# LongCat-2.5 vs MiMo-V2.6 Distill Qwen 9B

> MiMo-V2.6 Distill Qwen 9B scores 61 (C) to LongCat-2.5's 50.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-distill-qwen-9b
- Markdown: https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-distill-qwen-9b.md (~10,250 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 Distill Qwen 9B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json
- MiMo-V2.6 Distill Qwen 9B: grade C, 61/100, rank #47 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/mimo-v2-6-distill-qwen-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.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

### LongCat-2.5 (D)

Good for: Operators with NPU clusters, and anyone tracking what the open flagships can do

Ahead on:
- Agent interface, 75 against 70
- Documentation and upkeep, 46 against 40

Watch for: 3,562 GB in BF16 and 1,336 GB at 4 bits

### MiMo-V2.6 Distill Qwen 9B (C)

Good for: Researchers and agents that want a small coding and terminal-agent model to fine-tune further on a 24 GB GPU, or to run at 4-bit through Ollama in about 8 GB.

Ahead on:
- Reach, 80 against 35
- Risk, 65 against 60

Watch for: Only BF16 from Xiaomi, about 23 GB to run; the smaller builds are Ollama's


## Why the edge

### Reach: MiMo-V2.6 Distill Qwen 9B +45

- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.
- MiMo-V2.6 Distill Qwen 9B (80): Memory 30 of 30, Ollama's library build at q4_K_M is 6.6 GB, 7.9 GB with a fifth added, while Xiaomi's only build is BF16 at 18.85 GB. Runtimes 25, it keeps Qwen3.5-9B's architecture, which released vLLM, SGLang, Transformers and llama.cpp run, and Ollama's own library carries this model (entry updated 3 November 2026; the 21 October check found none). Builds 0, Xiaomi publishes no quantised build. Terms 15, MIT on the card over an Apache 2.0 base, both with nothing added. Download 10, not gated. Hosted 0, no provider serves this distill.

### Documentation and upkeep: LongCat-2.5 +6

- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.
- MiMo-V2.6 Distill Qwen 9B (40): Model card 5 of 20, intended use and parameters only. Training data 5, a domain mix with token counts but no sources or cut-off. Last change 20, README updates on 28 October 2026. Responsiveness 0, Xiaomi answered none of the 14 discussions. Version policy 5, a first release. File health 5, no LICENSE file, a config that lists an MTP layer the weights lack, and template and tool-call reports waiting without a reply.

### Risk: MiMo-V2.6 Distill Qwen 9B +5

- LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.
- MiMo-V2.6 Distill Qwen 9B (65): File safety 25, safetensors only and no repository code runs. Provenance 15, the files are in Xiaomi's MiMo organisation, which mimo.xiaomi.com links to, with a checksum on every file. Safety evaluation 0, the technical report has no safety results. Agent guidance 5, the card's Use Restrictions section (28 October 2026) gives general cautions, with nothing on prompt injection, refusals or safeguards. Licence risk 15, MIT and Apache 2.0 with no conditions, and the card says its restrictions add none. Security programme 5 of 10, Xiaomi's company security centre takes reports, but nothing links it to MiMo and no advisories were found.

### Agent interface: LongCat-2.5 +5

- LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.
- MiMo-V2.6 Distill Qwen 9B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format is only in the template and the card names no tool parser. Trained tool calling 20, several calls a turn and agentic fine-tuning. Structured output 0, not documented. Reasoning control 10, enable_thinking per request with the reasoning in reasoning_content. Context 10, 262,144 tokens from the base's native length, though the card doesn't say.

## Score by category

| Category | Weight | LongCat-2.5 | MiMo-V2.6 Distill Qwen 9B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 35 | 80 | MiMo-V2.6 Distill Qwen 9B +45 |
| Risk | 20% (20 this run) | 60 | 65 | MiMo-V2.6 Distill Qwen 9B +5 |
| Agent interface | 10% (10 this run) | 75 | 70 | LongCat-2.5 +5 |
| Documentation and upkeep | 10% (10 this run) | 46 | 40 | LongCat-2.5 +6 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **50.5 · D** | **61 · C** | |

## Facts side by side

| Fact | LongCat-2.5 | MiMo-V2.6 Distill Qwen 9B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Meituan (LongCat) | Xiaomi (MiMo team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-10 | 2026-09-21 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.3/5 (4) |

## How the scores have moved

### LongCat-2.5

No score change since it was listed.

### MiMo-V2.6 Distill Qwen 9B

- no change on 3 November 2026: product

## Verdicts

**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.

**MiMo-V2.6 Distill Qwen 9B.** A 9B cousin tuned on MiMo-V2.6's agent work for coding, terminal tasks and visual coding, released as a base for RL research. Ollama's library build needs about 8 GB at 4-bit, while Xiaomi ships only BF16. The main risks are no safety results while a seventh of its tuning is offensive security, and nobody at Xiaomi answering reports.

## Before you call either

### LongCat-2.5

1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet
2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format
3. Pass `chat_template_kwargs` with `enable_thinking` true or false
4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends
5. The card's results use temperature 1.0 and top_p 0.95
6. Its benchmark table is Meituan's own. The unmarked scores are in-house

### MiMo-V2.6 Distill Qwen 9B

1. Quickest start is `ollama run mimo-v2.6:9b`, Ollama's 6.6 GB q4_K_M build with text and image input
2. Serve it as the card does, `sglang serve --model-path XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B --reasoning-parser mimo`, and add `--tool-call-parser mimo` for parsed tool calls
3. SGLang's mimo reasoning parser expects thinking to be asked for, so pass `chat_template_kwargs` with `enable_thinking` true, as the card does, and read `reasoning_content`
4. Calls come back as function and parameter blocks inside tool_call, the same shape vLLM's and SGLang's qwen3_coder parsers read
5. The generation config samples at temperature 0.6, top_k 20 and top_p 0.95
6. Images and video go in through Qwen3.5's processor; the template's audio tokens have no audio encoder behind them
7. The MTP head isn't in the files, so skip MTP speculative decoding even though config.json lists a layer
8. It's an SFT checkpoint meant for further RL, and discussions report runaway generation on llama.cpp, so cap max_tokens
9. If you redistribute the weights, include the Apache 2.0 text of Qwen3.5-9B, which section 4(a) asks for and the repository lacks

## Questions

### Which is better for AI agents, LongCat-2.5 or MiMo-V2.6 Distill Qwen 9B?

MiMo-V2.6 Distill Qwen 9B scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-distill-qwen-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-distill-qwen-9b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "longcat-2-5", "b": "mimo-v2-6-distill-qwen-9b"}`. From a terminal: `anchor compare longcat-2-5 mimo-v2-6-distill-qwen-9b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.json

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