Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Nemotron 3 Nano 30B A3B
Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Granite 4.2 30B's 61 (C), and leads in 2 of 4 scored categories. Granite 4.2 30B leads on 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
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
- Documentation and upkeep, 87 against 80
Watch for
No safety results published for it
Best 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
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 +7
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 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
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 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 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 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 this run | Granite 4.2 30B | Nemotron 3 Nano 30B A3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 90 | even |
| Risk | 20%20 | 55 | 60 | Nemotron 3 Nano 30B A3B +5 |
| Agent interface | 10%10 | 85 | 90 | Nemotron 3 Nano 30B A3B +5 |
| Documentation and upkeep | 10%10 | 87 | 80 | Granite 4.2 30B +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | B 67.6/100 |
Facts side by side
| Fact | Granite 4.2 30B | 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 | 2026-09-04 | 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 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 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 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 30B A3B
- Start it with
ollama run nemotron-3-nano:30b(18 GB MLX on a Mac, 24 GB q4_K_M elsewhere) - 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, thenvllm 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 - NVFP4 needs an NVIDIA Blackwell GPU, and FP8 isn't documented on gfx1151
- Without the qwen3_coder tool parser and the nano_v3 reasoning parser, tool calls and reasoning come back as plain text
- Card sampling is temperature 0.6 and top_p 0.95 for tool calling, temperature 1.0 and top_p 1.0 for reasoning
- Thinking is on by default, so pass enable_thinking as false in chat_template_kwargs for short tasks
- Set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 and raise --max-model-len only when you need more than 262,144 tokens
- In SGLang the main branch names the reasoning parser nemotron_3, not the card's nano_v3
- Filter untrusted tool results before they reach the model, as NVIDIA warns prompt injection can lead to code execution
- 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 30B or Nemotron 3 Nano 30B A3B?
Nemotron 3 Nano 30B A3B scores 67.6 (B) on agent readiness against Granite 4.2 30B's 61 (C), and leads in 2 of 4 scored categories. Granite 4.2 30B leads on documentation and upkeep.
Other comparisons with Granite 4.2 30B or Nemotron 3 Nano 30B A3B
- Agents-A1-4B vs Granite 4.2 30B
- Agents-A1-4B vs Nemotron 3 Nano 30B A3B
- Apertus v1.5 8B vs Granite 4.2 30B
- Apertus v1.5 8B vs Nemotron 3 Nano 30B A3B
- Apodex-1.1-mini vs Granite 4.2 30B
- Apodex-1.1-mini vs Nemotron 3 Nano 30B A3B
- Arete-27B vs Granite 4.2 30B
- Arete-27B vs Nemotron 3 Nano 30B A3B
