet Unified EN 0.6B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-0.6B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-ASR-1.7B
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Underdog Bark 3.0
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Voxtral Mini 4B Realtime 2602
Machine-readable
- This page as Markdown
/compare/granite-4-2-30b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/granite-4-2-30b.json·/api/v1/tools/nemotron-3-nano-omni-30b-a3b-reasoning.json - From a terminal
anchor compare granite-4-2-30b nemotron-3-nano-omni-30b-a3b-reasoning(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "nemotron-3-nano-omni-30b-a3b-reasoning"}at/mcp, no key