# Gemma 4 E4B IT vs Mistral Large 4

> Gemma 4 E4B IT scores 63.2 (B) 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/gemma-4-e4b-it-vs-mistral-large-4
- Markdown: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4.md (~6,800 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-e4b-it-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

Gemma 4 E4B IT scores 63.2 (B) 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.

- Gemma 4 E4B IT: grade B, 63.2/100, rank #26 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-e4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.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

### Gemma 4 E4B IT (B)

Good for: Agents that need a cheap, permissively licensed helper on 8 GB of memory, for tool calls, short audio or images, rather than hard reasoning

Ahead on:
- Reach, 95 against 50
- Documentation and upkeep, 75 against 64

Watch for: No safety evaluation figures published

### 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 27
- Risk, 75 against 70
- 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 | Gemma 4 E4B IT | Mistral Large 4 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 27 | 32 | Mistral Large 4 +5 |
| Reach | 25% (25 this run) | 95 | 50 | Gemma 4 E4B IT +45 |
| Risk | 20% (20 this run) | 70 | 75 | Mistral Large 4 +5 |
| Agent interface | 10% (10 this run) | 85 | 95 | Mistral Large 4 +10 |
| Documentation and upkeep | 10% (10 this run) | 75 | 64 | Gemma 4 E4B IT +11 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.2 · B** | **54.6 · C** | |

## Facts side by side

| Fact | Gemma 4 E4B IT | Mistral Large 4 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | 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 | yes | no |
| Last release | 2026-07-20 | 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 | 3.5/5 (4) | none |

## Verdicts

**Gemma 4 E4B IT.** A small cousin for light agent work, tool-calling loops, transcription and image reading on a phone or laptop, under Apache 2.0. Google's 4-bit build needs about 7 GB with working space and runs in all the common runtimes. Google's card puts it far below larger Gemma 4 sizes on agent and coding tasks, with no published safety results.

**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

### Gemma 4 E4B IT

1. On the reference machine run `llama-server -hf google/gemma-4-E4B-it-qat-q4_0-gguf --hf-file gemma-4-E4B_q4_0-it.gguf --jinja -c 131072`, and add `--mmproj` with gemma-4-E4B-it-mmproj.gguf for images and audio
2. With Ollama run `ollama run gemma4:e4b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K
3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there
4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them

### 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, Gemma 4 E4B IT or Mistral Large 4?

Gemma 4 E4B IT scores 63.2 (B) 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/gemma-4-e4b-it-vs-mistral-large-4.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-mistral-large-4.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-e4b-it", "b": "mistral-large-4"}`. From a terminal: `anchor compare gemma-4-e4b-it mistral-large-4`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-e4b-it.json and https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json

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- [Gemma 4 E4B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-thinkingcap-qwen3-8-27b.md)
- [Gemma 4 E4B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-unlimited-ocr-2.md)
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- [Mistral Large 4 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-9b.md)
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- [Mistral Large 4 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-397b-a17b.md)
- [Mistral Large 4 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-4b.md)
- [Mistral Large 4 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-5-9b.md)
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- [Mistral Large 4 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-8-flash-next.md)
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- [Mistral Large 4 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-2b-instruct.md)
- [Mistral Large 4 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-30b-a3b-instruct.md)
- [Mistral Large 4 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-32b-instruct.md)
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- [Mistral Large 4 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/mistral-large-4-vs-qwen3-vl-8b-instruct.md)
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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 Qwen3.8-27B](https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b.md)
- [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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