Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Granite 4.2 3B
Granite 4.2 30B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 0 of 4 scored categories. Granite 4.2 3B 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 4969 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
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No safety results published for it
Best for Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off
Ahead on
- Reach, 95 against 90
- Documentation and upkeep, 92 against 87
Watch for
No safety results published for 4.2, and only general safety cautions
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.
Reach Granite 4.2 3B +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.
Granite 4.2 3B 95
Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added. Five named runtimes run the granite architecture in released versions or their own libraries, so runtimes take the cap of 25, though LM Studio has no 4.2 entry. IBM's FP8, FP4, GGUF and MLX builds earn 15. Apache 2.0 with nothing added gives terms 15 and the ungated repositories download 10. Hosted too is 0, since only DeepInfra serves it and watsonx.ai stops at 4.1.
Documentation and upkeep Granite 4.2 3B +5
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.
Granite 4.2 3B 92
The card covers everything but a knowledge cut-off (17). IBM's disclosure lists the datasets with sources and sizes (20). The index file changed on 20 October 2026, 15 days before this check, and nothing in IBM's 4.2 repositories or the GitHub repository changed since (20). The two questions asked on IBM's 3B repositories have an IBM reply, and no new thread was opened after 21 October (20). Granite 4.1 3B stays in its own repository with no note (5), and the files agree with each other (10).
Score by category
| Category | Weight this run | Granite 4.2 30B | Granite 4.2 3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 95 | Granite 4.2 3B +5 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 87 | 92 | Granite 4.2 3B +5 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 59.8/100 |
Facts side by side
| Fact | Granite 4.2 30B | Granite 4.2 3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | IBM |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-04 | 2026-10-20 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3.8/5 (4) |
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.
Granite 4.2 3B
A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.
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
Granite 4.2 3B
- Start it with
ollama run granite4.2:3b; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072 - With vLLM 0.32.0 or later, serve
ibm-granite/granite-4.2-3bwith--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice; that release has the parser built in, so the card's--reasoning-parser-plugin ./granite_thinking_parser.pyisn't needed - With SGLang 0.5.18 or later,
--reasoning-parser auto --tool-call-parser autoresolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content - Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires
- Pass
enable_thinkingfalse for direct answers orlow_efforttrue for short reasoning, through chat_template_kwargs on an OpenAI-compatible server - Earlier turns' thinking is stripped by default (
truncate_history_thinking), so pass it false to keep reasoning in multi-turn history - For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2
- Verify the files with
python -m model_signing verify sigstoreand IBM's identity, per IBM's signature guide
Questions
Which is better for AI agents, Granite 4.2 30B or Granite 4.2 3B?
Granite 4.2 30B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 0 of 4 scored categories. Granite 4.2 3B leads on reach and documentation and upkeep.
Other comparisons with Granite 4.2 30B or Granite 4.2 3B
- Agents-A1-4B vs Granite 4.2 30B
- Agents-A1-4B vs Granite 4.2 3B
- Apertus v1.5 8B vs Granite 4.2 30B
- Apertus v1.5 8B vs Granite 4.2 3B
- Apodex-1.1-mini vs Granite 4.2 30B
- Apodex-1.1-mini vs Granite 4.2 3B
- Arete-27B vs Granite 4.2 30B
- Arete-27B vs Granite 4.2 3B
- BigBang-v1 vs Granite 4.2 30B
- BigBang-v1 vs Granite 4.2 3B
- Carbon-X vs Granite 4.2 30B
- Carbon-X vs Granite 4.2 3B
- Cosmos-Reason2-2B vs Granite 4.2 30B
- Cosmos-Reason2-2B vs Granite 4.2 3B
- Darwin-36B-Opus vs Granite 4.2 30B
- Darwin-36B-Opus vs Granite 4.2 3B
- DeepSeek-V3.2 vs Granite 4.2 30B
- DeepSeek-V3.2 vs Granite 4.2 3B
- DeepSeek V4.1 Flash vs Granite 4.2 30B
- DeepSeek V4.1 Flash vs Granite 4.2 3B
