Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Qwen3.6-27B
Qwen3.6-27B scores 66.7 (B) on agent readiness against Granite 4.2 30B's 61 (C), and leads in 0 of 4 scored categories. Granite 4.2 30B leads on agent interface 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
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
- Agent interface, 85 against 70
- Documentation and upkeep, 87 against 31
Watch for
No safety results published for it
Best for Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No safety results, prompt-injection guidance or security contact
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 +56
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.
Qwen3.6-27B 31
Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, only a general family description without a cut-off, in the GitHub README. Last change 5, the files last changed on 24 April 2026, 191 days before this check, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports, recounted on 1 November. Version policy 5, earlier and later 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but template and tool-calling reports wait without a reply.
Agent interface Granite 4.2 30B +15
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.
Qwen3.6-27B 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the card names the qwen3_coder parser but the declaration and call format live only in the template. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request and comes back in think tags split out by the qwen3 parser. Context 10, 262,144 tokens native.
Score by category
| Category | Weight this run | Granite 4.2 30B | Qwen3.6-27B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 90 | even |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 85 | 70 | Granite 4.2 30B +15 |
| Documentation and upkeep | 10%10 | 87 | 31 | Granite 4.2 30B +56 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | B 66.7/100 |
Facts side by side
| Fact | Granite 4.2 30B | Qwen3.6-27B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | Alibaba (Qwen team) |
| 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 | 2026-04-22 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| 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.
Qwen3.6-27B
A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it.
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
Qwen3.6-27B
- Quickest start is
ollama run qwen3.6:27b, about 18 GB of files with the vision projector - Alibaba's FP8 build with the card's parsers is
vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, vllm 0.19.0 or later - Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call
- Thinking is on by default, pass
"chat_template_kwargs": {"enable_thinking": false}for short tasks, and/thinkand/nothinkaren't supported "preserve_thinking": truein chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt- Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without
- Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens
- Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented
- Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes
Questions
Which is better for AI agents, Granite 4.2 30B or Qwen3.6-27B?
Qwen3.6-27B scores 66.7 (B) on agent readiness against Granite 4.2 30B's 61 (C), and leads in 0 of 4 scored categories. Granite 4.2 30B leads on agent interface and documentation and upkeep.
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- Qwen3.6-27B vs Qwen3-Coder-Next
- Qwen3.6-27B vs Qwen3-Omni-30B-A3B-Instruct
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- Qwen3.6-27B vs Ripple-65B Preview
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- Qwen3.6-27B vs Step 3.7 Flash
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- Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.6-27B vs Baidu Unlimited-OCR-2
- Qwen3.6-27B vs WeVisOne-VL-2B
- Qwen3.6-27B vs WeVisOne-VL-4B
- Qwen3.6-27B vs ZAYA1-8B
Machine-readable
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/compare/granite-4-2-30b-vs-qwen3-6-27b.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/qwen3-6-27b.json - From a terminal
anchor compare granite-4-2-30b qwen3-6-27b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "qwen3-6-27b"}at/mcp, no key