Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 31B IT vs MiMo-V2.6-Flash-RL
Gemma 4 31B IT scores 74.1 (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.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence
Ahead on
- Reach, 95 against 65
- Risk, 70 against 45
- Documentation and upkeep, 65 against 59
Also in its favour
- Agent-ready, a grade of BB or better
Watch for
No safety evaluation results published
Best for Teams with a GPU server, or a provider account, that want an open agent model with a very long context
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
178 GB at 4 bits, so it needs about 200 GB of memory
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 Gemma 4 31B IT +30
Gemma 4 31B IT 95
Memory 25, Google's own QAT Q4_0 GGUF is 17.7 GB, which with a fifth added comes to about 21 GB and fits a 24 GB card even with the vision projector. Runtimes 25, released code in llama.cpp, Ollama's library, vLLM, SGLang and Transformers fills the cap without LM Studio, which only Google's guides name. Builds 15, Google ships a 4-bit GGUF and a 4-bit compressed-tensors build, read as two of the kinds the checklist names. Terms 15, plain Apache 2.0 with nothing added. Download 10, no gate and no account. Hosted 5, Google's Gemini API serves it today.
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.
Risk Gemma 4 31B IT +25
Gemma 4 31B IT 70
File safety 25, safetensors only and no code from the repository runs to load it or build prompts. Provenance 15, the files come from the google organisation that gemma.ai links to, with a SHA-256 for every weight file and a git hash for every file. Safety evaluation 0, Google says violations were minimal but publishes no results. Agent guidance 10, the card says what its safety training covers and asks for content safeguards, and the tool guide says to validate calls, but there is no prompt-injection guidance. Licence risk 15, Apache 2.0 carries none of the deductions and its link works. Security programme 5, Google's company-wide security.txt and reward programme exist but nothing ties them to Gemma, and no public 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.
Documentation and upkeep Gemma 4 31B IT +6
Gemma 4 31B IT 65
Model card 20, all seven items are there, with languages given as counts. Training data 10, a general description with a January 2025 cut-off. Last change 10, the files last changed on 20 July, 104 days before 1 November, and Google's reference code for Gemma 4 last changed in a way that runs this model on 6 July, both inside 180 days. Responsiveness 10, Google answered 14 of the last 40 discussions, none since 30 July. Version policy 10, earlier generations are kept in their own repositories with dated releases. File health 5, two template reports wait 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.
Score by category
| Category | Weight this run | Gemma 4 31B IT | MiMo-V2.6-Flash-RL | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 65 | Gemma 4 31B IT +30 |
| Risk | 20%20 | 70 | 45 | Gemma 4 31B IT +25 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 65 | 59 | Gemma 4 31B IT +6 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 74.1/100 | C 61/100 |
Facts side by side
| Fact | Gemma 4 31B 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 |
Verdicts
Gemma 4 31B IT
A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.
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 31B IT
- On the reference machine run
llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768, adding gemma-4-31B-it-mmproj.gguf for images - Or
ollama run gemma4:31bfor the 20 GB Q4_K_M build - In vLLM serve with
--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4, as vLLM's recipe does - Thinking is off by default, so pass
enable_thinking: truein chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block - Drop earlier thoughts from the history except on tool-call turns, as the card asks
- Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64
- Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding
- The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine
MiMo-V2.6-Flash-RL
- Plan for a provider unless you have a multi-GPU server: Xiaomi's API platform, OpenRouter or Novita
- On SGLang or vLLM pass
--tool-call-parser mimo --reasoning-parser mimo - Use temperature 1.0 and top_p 0.95, as the card recommends
- 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
- Skip the dflash folder unless you need speculative decoding. Its mask_embedding.pt is a pickle
- The Use Restrictions section asks for human oversight of high-risk actions
Questions
Which is better for AI agents, Gemma 4 31B IT or MiMo-V2.6-Flash-RL?
Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiMo-V2.6-Flash-RL's 61 (C), and leads in 3 of 4 scored categories.
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Machine-readable
- This page as Markdown
/compare/gemma-4-31b-it-vs-mimo-v2-6-flash-rl.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-31b-it.json·/api/v1/tools/mimo-v2-6-flash-rl.json - From a terminal
anchor compare gemma-4-31b-it mimo-v2-6-flash-rl(the CLI) - Over MCP
compare_tools {"a": "gemma-4-31b-it", "b": "mimo-v2-6-flash-rl"}at/mcp, no key