# Arete-27B vs MiMo-V2.6-Flash-RL

> MiMo-V2.6-Flash-RL scores 61 (C) to Arete-27B's 55.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl
- Markdown: https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.md (~8,850 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

MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on risk. Both do open-weight models you run yourself.

- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.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

### Arete-27B (C)

Good for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine

Ahead on:
- Risk, 55 against 45

Watch for: No quantised build from Nous Research, so the GGUF route depends on a third party

### 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 50
- Agent interface, 85 against 60

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


## Why the edge

### Agent interface: MiMo-V2.6-Flash-RL +25

- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.
- 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 +15

- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.
- 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: Arete-27B +10

- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.
- 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 | Arete-27B | MiMo-V2.6-Flash-RL | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 65 | MiMo-V2.6-Flash-RL +15 |
| Risk | 20% (20 this run) | 55 | 45 | Arete-27B +10 |
| Agent interface | 10% (10 this run) | 60 | 85 | MiMo-V2.6-Flash-RL +25 |
| Documentation and upkeep | 10% (10 this run) | 63 | 59 | Arete-27B +4 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.1 · C** | **61 · C** | |

## Facts side by side

| Fact | Arete-27B | MiMo-V2.6-Flash-RL |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Nous Research | 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 | no | no |
| Last release | 2026-11-06 | 2026-10-28 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |

## Verdicts

**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.

**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

### Arete-27B

1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM
2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's
3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off
4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for
5. Validate JSON output yourself, since structured output isn't documented

### 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, Arete-27B or MiMo-V2.6-Flash-RL?

MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 2 of 4 scored categories. Arete-27B leads on risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-mimo-v2-6-flash-rl.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "arete-27b", "b": "mimo-v2-6-flash-rl"}`. From a terminal: `anchor compare arete-27b mimo-v2-6-flash-rl`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json

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- [Arete-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/arete-27b-vs-thinkingcap-qwen3-8-27b.md)
- [Arete-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/arete-27b-vs-unlimited-ocr-2.md)
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- [Arete-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/arete-27b-vs-wevisone-vl-4b.md)
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- [LFM3-Omni-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-mimo-v2-6-flash-rl.md)
- [LFM3-VL-2B vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-mimo-v2-6-flash-rl.md)
- [Ling-3.0-tiny vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-mimo-v2-6-flash-rl.md)
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- [LongCat-2.5 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-flash-rl.md)
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- [MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-1b.md)
- [MiMo-V2.6-Flash-RL vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minicpm-v-4-7-35b-a3b.md)
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- [MiMo-V2.6-Flash-RL vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-2-3b.md)
- [MiMo-V2.6-Flash-RL vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nanbeige-4-3-33b.md)
- [MiMo-V2.6-Flash-RL vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-bolt-e4b-4gb.md)
- [MiMo-V2.6-Flash-RL vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-5-lightning-30b-a3b.md)
- [MiMo-V2.6-Flash-RL vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-nano-30b-a3b.md)
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- [MiMo-V2.6-Flash-RL vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-super-120b-a12b.md)
- [MiMo-V2.6-Flash-RL vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-nemotron-3-ultra-550b-a55b.md)
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- [MiMo-V2.6-Flash-RL vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-397b.md)
- [MiMo-V2.6-Flash-RL vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-ornith-1-5-9b.md)
- [MiMo-V2.6-Flash-RL vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-otel-2-0-llm-31b-it.md)
- [MiMo-V2.6-Flash-RL vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen-agentworld-35b-a3b.md)
- [MiMo-V2.6-Flash-RL vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-30b-a3b-instruct-2507.md)
- [MiMo-V2.6-Flash-RL vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-4b-instruct-2507.md)
- [MiMo-V2.6-Flash-RL vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-0-8b.md)
- [MiMo-V2.6-Flash-RL vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-122b-a10b.md)
- [MiMo-V2.6-Flash-RL vs Qwen3.5-2B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-2b.md)
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- [MiMo-V2.6-Flash-RL vs Qwen3.5-4B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-5-4b.md)
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- [MiMo-V2.6-Flash-RL vs Qwen3.6-27B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-27b.md)
- [MiMo-V2.6-Flash-RL vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-6-35b-a3b.md)
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- [MiMo-V2.6-Flash-RL vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-coder-30b-a3b-instruct.md)
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- [MiMo-V2.6-Flash-RL vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-2b-instruct.md)
- [MiMo-V2.6-Flash-RL vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-30b-a3b-instruct.md)
- [MiMo-V2.6-Flash-RL vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-32b-instruct.md)
- [MiMo-V2.6-Flash-RL vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-4b-instruct.md)
- [MiMo-V2.6-Flash-RL vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwen3-vl-8b-instruct.md)
- [MiMo-V2.6-Flash-RL vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-qwopus3-8-27b-flash-v2.md)
- [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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