- BigBang-v1 vs Granite 4.2 30B
- BigBang-v1 vs Nemotron 3 Nano 30B A3B
- Carbon-X vs Granite 4.2 30B
- Carbon-X vs Nemotron 3 Nano 30B A3B
- Cola2 vs Granite 4.2 30B
- Cola2 vs Nemotron 3 Nano 30B A3B
- Cosmos-Reason2-2B vs Granite 4.2 30B
- Cosmos-Reason2-2B vs Nemotron 3 Nano 30B A3B
- Darwin-36B-Opus vs Granite 4.2 30B
- Darwin-36B-Opus vs Nemotron 3 Nano 30B A3B
- DeepSeek-V3.2 vs Granite 4.2 30B
- DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B
- DeepSeek V4.1 Flash vs Granite 4.2 30B
- DeepSeek V4.1 Flash vs Nemotron 3 Nano 30B A3B
- DeepSeek-V4-Flash-0731 vs Granite 4.2 30B
- DeepSeek-V4-Flash-0731 vs Nemotron 3 Nano 30B A3B
- Devstral Small 2 vs Granite 4.2 30B
- Devstral Small 2 vs Nemotron 3 Nano 30B A3B
- DiffusionGemma 26B A4B IT vs Granite 4.2 30B
- DiffusionGemma 26B A4B IT vs Nemotron 3 Nano 30B A3B
- EXAONE 4.5 33B vs Granite 4.2 30B
- EXAONE 4.5 33B vs Nemotron 3 Nano 30B A3B
- Gemma 4 12B IT vs Granite 4.2 30B
- Gemma 4 12B IT vs Nemotron 3 Nano 30B A3B
- Gemma 4 26B A4B IT vs Granite 4.2 30B
- Gemma 4 26B A4B IT vs Nemotron 3 Nano 30B A3B
- Gemma 4 31B IT vs Granite 4.2 30B
- Gemma 4 31B IT vs Nemotron 3 Nano 30B A3B
- Gemma 4 E2B IT vs Granite 4.2 30B
- Gemma 4 E2B IT vs Nemotron 3 Nano 30B A3B
- Gemma 4 E4B IT vs Granite 4.2 30B
- Gemma 4 E4B IT vs Nemotron 3 Nano 30B A3B
- GLM-4.5-Air vs Granite 4.2 30B
- GLM-4.5-Air vs Nemotron 3 Nano 30B A3B
- GLM-4.7-Flash vs Granite 4.2 30B
- GLM-4.7-Flash vs Nemotron 3 Nano 30B A3B
- GLM-5.3-Flash vs Granite 4.2 30B
- GLM-5.3-Flash vs Nemotron 3 Nano 30B A3B
- GLM-5.3 vs Granite 4.2 30B
- GLM-5.3 vs Nemotron 3 Nano 30B A3B
- gpt-oss-120b vs Granite 4.2 30B
- gpt-oss-120b vs Nemotron 3 Nano 30B A3B
- gpt-oss-20b vs Granite 4.2 30B
- gpt-oss-20b vs Nemotron 3 Nano 30B A3B
- Granite 4.2 30B vs Granite 4.2 3B
- Granite 4.2 30B vs Granite 4.2 8B
- Granite 4.2 30B vs Hy-Memory-30B-A3B
- Granite 4.2 30B vs Hy3
- Granite 4.2 30B vs Inkling
- Granite 4.2 30B vs Inkling-Small
- Granite 4.2 30B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Granite 4.2 30B vs Kimi K3
- Granite 4.2 30B vs Kimi K3.1 Pro
- Granite 4.2 30B vs Kolibri-1
- Granite 4.2 30B vs Kronos
- Granite 4.2 30B vs Laguna M.1
- Granite 4.2 30B vs Laguna S 2.1
- Granite 4.2 30B vs Laguna XS 2.1
- Granite 4.2 30B vs LBS-27B
- Granite 4.2 30B vs LFM2.5-2.6B
- Granite 4.2 30B vs LFM3-1.7B
- Granite 4.2 30B vs LFM3-Audio-2B
- Granite 4.2 30B vs LFM3-Omni-2B
- Granite 4.2 30B vs LFM3-VL-2B
- Granite 4.2 30B vs Ling-3.0-tiny
- Granite 4.2 30B vs Ling-3.1-flash-1019
- Granite 4.2 30B vs LongCat-2.5
- Granite 4.2 30B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 30B vs MiMo-V2.6-Flash-RL
- Granite 4.2 30B vs MiniCPM-o 4.5
- Granite 4.2 30B vs MiniCPM-V 4.7 1B
- Granite 4.2 30B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 30B vs MiniCPM5-2B
- Granite 4.2 30B vs MiniMax-M2.5
- Granite 4.2 30B vs MiniMax-M2.7
- Granite 4.2 30B vs MiniMax-M3.1-Flash
- Granite 4.2 30B vs Mistral Large 4
- Granite 4.2 30B vs Muse Glimmer 30B
- Granite 4.2 30B vs Nanbeige4.2-3B
- Granite 4.2 30B vs Nanbeige4.3-33B
- Granite 4.2 30B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Granite 4.2 30B vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 30B vs Nemotron 3 Nano 4B
