Head to head · Open-weight models you run yourself · October 2026 research run
Arete-27B vs Granite 4.2 3B
Granite 4.2 3B scores 59.8 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 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 Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No quantised build from Nous Research, so the GGUF route depends on a third party
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 50
- Agent interface, 85 against 60
- Documentation and upkeep, 92 against 63
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 +45
Arete-27B 50
Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.
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 +29
Arete-27B 63
Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.
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).
Agent interface Granite 4.2 3B +25
Arete-27B 60
Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.
Granite 4.2 3B 85
The template ships in the repository (20). The card shows how tools are declared and how calls come back, and names qwen3_coder for vLLM and SGLang (25). The template renders several calls in a turn (20). JSON output isn't documented for 4.2 (0). Thinking turns off or goes to low effort per request, with the reasoning returned apart in SGLang (10), and 131,072 tokens are native (10).
Score by category
| Category | Weight this run | Arete-27B | Granite 4.2 3B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 95 | Granite 4.2 3B +45 |
| Risk | 20%20 | 55 | 55 | even |
| Agent interface | 10%10 | 60 | 85 | Granite 4.2 3B +25 |
| Documentation and upkeep | 10%10 | 63 | 92 | Granite 4.2 3B +29 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 55.1/100 | C 59.8/100 |
Facts side by side
| Fact | Arete-27B | Granite 4.2 3B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Nous Research | 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 | 2026-11-06 | 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
Arete-27B
Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.
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
Arete-27B
- Start it with
llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja, a third party's conversion, or serve the BF16 files with vLLM - Use the base's parsers in vLLM,
--reasoning-parser qwen3 --tool-call-parser qwen3_coder, and check tool calls first, since the template differs slightly from Qwen3.8-27B's - Thinking switches with
enable_thinkingin the template. The card's own evaluation ran with it off - Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for
- Validate JSON output yourself, since structured output isn't documented
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, Arete-27B or Granite 4.2 3B?
Granite 4.2 3B scores 59.8 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 3 of 4 scored categories.
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Machine-readable
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/compare/arete-27b-vs-granite-4-2-3b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/arete-27b.json·/api/v1/tools/granite-4-2-3b.json - From a terminal
anchor compare arete-27b granite-4-2-3b(the CLI) - Over MCP
compare_tools {"a": "arete-27b", "b": "granite-4-2-3b"}at/mcp, no key