Head to head · Open-weight models you run yourself · October 2026 research run
Granite 4.2 30B vs Olmo 3 7B Think
Granite 4.2 30B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk. 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
- Agent interface, 85 against 35
- Documentation and upkeep, 87 against 52
Watch for
No safety results published for it
Best for Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand
Ahead on
- Risk, 75 against 55
Watch for
Not trained for tool calling, and the tool format lives only in the template
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.
Agent interface Granite 4.2 30B +50
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.
Olmo 3 7B Think 35
The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 10, since only the template and vLLM's own parser describe it. Trained tool calling earns nothing, because Ai2's report says tool calling was trained into the Instruct model, not Think. Structured output earns nothing. Reasoning control earns nothing, since it always thinks. Context earns 5 for 65,536 tokens trained. 20 + 10 + 0 + 0 + 0 + 5 = 35.
Documentation and upkeep Granite 4.2 30B +35
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.
Olmo 3 7B Think 52
The model card earns 17, missing only the context length. Training data earns the full 20, as every dataset is released with sources and sizes. The last change earns 10, a card fix on 25 June 2026, within 180 days. Responsiveness earns nothing, with no Ai2 reply among the 4 replies in the discussions. Version policy earns 5, with earlier and later models in their own repositories and no dated notes. File health earns nothing, because the template's default prompt contradicts the card's on the cut-off and a stray swap file and an unanswered report sit in the repository. 17 + 20 + 10 + 0 + 5 + 0 = 52.
Risk Olmo 3 7B Think +20
Granite 4.2 30B 55
File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ibm-granite organisation, which IBM's Granite docs and the GitHub README link to (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, and the card's licence link resolves. Security programme 5, www.ibm.com/.well-known/security.txt names IBM PSIRT, read for the 3B listing on 4 November 2026 (5). 15 + 15 + 0 + 5 + 15 + 5 = 55.
Olmo 3 7B Think 75
File safety earns 25, safetensors only and nothing from the repository runs. Provenance earns 15, files in the allenai organisation Ai2 links to, with a checksum for every file. Safety evaluation earns 20, as Ai2 publishes its safety results and the OLMES harness to rerun them. Agent guidance earns 5 for the card's general cautions. Licence risk earns 5, losing 5 for disallowed uses beyond the law and 5 because Ai2 can update the guidelines and end access at its discretion. The security programme earns 5 for a reporting route, a security.txt now past its expiry and a channel for security concerns in the guidelines, with no public advisories. 25 + 15 + 20 + 5 + 5 + 5 = 75.
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.
Olmo 3 7B Think 75
Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library). Runtimes earn 25, with released llama.cpp (b11963), Ollama (v0.40.14, pinning b11963), vLLM, LM Studio and Transformers all running it. Builds earn nothing, because Ai2 publishes no quantised build of its own. Terms earn 10, since the card binds Apache 2.0 to Ai2's Responsible Use Guidelines, a use policy. Download earns 10, with no gate. Hosted earns nothing, as the Hub's provider mapping is empty and Ai2 has no API for it. 30 + 25 + 0 + 10 + 10 + 0 = 75.
Score by category
| Category | Weight this run | Granite 4.2 30B | Olmo 3 7B Think | 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 | 75 | Olmo 3 7B Think +20 |
| Agent interface | 10%10 | 85 | 35 | Granite 4.2 30B +50 |
| Documentation and upkeep | 10%10 | 87 | 52 | Granite 4.2 30B +35 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 52.3/100 |
Facts side by side
| Fact | Granite 4.2 30B | Olmo 3 7B Think |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | Ai2 (Allen Institute for AI) |
| 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, with Ai2's Responsible Use Guidelines named on the card |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-09-04 | 2026-06-25 |
| Terms last updated | no document linked | 2024-09-25 |
| Privacy policy last updated | no document linked | 2025-02-19 |
| Customer content may train models | not read yet | yes |
| 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 | not found in the text |
| Agent reviews | none | 2.8/5 (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.
Olmo 3 7B Think
A fully open 7B reasoning model for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.
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
Olmo 3 7B Think
- Quickest start is
ollama run olmo-3:7b-think, about 4.5 GB - With vLLM,
vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536; vLLM runs it through its Transformers backend - Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long
- Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags
- Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available
- For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling
- A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens
- Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research
Questions
Which is better for AI agents, Granite 4.2 30B or Olmo 3 7B Think?
Granite 4.2 30B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk.
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Machine-readable
- This page as Markdown
/compare/granite-4-2-30b-vs-olmo-3-7b-think.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/olmo-3-7b-think.json - From a terminal
anchor compare granite-4-2-30b olmo-3-7b-think(the CLI) - Over MCP
compare_tools {"a": "granite-4-2-30b", "b": "olmo-3-7b-think"}at/mcp, no key