Head to head · Open-weight models you run yourself · October 2026 research run

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

LFM3-Omni-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.

Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons

Which one, for what

LFM3-Omni-2B C

Best for Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools

Ahead on

  • Documentation and upkeep, 49 against 40

Watch for

Only llama.cpp and Ollama run it in a release

MiMo-V2.6 Distill Qwen 9B C

Best 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

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

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

LFM3-Omni-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.

Documentation and upkeep LFM3-Omni-2B +9

LFM3-Omni-2B 49

Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B), context (32K) and the text and speech languages. 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 28 October, documented video frame rate and frame limits. Responsiveness 0, none of the 12 discussions that aren't pull requests (27 October to 9 November 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. 14 + 5 + 20 + 0 + 5 + 5 = 49.

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

CategoryWeight this runLFM3-Omni-2BMiMo-V2.6 Distill Qwen 9BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258080even
Risk20%204065MiMo-V2.6 Distill Qwen 9B +25
Agent interface10%107070even
Documentation and upkeep10%104940LFM3-Omni-2B +9
Negative events≤1500
TotalC 61/100C 61/100

Facts side by side

FactLFM3-Omni-2BMiMo-V2.6 Distill Qwen 9B
KindOpen-weight modelOpen-weight model
VendorLiquid AIXiaomi (MiMo team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceLFM 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 documentednono
llms.txtyesno
Last release2026-11-032026-09-21
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone2.3/5 (4)

How the scores have moved

Verdicts

LFM3-Omni-2B

One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, 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-Omni-2B

  1. Start it with llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja or ollama run lfm3-omni
  2. Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card
  3. The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames
  4. Parse tool calls yourself from the XML it returns
  5. It doesn't reason, so don't pass enable_thinking
  6. Validate JSON output yourself

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-Omni-2B or MiMo-V2.6 Distill Qwen 9B?

LFM3-Omni-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.

Other comparisons with LFM3-Omni-2B or MiMo-V2.6 Distill Qwen 9B

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