- Granite 4.2 30B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Granite 4.2 30B vs Nemotron 3 Super 120B A12B
- Granite 4.2 30B vs Nemotron 3 Ultra 550B A55B
- Granite 4.2 30B vs North Mini Code 1.0
- Granite 4.2 30B vs occamy-1.0
- Granite 4.2 30B vs Olmo 3 7B Think
- Granite 4.2 30B vs Ornith-1.5-122B
- Granite 4.2 30B vs Ornith-1.5-35B-A3B
- Granite 4.2 30B vs Ornith-1.5-397B
- Granite 4.2 30B vs Ornith-1.5-9B
- Granite 4.2 30B vs OTel-2.0-LLM-31B-IT
- Granite 4.2 30B vs Qwen-AgentWorld-35B-A3B
- Granite 4.2 30B vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 30B vs Qwen3-4B-Instruct-2507
- Granite 4.2 30B vs Qwen3.5-0.8B
- Granite 4.2 30B vs Qwen3.5-122B-A10B
- Granite 4.2 30B vs Qwen3.5-2B
- Granite 4.2 30B vs Qwen3.5-397B-A17B
- Granite 4.2 30B vs Qwen3.5-4B
- Granite 4.2 30B vs Qwen3.5-9B
- Granite 4.2 30B vs Qwen3.6-27B
- Granite 4.2 30B vs Qwen3.6-35B-A3B
- Granite 4.2 30B vs Qwen3.8-27B
- Granite 4.2 30B vs Qwen3.8-Flash-Next
- Granite 4.2 30B vs Qwen3-Coder-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-Coder-Next
- Granite 4.2 30B vs Qwen3-Omni-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-VL-2B-Instruct
- Granite 4.2 30B vs Qwen3-VL-30B-A3B-Instruct
- Granite 4.2 30B vs Qwen3-VL-32B-Instruct
- Granite 4.2 30B vs Qwen3-VL-4B-Instruct
- Granite 4.2 30B vs Qwen3-VL-8B-Instruct
- Granite 4.2 30B vs Qwopus3.8-27B-Flash-V2
- Granite 4.2 30B vs Recursive Flint
- Granite 4.2 30B vs Ripple-65B Preview
- Granite 4.2 30B vs SenseNova-Loop-9B-Preview
- Granite 4.2 30B vs SmolLM3-3B
- Granite 4.2 30B vs Spark-X2.6-4B
- Granite 4.2 30B vs Step 3.7 Flash
- Granite 4.2 30B vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 30B vs Swift 1.5 Qwen3.8-Flash-Next
- Granite 4.2 30B vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 30B vs Baidu Unlimited-OCR-2
- Granite 4.2 30B vs WeVisOne-VL-2B
- Granite 4.2 30B vs WeVisOne-VL-4B
- Granite 4.2 30B vs ZAYA1-8B
- Granite 4.2 3B vs Nemotron 3 Nano 30B A3B
- Granite 4.2 8B vs Nemotron 3 Nano 30B A3B
- Hy-Memory-30B-A3B vs Nemotron 3 Nano 30B A3B
- Hy3 vs Nemotron 3 Nano 30B A3B
- Inkling-Small vs Nemotron 3 Nano 30B A3B
- Inkling vs Nemotron 3 Nano 30B A3B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Nemotron 3 Nano 30B A3B
- Kimi K3.1 Pro vs Nemotron 3 Nano 30B A3B
- Kimi K3 vs Nemotron 3 Nano 30B A3B
- Kolibri-1 vs Nemotron 3 Nano 30B A3B
- Kronos vs Nemotron 3 Nano 30B A3B
- Laguna M.1 vs Nemotron 3 Nano 30B A3B
- Laguna S 2.1 vs Nemotron 3 Nano 30B A3B
- Laguna XS 2.1 vs Nemotron 3 Nano 30B A3B
- LBS-27B vs Nemotron 3 Nano 30B A3B
- LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B
- LFM3-1.7B vs Nemotron 3 Nano 30B A3B
- LFM3-Audio-2B vs Nemotron 3 Nano 30B A3B
- LFM3-Omni-2B vs Nemotron 3 Nano 30B A3B
- LFM3-VL-2B vs Nemotron 3 Nano 30B A3B
- Ling-3.0-tiny vs Nemotron 3 Nano 30B A3B
- Ling-3.1-flash-1019 vs Nemotron 3 Nano 30B A3B
- LongCat-2.5 vs Nemotron 3 Nano 30B A3B
- MiMo-V2.6 Distill Qwen 9B vs Nemotron 3 Nano 30B A3B
- MiMo-V2.6-Flash-RL vs Nemotron 3 Nano 30B A3B
- MiniCPM-o 4.5 vs Nemotron 3 Nano 30B A3B
- MiniCPM-V 4.7 1B vs Nemotron 3 Nano 30B A3B
