# Gemma 4 12B IT vs MiMo-V2.6-Flash-RL

> Gemma 4 12B IT scores 72.3 (BB) to MiMo-V2.6-Flash-RL's 61 (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-4-12b-it-vs-mimo-v2-6-flash-rl
- Markdown: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-flash-rl.md (~9,500 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: 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)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

Gemma 4 12B IT scores 72.3 (BB) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- Gemma 4 12B IT: grade BB, 72.3/100, rank #4 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-12b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json
- MiMo-V2.6-Flash-RL: grade C, 61/100, rank #75 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-flash-rl.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Gemma 4 12B IT (BB)

Good for: Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context

Ahead on:
- Reach, 95 against 65
- Risk, 70 against 45
- Documentation and upkeep, 85 against 59

Also in its favour:
- Agent-ready, a grade of BB or better

Watch for: No safety evaluation figures published

### MiMo-V2.6-Flash-RL (C)

Good for: Teams with a GPU server, or a provider account, that want an open agent model with a very long context

Watch for: 178 GB at 4 bits, so it needs about 200 GB of memory


## Why the edge

### Reach: Gemma 4 12B IT +30

- Gemma 4 12B IT (95): Memory 30, Google's own 4-bit QAT GGUF with its projector is 7.15 GB, which with a fifth on top is 8.6 GB and fits in 16 GB or less. Runtimes 25, released support in Transformers, vLLM, SGLang, llama.cpp, Ollama and LM Studio, six of six, all for the separate gemma4_unified architecture. Builds 15, Google publishes a Q4_0 GGUF and a w4a16 build, read as two kinds as for the 31B. Terms 15, plain Apache 2.0 with nothing added. Download 10, no account or gate. Hosted 0, the Gemini API serves only the two largest sizes and no inference provider lists this one.
- MiMo-V2.6-Flash-RL (65): Memory 5, the smallest build at 4 bits or better is ggml-org's MXFP4 GGUF, 168.93 GB with its projector, and 168.93 GB × 1.2 = 202.7 GB, within 256 GB. Xiaomi's own files are 177.74 GB. ggml-org's Q2_K is below 4 bits and isn't read. Runtimes 20, Released vLLM 0.33.0 and SGLang 0.5.23 have the mimo_v2 model and the mimo parsers, Transformers 5.22.2 has a mimo_v2_flash model, and llama.cpp b12242 has the architecture, with a GGUF from its own organisation. Builds 10, one precision from Xiaomi, the 4-bit files it releases. Terms 15, MIT in the card's metadata, an OSI licence. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says it is on Xiaomi's MiMo Open Platform API and on OpenRouter, and Novita is live for it on Hugging Face. 5 + 20 + 10 + 15 + 10 + 5 = 65.

### Documentation and upkeep: Gemma 4 12B IT +26

- Gemma 4 12B IT (85): Model card 20, all seven items are there. Training data 10, a general description by type with a January 2025 cut-off and the filtering, but no sources, mix or sizes. Last change 20, Google merged a fix for 210 glitch tokens in tokenizer.json on 30 October 2026, two days before 1 November. Responsiveness 20, Google replied to 18 of 34 questions and reports, the last on 2026-07-31, re-counted on 1 November. Version policy 10, earlier Gemma generations keep their own repositories and gemma.ai has a dated releases page. File health 5, the files agree with the card but a report about them waits without a reply.
- MiMo-V2.6-Flash-RL (59): Model card 14, Intended use, the architecture, the parameters, the context and the languages (in the metadata) are on the card. Training data 5, A description of the training method with no data sources, sizes or cut-off. Last change 20, the last commits, on 28 October 2026, updated the card, 13 days before this check. Responsiveness 5, Of the 13 most recent discussions that aren't pull requests (21 September to 6 November 2026), 3 have a reply from a XiaomiMiMo member, which is a few. Version policy 5, MiMo-V2.5 stays in its own repositories and V2.6 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the 1M context, and two early faults (invalid JSON in the draft model's config, preprocessor values) were fixed in commits that say so. 14 + 5 + 20 + 5 + 5 + 10 = 59.

