# LFM3-Audio-2B vs MiMo-V2.6 Distill Qwen 9B

> LFM3-Audio-2B and MiMo-V2.6 Distill Qwen 9B score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-distill-qwen-9b
- Markdown: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-distill-qwen-9b.md (~9,900 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

LFM3-Audio-2B and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B leads on risk. Both do open-weight models you run yourself.

- LFM3-Audio-2B: grade C, 61/100, rank #70 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-audio-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.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

### LFM3-Audio-2B (C)

Good for: Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model

Ahead on:
- Agent interface, 85 against 70
- Documentation and upkeep, 52 against 40

Watch for: Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang

### 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:
- Risk, 65 against 40

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


## Why the edge

### Risk: MiMo-V2.6 Distill Qwen 9B +25

- LFM3-Audio-2B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.
- 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: LFM3-Audio-2B +15

- LFM3-Audio-2B (85): Chat template 20, chat_template.jinja in the repository (9,328 characters), with roles, audio, tools, deferred tools and tool results. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card names turning spoken requests into tool calls as one of its four uses. Structured output 5, the card says the model is designed for structured outputs without saying how to ask for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, the card documents reasoning per request for text replies: the caller prefills the assistant turn with an open think tag to let the model reason, or a closed one for a direct answer, with a use_thinking example. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 5 + 10 + 5 = 85.
- 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.

### Documentation and upkeep: LFM3-Audio-2B +12

- LFM3-Audio-2B (52): Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), limitations (not recommended for agentic coding or complex multi-step reasoning, voice chat in English only), an architecture section, parameters (2B), context (32K) and 19 speech languages and locales. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 31 October, edited the card. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (27 and 31 October 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 17 + 5 + 20 + 0 + 5 + 5 = 52.
- 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.

## Score by category

| Category | Weight | LFM3-Audio-2B | MiMo-V2.6 Distill Qwen 9B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 80 | 80 | even |
| Risk | 20% (20 this run) | 40 | 65 | MiMo-V2.6 Distill Qwen 9B +25 |
| Agent interface | 10% (10 this run) | 85 | 70 | LFM3-Audio-2B +15 |
| Documentation and upkeep | 10% (10 this run) | 52 | 40 | LFM3-Audio-2B +12 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **61 · C** | |

## Facts side by side

| Fact | LFM3-Audio-2B | MiMo-V2.6 Distill Qwen 9B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | Xiaomi (MiMo team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | MIT (metadata only, no LICENSE file); built on Qwen3.5-9B under Apache 2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-09-21 |
| Terms last updated | no document linked | 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

### LFM3-Audio-2B

No score change since it was listed.

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

- no change on 3 November 2026: product

## Verdicts

**LFM3-Audio-2B.** A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.

**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

### LFM3-Audio-2B

1. Start it with `llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-audio`
2. llama.cpp ships examples/realtime_client.py for live speech with this model
3. Prefill the assistant turn with an open think tag to let it reason before a text reply
4. The context is 32K
5. Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales
6. Liquid doesn't recommend it for agentic coding or complex multi-step reasoning

### 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, LFM3-Audio-2B or MiMo-V2.6 Distill Qwen 9B?

LFM3-Audio-2B and MiMo-V2.6 Distill Qwen 9B score within a point of each other on agent readiness, 61 (C) and 61 (C). MiMo-V2.6 Distill Qwen 9B leads on risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-distill-qwen-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-mimo-v2-6-distill-qwen-9b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lfm3-audio-2b", "b": "mimo-v2-6-distill-qwen-9b"}`. From a terminal: `anchor compare lfm3-audio-2b mimo-v2-6-distill-qwen-9b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json and https://www.anchorterminal.com/api/v1/tools/mimo-v2-6-distill-qwen-9b.json

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