# Granite 4.2 3B vs Mistral Large 4

> Granite 4.2 3B scores 59.8 (C) to Mistral Large 4's 54.6 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4
- Markdown: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4.md (~6,900 tokens)
- Slim: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-30

Granite 4.2 3B scores 59.8 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 5 scored categories. Mistral Large 4 leads on capability, risk and agent interface. Both do open-weight models you run yourself.

- Granite 4.2 3B: grade C, 59.8/100, rank #63 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json
- Mistral Large 4: grade C, 54.6/100, rank #92 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/mistral-large-4.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3588 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Granite 4.2 3B (C)

Good 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
- Documentation and upkeep, 92 against 64

Watch for: No safety results published for 4.2, and only general safety cautions

### Mistral Large 4 (C)

Good for: Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.

Ahead on:
- Capability, 32 against 21
- Risk, 75 against 55
- Agent interface, 95 against 85

Watch for: The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine


## Score by category

| Category | Weight | Granite 4.2 3B | Mistral Large 4 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 21 | 32 | Mistral Large 4 +11 |
| Reach | 25% (25 this run) | 95 | 50 | Granite 4.2 3B +45 |
| Risk | 20% (20 this run) | 55 | 75 | Mistral Large 4 +20 |
| Agent interface | 10% (10 this run) | 85 | 95 | Mistral Large 4 +10 |
| Documentation and upkeep | 10% (10 this run) | 92 | 64 | Granite 4.2 3B +28 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.8 · C** | **54.6 · C** | |

## Facts side by side

| Fact | Granite 4.2 3B | Mistral Large 4 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | Mistral AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Mistral Ecosystem Open-Weight License (modified MIT) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-20 | 2026-10-29 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 3.8/5 (4) | none |

## Verdicts

**Granite 4.2 3B.** A small reasoning cousin for tool calls, coding and multilingual chat that runs almost anywhere, from a 2.2 GB Ollama build to vLLM and SGLang, under plain Apache 2.0 with signed files and a dataset disclosure. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.

**Mistral Large 4.** A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.

## Before you call either

### Granite 4.2 3B

1. 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
2. With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--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.py` isn't needed
3. With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content
4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires
5. Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server
6. Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history
7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2
8. Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide

### Mistral Large 4

1. Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it
2. On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe
3. Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch
4. Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too
5. Use temperature 1.0 and top_p 0.95 at every effort, per the card
6. Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model
7. The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message
8. Add `--speculative-config '{"method": "dspark", "num_speculative_tokens": 3}'` to use the embedded draft head
9. If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence

## Questions

### Which is better for AI agents, Granite 4.2 3B or Mistral Large 4?

Granite 4.2 3B scores 59.8 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 2 of 5 scored categories. Mistral Large 4 leads on capability, risk and agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-mistral-large-4.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "granite-4-2-3b", "b": "mistral-large-4"}`. From a terminal: `anchor compare granite-4-2-3b mistral-large-4`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json and https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json

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- [Mistral Large 4 vs Recursive Flint](https://www.anchorterminal.com/compare/mistral-large-4-vs-recursive-flint.md)
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- [Mistral Large 4 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next.md)
- [Mistral Large 4 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/mistral-large-4-vs-thinkingcap-qwen3-8-27b.md)
- [Mistral Large 4 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/mistral-large-4-vs-unlimited-ocr-2.md)

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