### Risk: Gemma 4 12B IT +25

- Gemma 4 12B IT (70): File safety 25, safetensors and GGUF only, with nothing from the repository run on load. Provenance 15, the files sit in Google's own organisation and every file has a checksum. Safety evaluation 0, the card says testing showed minimal policy violations but gives no figures. Agent guidance 10, the card names the harm categories its safety training covers and asks for content-safety safeguards, and the tool guide says to validate calls, with no prompt-injection guidance. Licence risk 15, Apache 2.0 with no added policy, no threshold and a working link. Security programme 5, Google's security.txt and reward programme give a route to report a flaw, but no public Gemma advisories were found.
- MiMo-V2.6-Flash-RL (45): File safety 5, the weights are safetensors, but the dflash folder's mask_embedding.pt is a pickle beside them (15). Provenance 15, the files sit in the XiaomiMiMo organisation, which mimo.xiaomi.com links to per the Distill listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions only. Licence risk 15, MIT has no use policy, threshold or change clause. Security programme 5, Xiaomi runs a company security centre for reporting flaws, as read for the Distill listing (5). 5 + 15 + 0 + 5 + 15 + 5 = 45.

## Score by category

| Category | Weight | Gemma 4 12B IT | MiMo-V2.6-Flash-RL | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 95 | 65 | Gemma 4 12B IT +30 |
| Risk | 20% (20 this run) | 70 | 45 | Gemma 4 12B IT +25 |
| Agent interface | 10% (10 this run) | 85 | 85 | even |
| Documentation and upkeep | 10% (10 this run) | 85 | 59 | Gemma 4 12B IT +26 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **72.3 · BB** | **61 · C** | |

## Facts side by side

| Fact | Gemma 4 12B IT | MiMo-V2.6-Flash-RL |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | Xiaomi (MiMo team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MIT (metadata only, no LICENSE file) |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-10-28 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.8/5 (4) | none |

## How the scores have moved

### Gemma 4 12B IT

- +1.0 on 1 November 2026: product

### MiMo-V2.6-Flash-RL

No score change since it was listed.

## Verdicts

**Gemma 4 12B IT.** A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.

**MiMo-V2.6-Flash-RL.** A large agent model with an open licence: text, images, video and audio, a 1M context, tool calling it was trained for, and code in four released runtimes. It's a server model, 178 GB at 4 bits, and the card's commands run the repository's code. No safety results, little said about data, and few questions answered.

## Before you call either

### Gemma 4 12B IT

1. On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio
2. With Ollama run `ollama run gemma4:12b` 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
6. Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama

### MiMo-V2.6-Flash-RL

1. Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita
2. On SGLang or vLLM pass `--tool-call-parser mimo --reasoning-parser mimo`
3. Use temperature 1.0 and top_p 0.95, as the card recommends
4. The card's commands pass trust-remote-code. Read the repository's three Python files first, or use Transformers 5.22.2, which has the model built in
5. Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle
6. The Use Restrictions section asks for human oversight of high-risk actions

## Questions

### Which is better for AI agents, Gemma 4 12B IT or MiMo-V2.6-Flash-RL?

Gemma 4 12B IT scores 72.3 (BB) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-flash-rl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-mimo-v2-6-flash-rl.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-12b-it", "b": "mimo-v2-6-flash-rl"}`. From a terminal: `anchor compare gemma-4-12b-it mimo-v2-6-flash-rl`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-it.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json

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- [MiMo-V2.6-Flash-RL vs Recursive Flint](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-recursive-flint.md)
- [MiMo-V2.6-Flash-RL vs Ripple-65B Preview](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ripple-65b-preview.md)
- [MiMo-V2.6-Flash-RL vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-sensenova-loop-9b-preview.md)
- [MiMo-V2.6-Flash-RL vs SmolLM3-3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-smollm3-3b.md)
- [MiMo-V2.6-Flash-RL vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-spark-x2-6-4b.md)
- [MiMo-V2.6-Flash-RL vs Step 3.7 Flash](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-step-3-7-flash.md)
- [MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-27b.md)
- [MiMo-V2.6-Flash-RL vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-swift-1-5-qwen3-8-flash-next.md)
- [MiMo-V2.6-Flash-RL vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-thinkingcap-qwen3-8-27b.md)
- [MiMo-V2.6-Flash-RL vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-unlimited-ocr-2.md)
- [MiMo-V2.6-Flash-RL vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-2b.md)
- [MiMo-V2.6-Flash-RL vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-wevisone-vl-4b.md)
- [MiMo-V2.6-Flash-RL vs ZAYA1-8B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-zaya1-8b.md)

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