Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Laguna M.1
Granite 4.2 30B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 2 of 4 scored categories. 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
Ahead on
- Reach, 90 against 70
- Documentation and upkeep, 87 against 36
Watch for
No safety results published for it
Best for Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
The smallest build needs about 167 GB, more than a 128 GB machine
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 +51
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.
Laguna M.1 36
Model card 11 of 20, intended use, architecture, parameters and context are there, limitations, languages and a cut-off aren't. Training data 5, the report describes sources and filtering but gives no cut-off. Last change 10, 14 July 2026, 113 days ago. Responsiveness 0, the one discussion's only reply is from someone outside the poolside organisation. Version policy 5, a first open release with nothing replaced. File health 5, generation config and template disagree on the thinking default and the FP8 build's Docker files name different image tags.
Reach Granite 4.2 30B +20
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.
Laguna M.1 70
Memory 5 of 30, the smallest 4-bit build is Poolside's 139.3 GB NVFP4, which needs 192 GB with a fifth added, beyond our reference machine. Runtimes 25, released llama.cpp, Ollama (through the llama.cpp release it pins), vLLM, SGLang and Transformers run the laguna architecture. Builds 15, FP8 and NVFP4 from Poolside. Terms 15, Apache 2.0 with nothing binding added. Download 10, nothing is gated. Hosted 0, no provider is mapped on the Hub and Poolside's API docs name only S 2.1.
Score by category
| Category | Weight this run | Granite 4.2 30B | Laguna M.1 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 70 | Granite 4.2 30B +20 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 87 | 36 | Granite 4.2 30B +51 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.7/100 |
Facts side by side
| Fact | Granite 4.2 30B | Laguna M.1 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | Poolside |
| 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 | 2026-07-14 |
| Terms last updated | no document linked | 2026-07-21 |
| Privacy policy last updated | no document linked | 2026-07-21 |
| Customer content may train models | not read yet | yes, with an opt-out |
| 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 | yes |
| Arbitration or class-action waiver | not read yet | yes |
| Agent reviews | none | 2.5/5 (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.
Laguna M.1
A large open-weight coding and agent model under plain Apache 2.0, for teams with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine. Released llama.cpp, Ollama, vLLM, SGLang and Transformers code runs the architecture. The files haven't changed since 14 July 2026, no hosted endpoint was found, and no safety results are published.
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
Laguna M.1
- Run the NVFP4 build on Blackwell GPUs with
vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice, on vLLM 0.21.0 or later - Thinking is off in the template unless you pass
enable_thinkingtrue or start the server with--default-chat-template-kwargs '{"enable_thinking": true}', which Poolside recommends for agentic coding - Pass earlier reasoning back as
reasoning_contentin the history, since the card says reasoning may stop in later steps if it's dropped - Sampling per the card is temperature 1.0 and top_k 20
- The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM
- It doesn't fit our 128 GB reference machine at any published precision
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
Questions
Which is better for AI agents, Granite 4.2 30B or Laguna M.1?
Granite 4.2 30B scores 61 (C) on agent readiness against Laguna M.1's 56.7 (C), and leads in 2 of 4 scored categories.
Other comparisons with Granite 4.2 30B or Laguna M.1
- Agents-A1-4B vs Granite 4.2 30B
- Agents-A1-4B vs Laguna M.1
- Apertus v1.5 8B vs Granite 4.2 30B
- Apertus v1.5 8B vs Laguna M.1
- Apodex-1.1-mini vs Granite 4.2 30B
- Apodex-1.1-mini vs Laguna M.1
- Arete-27B vs Granite 4.2 30B
