# MiMo-V2.6-Flash-RL vs MiniMax-M2.5

> MiMo-V2.6-Flash-RL scores 61 (C) to MiniMax-M2.5's 59 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-5
- Markdown: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-5.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

MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against MiniMax-M2.5's 59 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.

- 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
- MiniMax-M2.5: grade C, 59/100, rank #99 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.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

### 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:
- Risk, 45 against 30
- Agent interface, 85 against 75
- Documentation and upkeep, 59 against 37

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

### MiniMax-M2.5 (C)

Good for: Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy

Watch for: 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine


## Why the edge

### Documentation and upkeep: MiMo-V2.6-Flash-RL +22

- 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.
- MiniMax-M2.5 (37): The model card earns 2, stating only intended use and leaving out limitations, architecture, parameters, context, natural languages and a cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 20, a card edit on 26 October 2026 (commit 2515a3ea, a link renamed from MiniMax Agent to MiniMax Code), 9 days before this check; the files, template and code are unchanged since February. Responsiveness earns 5, with a MiniMax reply in 2 of the 40 most recent discussions and none since April. Version policy earns 5, since earlier and later models sit in their own repositories without dated notes. File health earns nothing, because the card's licence metadata still contradicts the licence in the files and an FP8 output report still waits without a reply. 2+5+20+5+5+0 = 37.

### Risk: MiMo-V2.6-Flash-RL +15

- 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.
- MiniMax-M2.5 (30): File safety earns 15, safetensors only with no pickle files, less 10 because every documented loading route passes trust_remote_code for the repository's Python. Provenance earns the full 15, files in the organisation MiniMax links to with a SHA-256 for each shard. Safety evaluation and agent guidance earn nothing, as MiniMax publishes neither. Licence risk earns nothing (read 21 October 2026). The Prohibited Uses Policy goes beyond the law, MiniMax can end the licence for any breach and replaced its terms three weeks after release, and that change on 6 March 2026 came with no dated note while the card still links to a deleted file. The security programme earns nothing, with no security.txt, disclosure policy, SECURITY.md or advisories, and only a general contact address on the card.

### Agent interface: MiMo-V2.6-Flash-RL +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.
- MiniMax-M2.5 (75): The chat template earns 20 for chat_template.jinja in the repository. The tool-calling format earns 25, as MiniMax's own guide documents how tools go in and how calls come back, gives a parsing function, and its deployment guides name the vLLM and SGLang parsers. Trained tool calling earns 20, with several invoke blocks in one call. Structured output earns nothing, since JSON output isn't documented or claimed. Reasoning control earns nothing, because thinking is always on with no documented way to turn it off or budget it. Context earns 10 for 196,608 tokens with no position scaling.

## Score by category

| Category | Weight | MiMo-V2.6-Flash-RL | MiniMax-M2.5 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 65 | even |
| Risk | 20% (20 this run) | 45 | 30 | MiMo-V2.6-Flash-RL +15 |
| Agent interface | 10% (10 this run) | 85 | 75 | MiMo-V2.6-Flash-RL +10 |
| Documentation and upkeep | 10% (10 this run) | 59 | 37 | MiMo-V2.6-Flash-RL +22 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **59 · C** | |

## Facts side by side

| Fact | MiMo-V2.6-Flash-RL | MiniMax-M2.5 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Xiaomi (MiMo team) | MiniMax |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT (metadata only, no LICENSE file) | MiniMax Model License (model) and modified MIT (code) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-28 | 2026-02-12 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 1.8/5 (4) |

## How the scores have moved

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

No score change since it was listed.

### MiniMax-M2.5

- +1.5 on 4 November 2026: product

## Verdicts

**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.

**MiniMax-M2.5.** MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.

## Before you call either

### 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

### MiniMax-M2.5

1. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think
2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code
3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache
4. On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory
5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends
6. Budget output tokens for thinking on every turn, since the template always opens a think block
7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy
8. Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026

## Questions

### Which is better for AI agents, MiMo-V2.6-Flash-RL or MiniMax-M2.5?

MiMo-V2.6-Flash-RL scores 61 (C) on agent readiness against MiniMax-M2.5's 59 (C), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mimo-v2-6-flash-rl-vs-minimax-m2-5.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "mimo-v2-6-flash-rl", "b": "minimax-m2-5"}`. From a terminal: `anchor compare mimo-v2-6-flash-rl minimax-m2-5`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-flash-rl.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json

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- [MiniMax-M2.5 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwopus3-8-27b-flash-v2.md)
- [MiniMax-M2.5 vs Recursive Flint](https://www.anchorterminal.com/compare/minimax-m2-5-vs-recursive-flint.md)
- [MiniMax-M2.5 vs Ripple-65B Preview](https://www.anchorterminal.com/compare/minimax-m2-5-vs-ripple-65b-preview.md)
- [MiniMax-M2.5 vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/minimax-m2-5-vs-sensenova-loop-9b-preview.md)
- [MiniMax-M2.5 vs SmolLM3-3B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b.md)
- [MiniMax-M2.5 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.md)
- [MiniMax-M2.5 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/minimax-m2-5-vs-step-3-7-flash.md)
- [MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.md)
- [MiniMax-M2.5 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.md)
- [MiniMax-M2.5 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b.md)
- [MiniMax-M2.5 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.md)
- [MiniMax-M2.5 vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-2b.md)
- [MiniMax-M2.5 vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-wevisone-vl-4b.md)
- [MiniMax-M2.5 vs ZAYA1-8B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-zaya1-8b.md)

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