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

LFM3-Audio-2B vs Qwen3.8-27B

Qwen3.8-27B scores 68.6 (B) on agent readiness against LFM3-Audio-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Audio-2B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 4969 open weights comparisons

Which one, for what

LFM3-Audio-2B C

Best 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 36

Watch for

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

Qwen3.8-27B B

Best for Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.

Ahead on

  • Reach, 90 against 80
  • Risk, 55 against 40

Watch for

No safety results, prompt-injection guidance or security contact

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.

Documentation and upkeep LFM3-Audio-2B +16

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.

Qwen3.8-27B 36

Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 0, not described. Last change 15, the files last changed on 14 August 2026, 79 days before this check, and Qwen's GitHub repository for the model holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports, recounted on 1 November. Version policy 5, earlier 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but reports about the template and structured output wait without a reply.

Risk Qwen3.8-27B +15

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.

Qwen3.8-27B 55

File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a checksum. Safety evaluation 0, none published. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.

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.

Qwen3.8-27B 70

Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native.

Reach Qwen3.8-27B +10

LFM3-Audio-2B 80

Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 audio projector and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example that starts this model), and Ollama's library has an lfm3-audio entry. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-audio-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.

Qwen3.8-27B 90

Memory 25 of 30, Ollama's smallest 4-bit build is 18 GB, or 21.6 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, seven inference providers are live for it on Hugging Face on 1 November, though Qwen Cloud's own version is still described as coming soon.

Score by category

CategoryWeight this runLFM3-Audio-2BQwen3.8-27BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258090Qwen3.8-27B +10
Risk20%204055Qwen3.8-27B +15
Agent interface10%108570LFM3-Audio-2B +15
Documentation and upkeep10%105236LFM3-Audio-2B +16
Negative events≤1500
TotalC 61/100B 68.6/100

Facts side by side

FactLFM3-Audio-2BQwen3.8-27B
KindOpen-weight modelOpen-weight model
VendorLiquid AIAlibaba (Qwen team)
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceLFM Open License v1.0 (free commercial use below $10M annual revenue)Apache-2.0
Read-only variant documentednono
llms.txtyesno
Last release2026-11-032026-08-14
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviewsnone3/5 (4)

How the scores have moved

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.

Qwen3.8-27B

A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and seven providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.

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

Qwen3.8-27B

  1. Quickest start is ollama run qwen3.8, about 18 GB of files
  2. On our reference machine use BF16 with vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder, since FP8 on gfx1151 isn't documented
  3. Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed
  4. Thinking is on by default, pass chat_template_kwargs with enable_thinking false for short tasks, and read the reasoning from reasoning_content
  5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error
  6. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without
  7. Preserved thinking keeps earlier reasoning in the context unless preserve_thinking is false, which grows long agent sessions
  8. Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts
  9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

Questions

Which is better for AI agents, LFM3-Audio-2B or Qwen3.8-27B?

Qwen3.8-27B scores 68.6 (B) on agent readiness against LFM3-Audio-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Audio-2B leads on agent interface and documentation and upkeep.

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