Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Laguna S 2.1
Granite 4.2 30B and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C). 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 75
- Documentation and upkeep, 87 against 41
Watch for
No safety results published for it
Best for Agents doing long coding sessions on a 128 GB machine or a multi-GPU server that want a permissive licence and a 1M context.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No reply from Poolside to any of 34 discussions, many of them reports about thinking and tool calls
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 +46
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 S 2.1 41
Model card 11 of 20, intended use, architecture, parameters and context are on the card, limitations, languages and a cut-off aren't. Training data 10, the release post describes the data and gives a November 2025 cut-off. Last change 15, 25 August 2026, 70 days back. Responsiveness 0, Poolside answered none of the 34 discussions. Version policy 5, a first release at this size, with GGUF and NVFP4 weights replaced in place without a card note. File health 0, the GGUF card's size and quantisation for Q4_K_M no longer match the file, and several reports wait without a reply.
Reach Granite 4.2 30B +15
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 S 2.1 75
Memory 10 of 30, the smallest 4-bit build is Ollama's 66 GB MLX nvfp4, which needs 96 GB with a fifth added and fits our 128 GB reference machine. Runtimes 25, released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the laguna architecture. Builds 15, Poolside publishes FP8, INT4, NVFP4, MLX and GGUF builds. Terms 10, OpenMDW-1.1 is permissive but not OSI-approved. Download 10, nothing is gated. Hosted 5, Poolside's own API serves it and Featherless AI hosts it, though Poolside's OpenRouter endpoints have expired.
Score by category
| Category | Weight this run | Granite 4.2 30B | Laguna S 2.1 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 75 | Granite 4.2 30B +15 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 87 | 41 | Granite 4.2 30B +46 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | Granite 4.2 30B | Laguna S 2.1 |
|---|---|---|
| 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 | OpenMDW-1.1 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-04 | 2026-08-19 |
| 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 | 2.3/5 (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.
Laguna S 2.1
A strong coding and agent cousin for long sessions, with 8B active parameters and a 1M-token context. At 4-bit it needs a 96 to 128 GB machine, tight on a 128 GB one. The main risks are that nobody at Poolside answers its many reports, there are no safety results, and a build card misdescribes its file.
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 S 2.1
- Quickest start is
ollama run laguna-s-2.1, 96 GB for the default Q4_K_M, or the 66 GB nvfp4 tag on Apple Silicon - On our reference machine try
llama-server -hf poolside/Laguna-S-2.1-GGUF:Q4_K_M --jinja -c 65536, from a llama.cpp release with the laguna architecture (b11772 has it). The 96 GB file leaves little room for context in 107.4 GB - With vLLM or SGLang use
--tool-call-parser poolside_v1 --reasoning-parser poolside_v1, and with TensorRT-LLM--tool_parser poolside_v1 --reasoning_parser laguna - Thinking is on by default in the template, pass
chat_template_kwargswithenable_thinkingfalse to turn it off, and keepreasoning_contentfrom earlier turns - The GGUFs ship set to 262,144 tokens. For 1M with llama.cpp add
--ctx-size 1048576 --rope-scaling yarn --rope-scale 128 --yarn-orig-ctx 8192, which Poolside says can cost quality - Sampling per the generation config is temperature 1.0 and top_k 20, and Poolside suggests temperature 0.7 and top_p 0.95 at 1M
- Expect occasional tool-schema slips in harnesses unlike Poolside's own, and validate JSON arguments, which Poolside lists as known limitations
Questions
Which is better for AI agents, Granite 4.2 30B or Laguna S 2.1?
Granite 4.2 30B and Laguna S 2.1 score within a point of each other on agent readiness, 61 (C) and 61 (C).
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- gpt-oss-20b vs Granite 4.2 30B
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- Granite 4.2 30B vs Qwen3-VL-32B-Instruct
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- Laguna S 2.1 vs Ornith-1.5-35B-A3B
- Laguna S 2.1 vs Ornith-1.5-397B
- Laguna S 2.1 vs Ornith-1.5-9B
- Laguna S 2.1 vs Qwen-AgentWorld-35B-A3B
- Laguna S 2.1 vs Qwen3-30B-A3B-Instruct-2507
- Laguna S 2.1 vs Qwen3-4B-Instruct-2507
- Laguna S 2.1 vs Qwen3.5-0.8B
- Laguna S 2.1 vs Qwen3.5-122B-A10B
- Laguna S 2.1 vs Qwen3.5-2B
- Laguna S 2.1 vs Qwen3.5-397B-A17B
- Laguna S 2.1 vs Qwen3.5-4B
- Laguna S 2.1 vs Qwen3.5-9B
- Laguna S 2.1 vs Qwen3.6-27B
- Laguna S 2.1 vs Qwen3.6-35B-A3B
- Laguna S 2.1 vs Qwen3.8-27B
- Laguna S 2.1 vs Qwen3.8-Flash-Next
- Laguna S 2.1 vs Qwen3-Coder-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-Coder-Next
- Laguna S 2.1 vs Qwen3-Omni-30B-A3B-Instruct
- Laguna S 2.1 vs Qwen3-VL-2B-Instruct
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- Laguna S 2.1 vs Swift 1.5 Qwen3.8-27B
- Laguna S 2.1 vs Swift 1.5 Qwen3.8-Flash-Next
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Machine-readable
- This page as Markdown
/compare/granite-4-2-30b-vs-laguna-s-2-1.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-s-2-1.json - From a terminal
anchor compare granite-4-2-30b laguna-s-2-1(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "laguna-s-2-1"}at/mcp, no key