- MiniCPM-V 4.7 35B-A3B vs Nemotron 3 Nano 30B A3B
- MiniCPM5-2B vs Nemotron 3 Nano 30B A3B
- MiniMax-M2.5 vs Nemotron 3 Nano 30B A3B
- MiniMax-M2.7 vs Nemotron 3 Nano 30B A3B
- MiniMax-M3.1-Flash vs Nemotron 3 Nano 30B A3B
- Mistral Large 4 vs Nemotron 3 Nano 30B A3B
- Muse Glimmer 30B vs Nemotron 3 Nano 30B A3B
- Nanbeige4.2-3B vs Nemotron 3 Nano 30B A3B
- Nanbeige4.3-33B vs Nemotron 3 Nano 30B A3B
- NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Nemotron 3 Nano 30B A3B
- Nemotron 3.5 Lightning 30B A3B vs Nemotron 3 Nano 30B A3B
- Nemotron 3 Nano 30B A3B vs Nemotron 3 Nano 4B
- Nemotron 3 Nano 30B A3B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Nemotron 3 Nano 30B A3B vs Nemotron 3 Super 120B A12B
- Nemotron 3 Nano 30B A3B vs Nemotron 3 Ultra 550B A55B
- Nemotron 3 Nano 30B A3B vs North Mini Code 1.0
- Nemotron 3 Nano 30B A3B vs occamy-1.0
- Nemotron 3 Nano 30B A3B vs Olmo 3 7B Think
- Nemotron 3 Nano 30B A3B vs Ornith-1.5-122B
- Nemotron 3 Nano 30B A3B vs Ornith-1.5-35B-A3B
- Nemotron 3 Nano 30B A3B vs Ornith-1.5-397B
- Nemotron 3 Nano 30B A3B vs Ornith-1.5-9B
- Nemotron 3 Nano 30B A3B vs OTel-2.0-LLM-31B-IT
- Nemotron 3 Nano 30B A3B vs Qwen-AgentWorld-35B-A3B
- Nemotron 3 Nano 30B A3B vs Qwen3-30B-A3B-Instruct-2507
- Nemotron 3 Nano 30B A3B vs Qwen3-4B-Instruct-2507
- Nemotron 3 Nano 30B A3B vs Qwen3.5-0.8B
- Nemotron 3 Nano 30B A3B vs Qwen3.5-122B-A10B
- Nemotron 3 Nano 30B A3B vs Qwen3.5-2B
- Nemotron 3 Nano 30B A3B vs Qwen3.5-397B-A17B
- Nemotron 3 Nano 30B A3B vs Qwen3.5-4B
- Nemotron 3 Nano 30B A3B vs Qwen3.5-9B
- Nemotron 3 Nano 30B A3B vs Qwen3.6-27B
- Nemotron 3 Nano 30B A3B vs Qwen3.6-35B-A3B
- Nemotron 3 Nano 30B A3B vs Qwen3.8-27B
- Nemotron 3 Nano 30B A3B vs Qwen3.8-Flash-Next
- Nemotron 3 Nano 30B A3B vs Qwen3-Coder-30B-A3B-Instruct
- Nemotron 3 Nano 30B A3B vs Qwen3-Coder-Next
- Nemotron 3 Nano 30B A3B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3 Nano 30B A3B vs Qwen3-VL-2B-Instruct
- Nemotron 3 Nano 30B A3B vs Qwen3-VL-30B-A3B-Instruct
- Nemotron 3 Nano 30B A3B vs Qwen3-VL-32B-Instruct
- Nemotron 3 Nano 30B A3B vs Qwen3-VL-4B-Instruct
- Nemotron 3 Nano 30B A3B vs Qwen3-VL-8B-Instruct
- Nemotron 3 Nano 30B A3B vs Qwopus3.8-27B-Flash-V2
- Nemotron 3 Nano 30B A3B vs Recursive Flint
- Nemotron 3 Nano 30B A3B vs Ripple-65B Preview
- Nemotron 3 Nano 30B A3B vs SenseNova-Loop-9B-Preview
- Nemotron 3 Nano 30B A3B vs SmolLM3-3B
- Nemotron 3 Nano 30B A3B vs Spark-X2.6-4B
- Nemotron 3 Nano 30B A3B vs Step 3.7 Flash
- Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-27B
- Nemotron 3 Nano 30B A3B vs Swift 1.5 Qwen3.8-Flash-Next
- Nemotron 3 Nano 30B A3B vs ThinkingCap (Qwen3.8 27B)
- Nemotron 3 Nano 30B A3B vs Baidu Unlimited-OCR-2
- Nemotron 3 Nano 30B A3B vs WeVisOne-VL-2B
- Nemotron 3 Nano 30B A3B vs WeVisOne-VL-4B
- Nemotron 3 Nano 30B A3B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/granite-4-2-30b-vs-nemotron-3-nano-30b-a3b.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-30b-a3b.json - From a terminal
anchor compare granite-4-2-30b nemotron-3-nano-30b-a3b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "nemotron-3-nano-30b-a3b"}at/mcp, no key