- Arete-27B vs Laguna M.1
- BigBang-v1 vs Granite 4.2 30B
- BigBang-v1 vs Laguna M.1
- Carbon-X vs Granite 4.2 30B
- Carbon-X vs Laguna M.1
- Cosmos-Reason2-2B vs Granite 4.2 30B
- Cosmos-Reason2-2B vs Laguna M.1
- Darwin-36B-Opus vs Granite 4.2 30B
- Darwin-36B-Opus vs Laguna M.1
- DeepSeek-V3.2 vs Granite 4.2 30B
- DeepSeek-V3.2 vs Laguna M.1
- DeepSeek V4.1 Flash vs Granite 4.2 30B
- DeepSeek V4.1 Flash vs Laguna M.1
- DeepSeek-V4-Flash-0731 vs Granite 4.2 30B
- DeepSeek-V4-Flash-0731 vs Laguna M.1
- DiffusionGemma 26B A4B IT vs Granite 4.2 30B
- DiffusionGemma 26B A4B IT vs Laguna M.1
- Gemma 4 12B IT vs Granite 4.2 30B
- Gemma 4 12B IT vs Laguna M.1
- Gemma 4 26B A4B IT vs Granite 4.2 30B
- Gemma 4 26B A4B IT vs Laguna M.1
- Gemma 4 31B IT vs Granite 4.2 30B
- Gemma 4 31B IT vs Laguna M.1
- Gemma 4 E2B IT vs Granite 4.2 30B
- Gemma 4 E2B IT vs Laguna M.1
- Gemma 4 E4B IT vs Granite 4.2 30B
- Gemma 4 E4B IT vs Laguna M.1
- GLM-4.5-Air vs Granite 4.2 30B
- GLM-4.5-Air vs Laguna M.1
- GLM-4.7-Flash vs Granite 4.2 30B
- GLM-4.7-Flash vs Laguna M.1
- GLM-5.3-Flash vs Granite 4.2 30B
- GLM-5.3-Flash vs Laguna M.1
- GLM-5.3 vs Granite 4.2 30B
- GLM-5.3 vs Laguna M.1
- gpt-oss-120b vs Granite 4.2 30B
- gpt-oss-120b vs Laguna M.1
- gpt-oss-20b vs Granite 4.2 30B
- gpt-oss-20b vs Laguna M.1
- 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 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 Laguna M.1
- Granite 4.2 8B vs Laguna M.1
- Hy-Memory-30B-A3B vs Laguna M.1
- Hy3 vs Laguna M.1
- Inkling-Small vs Laguna M.1
- Inkling vs Laguna M.1
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Laguna M.1
- Kimi K3.1 Pro vs Laguna M.1
- Kimi K3 vs Laguna M.1
- Kolibri-1 vs Laguna M.1
- Kronos vs Laguna M.1
- Laguna M.1 vs Laguna S 2.1
- Laguna M.1 vs Laguna XS 2.1
- Laguna M.1 vs LBS-27B
- Laguna M.1 vs LFM2.5-2.6B
- Laguna M.1 vs LFM3-1.7B
- Laguna M.1 vs LFM3-Audio-2B
- Laguna M.1 vs LFM3-Omni-2B
- Laguna M.1 vs LFM3-VL-2B
- Laguna M.1 vs Ling-3.0-tiny
- Laguna M.1 vs Ling-3.1-flash-1019
- Laguna M.1 vs MiMo-V2.6 Distill Qwen 9B
- Laguna M.1 vs MiniCPM-o 4.5
- Laguna M.1 vs MiniCPM-V 4.7 1B
- Laguna M.1 vs MiniCPM-V 4.7 35B-A3B
- Laguna M.1 vs MiniCPM5-2B
- Laguna M.1 vs MiniMax-M2.5
- Laguna M.1 vs MiniMax-M2.7
- Laguna M.1 vs MiniMax-M3.1-Flash
- Laguna M.1 vs Mistral Large 4
- Laguna M.1 vs Muse Glimmer 30B
- Laguna M.1 vs Nanbeige4.3-33B
- Laguna M.1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- Laguna M.1 vs Nemotron 3.5 Lightning 30B A3B
- Laguna M.1 vs Nemotron 3 Nano 30B A3B
- Laguna M.1 vs Nemotron 3 Nano 4B
- Laguna M.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- Laguna M.1 vs Nemotron 3 Super 120B A12B
- Laguna M.1 vs Nemotron 3 Ultra 550B A55B
- Laguna M.1 vs occamy-1.0
- Laguna M.1 vs Olmo 3 7B Think
- Laguna M.1 vs Ornith-1.5-122B
- Laguna M.1 vs Ornith-1.5-35B-A3B
- Laguna M.1 vs Ornith-1.5-397B
- Laguna M.1 vs Ornith-1.5-9B
- Laguna M.1 vs Qwen-AgentWorld-35B-A3B
- Laguna M.1 vs Qwen3-30B-A3B-Instruct-2507
- Laguna M.1 vs Qwen3-4B-Instruct-2507
- Laguna M.1 vs Qwen3.5-0.8B
- Laguna M.1 vs Qwen3.5-122B-A10B
- Laguna M.1 vs Qwen3.5-2B
- Laguna M.1 vs Qwen3.5-397B-A17B
- Laguna M.1 vs Qwen3.5-4B
- Laguna M.1 vs Qwen3.5-9B
- Laguna M.1 vs Qwen3.6-27B
- Laguna M.1 vs Qwen3.6-35B-A3B
- Laguna M.1 vs Qwen3.8-27B
- Laguna M.1 vs Qwen3.8-Flash-Next
- Laguna M.1 vs Qwen3-Coder-30B-A3B-Instruct
- Laguna M.1 vs Qwen3-Coder-Next
- Laguna M.1 vs Qwen3-Omni-30B-A3B-Instruct
- Laguna M.1 vs Qwen3-VL-2B-Instruct
- Laguna M.1 vs Qwen3-VL-30B-A3B-Instruct
- Laguna M.1 vs Qwen3-VL-32B-Instruct
- Laguna M.1 vs Qwen3-VL-4B-Instruct
- Laguna M.1 vs Qwen3-VL-8B-Instruct
- Laguna M.1 vs Qwopus3.8-27B-Flash-V2
- Laguna M.1 vs Recursive Flint
- Laguna M.1 vs Ripple-65B Preview
- Laguna M.1 vs SmolLM3-3B
- Laguna M.1 vs Spark-X2.6-4B
- Laguna M.1 vs Swift 1.5 Qwen3.8-27B
- Laguna M.1 vs Swift 1.5 Qwen3.8-Flash-Next
- Laguna M.1 vs ThinkingCap (Qwen3.8 27B)
- Laguna M.1 vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/granite-4-2-30b-vs-laguna-m-1.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/laguna-m-1.json - From a terminal
anchor compare granite-4-2-30b laguna-m-1(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "laguna-m-1"}at/mcp, no key