- DeepSeek-V4-Flash-0731 vs Granite 4.2 30B
- DeepSeek-V4-Flash-0731 vs Granite 4.2 3B
- DiffusionGemma 26B A4B IT vs Granite 4.2 30B
- DiffusionGemma 26B A4B IT vs Granite 4.2 3B
- Gemma 4 12B IT vs Granite 4.2 30B
- Gemma 4 12B IT vs Granite 4.2 3B
- Gemma 4 26B A4B IT vs Granite 4.2 30B
- Gemma 4 26B A4B IT vs Granite 4.2 3B
- Gemma 4 31B IT vs Granite 4.2 30B
- Gemma 4 31B IT vs Granite 4.2 3B
- Gemma 4 E2B IT vs Granite 4.2 30B
- Gemma 4 E2B IT vs Granite 4.2 3B
- Gemma 4 E4B IT vs Granite 4.2 30B
- Gemma 4 E4B IT vs Granite 4.2 3B
- GLM-4.5-Air vs Granite 4.2 30B
- GLM-4.5-Air vs Granite 4.2 3B
- GLM-4.7-Flash vs Granite 4.2 30B
- GLM-4.7-Flash vs Granite 4.2 3B
- GLM-5.3-Flash vs Granite 4.2 30B
- GLM-5.3-Flash vs Granite 4.2 3B
- GLM-5.3 vs Granite 4.2 30B
- GLM-5.3 vs Granite 4.2 3B
- gpt-oss-120b vs Granite 4.2 30B
- gpt-oss-120b vs Granite 4.2 3B
- gpt-oss-20b vs Granite 4.2 30B
- gpt-oss-20b 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 MiMo-V2.6 Distill Qwen 9B
- 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 Nanbeige4.3-33B
- 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 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 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 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 SmolLM3-3B
- Granite 4.2 30B vs Spark-X2.6-4B
- 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 3B vs Granite 4.2 8B
- Granite 4.2 3B vs Hy-Memory-30B-A3B
- Granite 4.2 3B vs Hy3
- Granite 4.2 3B vs Inkling
- Granite 4.2 3B vs Inkling-Small
- Granite 4.2 3B vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- Granite 4.2 3B vs Kimi K3
- Granite 4.2 3B vs Kimi K3.1 Pro
- Granite 4.2 3B vs Kolibri-1
- Granite 4.2 3B vs Kronos
- Granite 4.2 3B vs Laguna M.1
- Granite 4.2 3B vs Laguna S 2.1
- Granite 4.2 3B vs Laguna XS 2.1
- Granite 4.2 3B vs LBS-27B
- Granite 4.2 3B vs LFM2.5-2.6B
- Granite 4.2 3B vs LFM3-1.7B
- Granite 4.2 3B vs LFM3-Audio-2B
- Granite 4.2 3B vs LFM3-Omni-2B
- Granite 4.2 3B vs LFM3-VL-2B
- Granite 4.2 3B vs Ling-3.0-tiny
- Granite 4.2 3B vs Ling-3.1-flash-1019
- Granite 4.2 3B vs MiMo-V2.6 Distill Qwen 9B
- Granite 4.2 3B vs MiniCPM-o 4.5
- Granite 4.2 3B vs MiniCPM-V 4.7 1B
- Granite 4.2 3B vs MiniCPM-V 4.7 35B-A3B
- Granite 4.2 3B vs MiniCPM5-2B
- Granite 4.2 3B vs MiniMax-M2.5
- Granite 4.2 3B vs MiniMax-M2.7
- Granite 4.2 3B vs MiniMax-M3.1-Flash
- Granite 4.2 3B vs Mistral Large 4
- Granite 4.2 3B vs Muse Glimmer 30B
- Granite 4.2 3B vs Nanbeige4.3-33B
- Granite 4.2 3B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Granite 4.2 3B vs Nemotron 3.5 Lightning 30B A3B
- Granite 4.2 3B vs Nemotron 3 Nano 30B A3B
- Granite 4.2 3B vs Nemotron 3 Nano 4B
- Granite 4.2 3B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Granite 4.2 3B vs Nemotron 3 Super 120B A12B
- Granite 4.2 3B vs Nemotron 3 Ultra 550B A55B
- Granite 4.2 3B vs occamy-1.0
- Granite 4.2 3B vs Olmo 3 7B Think
- Granite 4.2 3B vs Ornith-1.5-122B
- Granite 4.2 3B vs Ornith-1.5-35B-A3B
- Granite 4.2 3B vs Ornith-1.5-397B
- Granite 4.2 3B vs Ornith-1.5-9B
- Granite 4.2 3B vs Qwen-AgentWorld-35B-A3B
- Granite 4.2 3B vs Qwen3-30B-A3B-Instruct-2507
- Granite 4.2 3B vs Qwen3-4B-Instruct-2507
- Granite 4.2 3B vs Qwen3.5-0.8B
- Granite 4.2 3B vs Qwen3.5-122B-A10B
- Granite 4.2 3B vs Qwen3.5-2B
- Granite 4.2 3B vs Qwen3.5-397B-A17B
- Granite 4.2 3B vs Qwen3.5-4B
- Granite 4.2 3B vs Qwen3.5-9B
- Granite 4.2 3B vs Qwen3.6-27B
- Granite 4.2 3B vs Qwen3.6-35B-A3B
- Granite 4.2 3B vs Qwen3.8-27B
- Granite 4.2 3B vs Qwen3.8-Flash-Next
- Granite 4.2 3B vs Qwen3-Coder-30B-A3B-Instruct
- Granite 4.2 3B vs Qwen3-Coder-Next
- Granite 4.2 3B vs Qwen3-Omni-30B-A3B-Instruct
- Granite 4.2 3B vs Qwen3-VL-2B-Instruct
- Granite 4.2 3B vs Qwen3-VL-30B-A3B-Instruct
- Granite 4.2 3B vs Qwen3-VL-32B-Instruct
- Granite 4.2 3B vs Qwen3-VL-4B-Instruct
- Granite 4.2 3B vs Qwen3-VL-8B-Instruct
- Granite 4.2 3B vs Qwopus3.8-27B-Flash-V2
- Granite 4.2 3B vs Recursive Flint
- Granite 4.2 3B vs Ripple-65B Preview
- Granite 4.2 3B vs SmolLM3-3B
- Granite 4.2 3B vs Spark-X2.6-4B
- Granite 4.2 3B vs Swift 1.5 Qwen3.8-27B
- Granite 4.2 3B vs Swift 1.5 Qwen3.8-Flash-Next
- Granite 4.2 3B vs ThinkingCap (Qwen3.8 27B)
- Granite 4.2 3B vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/granite-4-2-30b-vs-granite-4-2-3b.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/granite-4-2-3b.json - From a terminal
anchor compare granite-4-2-30b granite-4-2-3b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "granite-4-2-3b"}at/mcp, no key