# DiffusionGemma 26B A4B IT vs MiMo-V2.6-Flash-RL

> MiMo-V2.6-Flash-RL scores 61 (C) to DiffusionGemma 26B A4B IT's 51.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-flash-rl
- Markdown: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-flash-rl.md (~10,150 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
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- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 2 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk. Both do open-weight models you run yourself.

- DiffusionGemma 26B A4B IT: grade D, 51.3/100, rank #143 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-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

### DiffusionGemma 26B A4B IT (D)

Good for: Agents with about 64 GB of GPU memory that want fast single-user generation from text and images and can accept lower quality than Gemma 4

Ahead on:
- Risk, 70 against 45

Watch for: No quantised build from Google, so the smallest official build needs the 64 GB band

### 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

Ahead on:
- Reach, 65 against 55
- Agent interface, 85 against 70

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


## Why the edge

### Risk: DiffusionGemma 26B A4B IT +25

- DiffusionGemma 26B A4B IT (70): File safety gets the full 25, safetensors only and loaded by Transformers' own code and a Jinja template. Provenance gets 15 for files in Google's organisation with a hash for every file. Licence risk keeps 15 under plain Apache 2.0. Agent guidance gets 10 for the card's list of harm categories and its advice to add content-safety safeguards. Safety evaluation scores 0 because the card states improvements without figures. The security programme gets 5 for Google's security.txt, with no public advisories for Gemma.
- 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.

### Agent interface: MiMo-V2.6-Flash-RL +15

- DiffusionGemma 26B A4B IT (70): The chat template is in the repository (20) and handles several tool calls in one turn, and the card names function calling as trained (20). The tool-calling format gets 10, since only the template and the shipped response schema say how tools work here and Google's tool-calling guide is for Gemma 4. Structured output scores 0, undocumented and rejected by SGLang. Thinking can be turned on or off per request with the reasoning in its own channel (10), and the context is 256K (10).
- MiMo-V2.6-Flash-RL (85): Chat template 20, chat_template.jinja in the repository (3,868 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card names the parser to use in two runtimes: the mimo tool-call parser in SGLang and in vLLM, with auto tool choice, and both ship it in a release. Trained tool calling 20, the card describes reinforcement learning on coding and general agent tasks across several harnesses, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch and keeps reasoning_content, and the card names the mimo reasoning parser for SGLang and vLLM, which returns the reasoning apart from the answer. Context 10, 1M tokens per the card's model summary, and 1,048,576 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.

### Reach: MiMo-V2.6-Flash-RL +10

- DiffusionGemma 26B A4B IT (55): Memory earns 15 because the only Google build is 51.7 GB of BF16, about 62 GB with the fifth added, which needs the 64 GB band. Runtimes get 15 for Transformers from 5.11.0, SGLang from v0.5.22 and vLLM, whose releases from v0.24.0 (tag commit 28 June 2026) to v0.33.0 register DiffusionGemmaForBlockDiffusion; llama.cpp (b12114, and Ollama through its pin b11963) and LM Studio don't run it. Builds score 0 because Google publishes no quantised build, though others have published FP8, NVFP4, GGUF and 4-bit copies. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no inference provider is mapped and the Gemini API doesn't serve it.
- 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.

## Score by category

| Category | Weight | DiffusionGemma 26B A4B IT | MiMo-V2.6-Flash-RL | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 55 | 65 | MiMo-V2.6-Flash-RL +10 |
| Risk | 20% (20 this run) | 70 | 45 | DiffusionGemma 26B A4B IT +25 |
| Agent interface | 10% (10 this run) | 70 | 85 | MiMo-V2.6-Flash-RL +15 |
| Documentation and upkeep | 10% (10 this run) | 60 | 59 | DiffusionGemma 26B A4B IT +1 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **51.3 · D** | **61 · C** | |

## Facts side by side

| Fact | DiffusionGemma 26B A4B 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 | 2026-07-15 | 2026-10-28 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.5/5 (4) | none |

## How the scores have moved

### DiffusionGemma 26B A4B IT

- +1.3 on 4 November 2026: correction

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

No score change since it was listed.

## Verdicts

**DiffusionGemma 26B A4B IT.** A text diffusion model for fast low-concurrency local generation from text, images and video, with optional thinking and Gemma 4's tool format, under Apache 2.0. Google's BF16 files need about 62 GB, and it runs in Transformers, SGLang and vLLM. Google's own figures put it below Gemma 4 26B A4B, and Google labels it experimental.

**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

### DiffusionGemma 26B A4B IT

1. Serve it with `sglang serve --model-path google/diffusiongemma-26B-A4B-it --dllm-algorithm Gemma4Renoise --trust-remote-code` on SGLang v0.5.22 or later, then call /v1/chat/completions
2. In Transformers, install `transformers>=5.11.0` and load `DiffusionGemmaForBlockDiffusion` with `dtype="auto"`; generation_config.json already holds the recommended sampler
3. Turn thinking on with `enable_thinking=True` in apply_chat_template or `<|think|>` at the start of the system prompt, read the reasoning between `<|channel>thought` and `<channel|>`, and drop it from history before the next turn
4. Don't rely on temperature, top_p, logit_bias or JSON Schema in SGLang, which ignores or rejects them for this model; validate JSON yourself
5. Streaming arrives one 256-token canvas at a time, not token by token
6. Put images before text in the prompt, and pick an image token budget of 70 to 1,120 by how much detail the task needs

### 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, DiffusionGemma 26B A4B IT or MiMo-V2.6-Flash-RL?

MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against DiffusionGemma 26B A4B IT's 51.3 (D), and leads in 2 of 4 scored categories. DiffusionGemma 26B A4B IT leads on risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-flash-rl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-mimo-v2-6-flash-rl.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "diffusiongemma-26b-a4b-it", "b": "mimo-v2-6-flash-rl"}`. From a terminal: `anchor compare diffusiongemma-26b-a4b-it mimo-v2-6-flash-rl`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/diffusiongemma-26b-a4b-it.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json

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