Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Qwen3.6-35B-A3B
Qwen3.6-35B-A3B scores 62.7 (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 reach, 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
- Reach, 90 against 85
- Agent interface, 85 against 70
- Documentation and upkeep, 87 against 31
Watch for
No safety results published for it
Best for Agents with a 32 GB or larger machine that want a quick, permissively licensed coding and tool-calling cousin with vision.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No safety evaluation, no agent-use guidance and no way to report a security flaw
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.
Qwen3.6-35B-A3B 31
The model card earns 11, covering intended use, architecture, parameters and context but not limitations, languages or a cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 5, a README edit on 24 April 2026, 192 days before this check, past 180 days and within a year, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness earns 0, with no Qwen reply in the 40 most recent of 62 discussions that aren't pull requests, recounted on 2 November. Version policy earns 5, the earlier version kept in its own repository without a note. File health earns 5, the files agree but reports about tool calling and the template wait unanswered.
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.
Qwen3.6-35B-A3B 70
The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 10, because the card names the qwen3_coder parser but leaves the return format to the template. Trained tool calling earns 20, with several calls to a turn in the template. Structured output earns 0, as JSON output isn't documented for the model. Reasoning control earns 10, thinking off per request with enable_thinking and the reasoning returned in think tags for the qwen3 parser. Context earns 10 for 262,144 tokens native.
Reach Granite 4.2 30B +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.
Qwen3.6-35B-A3B 85
Memory earns 20 because the smallest 4-bit build from a runtime's own library is Ollama's Q4_K_M at 23 GB, which comes to 27.6 GB with the fifth added, so it needs a 32 GB machine rather than a 24 GB one. Runtimes earn the full 25, since released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the architecture. Builds earn 10 for Alibaba's one quantised build, FP8, with no 4-bit or GGUF from the publisher. Terms earn 15 for plain Apache 2.0, download earns 10 for ungated files, and hosted earns 5 because DeepInfra, Featherless AI and Scaleway serve it through Hugging Face.
Score by category
| Category | Weight this run | Granite 4.2 30B | Qwen3.6-35B-A3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 85 | Granite 4.2 30B +5 |
| 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 62.7/100 |
Facts side by side
| Fact | Granite 4.2 30B | Qwen3.6-35B-A3B |
|---|---|---|
| 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 | 2026-04-16 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (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.
Qwen3.6-35B-A3B
A fast mixture-of-experts cousin with 3B active parameters for coding and tool calling, with image and video input, in every major runtime under plain Apache 2.0. Ollama's 4-bit build needs about 28 GB. The main risk is that nobody at Alibaba maintains it, with no safety results, no security contact and no replies to tool-calling reports.
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
Qwen3.6-35B-A3B
- Start it with
ollama run qwen3.6:35b(Q4_K_M, 23 to 24 GB on disk, about 28 GB in memory) - On the reference machine use
vllm serve Qwen/Qwen3.6-35B-A3B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, BF16, since vLLM doesn't document FP8 compute on gfx1151 - Use the qwen3_coder tool-call parser and the qwen3 reasoning parser in vLLM 0.19.0 or SGLang 0.5.10 or later
- Thinking is on by default and /nothink isn't supported, so pass enable_thinking as false in chat_template_kwargs for short tasks, or preserve_thinking as true to keep earlier reasoning
- Card sampling for thinking is temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, and for non-thinking temperature 0.7, top_p 0.8
- Keep at least 128K of context as the card advises, and turn on YaRN only beyond 262,144 tokens
- Validate JSON output yourself or use the runtime's guided decoding, since the model card documents none
Questions
Which is better for AI agents, Granite 4.2 30B or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B scores 62.7 (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 reach, agent interface and documentation and upkeep.
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- Qwen3.6-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-2B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-30B-A3B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-32B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-4B-Instruct
- Qwen3.6-35B-A3B vs Qwen3-VL-8B-Instruct
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- Qwen3.6-35B-A3B vs Recursive Flint
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- Qwen3.6-35B-A3B vs SenseNova-Loop-9B-Preview
- Qwen3.6-35B-A3B vs SmolLM3-3B
- Qwen3.6-35B-A3B vs Spark-X2.6-4B
- Qwen3.6-35B-A3B vs Step 3.7 Flash
- Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-27B
- Qwen3.6-35B-A3B vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)
- Qwen3.6-35B-A3B vs Baidu Unlimited-OCR-2
- Qwen3.6-35B-A3B vs WeVisOne-VL-2B
- Qwen3.6-35B-A3B vs WeVisOne-VL-4B
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Machine-readable
- This page as Markdown
/compare/granite-4-2-30b-vs-qwen3-6-35b-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/qwen3-6-35b-a3b.json - From a terminal
anchor compare granite-4-2-30b qwen3-6-35b-a3b(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "qwen3-6-35b-a3b"}at/mcp, no key