Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-Audio-2B vs Qwen3.5-0.8B
LFM3-Audio-2B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 2 of 4 scored categories. Qwen3.5-0.8B leads on risk. 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
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 60
- Documentation and upkeep, 52 against 37
Watch for
Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang
Best for Builders who want a near-free local helper or fine-tuning base for narrow tasks on a phone-class device, laptop or CPU
Ahead on
- Risk, 55 against 40
Watch for
No quantised build from Alibaba and no inference provider found
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.
Agent interface LFM3-Audio-2B +25
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.
Qwen3.5-0.8B 60
The template ships in the repository (20) and the model was trained for tool use with several calls per turn in the template (20). Tool-calling format gets 10, because the card names the qwen3_coder parser but leaves the format to the template. Reasoning control scores 0, since the card shows how to turn thinking on but not where the reasoning comes back. 262,144 tokens is native (10). Structured output scores 0, as nothing documents JSON output for the model. 20+10+20+0+0+10 = 60.
Risk Qwen3.5-0.8B +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.
Qwen3.5-0.8B 55
Safetensors only with no remote code gives file safety its 25, and files in the Qwen organisation the publisher's GitHub links to, each with a hash on the Hub, give provenance 15. The Apache 2.0 licence carries none of the deductions, so licence risk keeps 15. Safety evaluation, agent guidance and a security programme score 0, since the card has no safety results or agent cautions and no reporting route was found. The Qwen3.5 blog and Alibaba's security response centre couldn't be read, so those are scored as absent and left open. 25+15+0+0+15+0 = 55.
Documentation and upkeep LFM3-Audio-2B +15
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.
Qwen3.5-0.8B 37
The card covers six of the seven items, missing only a cut-off (17), because it states the intended uses and warns about thinking loops. Training data is a general description without a cut-off (5). The files last changed on 2 March 2026, 248 days before this check, within a year (5). No Qwen member has answered any of the 15 discussions that aren't pull requests (0). Qwen3-0.6B stays in its own repository without a dated note (5), and a template fix and the missing generation_config.json wait unanswered (5). 17+5+5+0+5+5 = 37.
Score by category
| Category | Weight this run | LFM3-Audio-2B | Qwen3.5-0.8B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 80 | even |
| Risk | 20%20 | 40 | 55 | Qwen3.5-0.8B +15 |
| Agent interface | 10%10 | 85 | 60 | LFM3-Audio-2B +25 |
| Documentation and upkeep | 10%10 | 52 | 37 | LFM3-Audio-2B +15 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | D 46.7/100 |
Facts side by side
| Fact | LFM3-Audio-2B | Qwen3.5-0.8B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | Alibaba (Qwen 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) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-03-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (4) |
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.
Qwen3.5-0.8B
A tiny vision-language model for prototypes, fine-tuning and narrow local steps such as routing, tagging or reading an image. It runs in about 1 GB at 4 or 8 bits in six runtimes under plain Apache 2.0. Keep thinking off, since the card warns it loops. The card has no safety results, and Qwen has answered none of its discussions.
Before you call either
LFM3-Audio-2B
- Start it with
llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinjaorollama run lfm3-audio - llama.cpp ships examples/realtime_client.py for live speech with this model
- Prefill the assistant turn with an open think tag to let it reason before a text reply
- The context is 32K
- Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales
- Liquid doesn't recommend it for agentic coding or complex multi-step reasoning
Qwen3.5-0.8B
- Smallest start is
ollama run qwen3.5:0.8b, orqwen3.5:0.8b-q8_0at 1.0 GB - Full precision
vllm serve Qwen/Qwen3.5-0.8B --port 8000 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder - Thinking is off by default; if you turn it on with
"chat_template_kwargs": {"enable_thinking": true}, stream and cut off runaway output, since the card warns of thinking loops, and add--reasoning-parser qwen3in vLLM or SGLang to split the reasoning out - There's no generation_config.json, so pass the card's sampling yourself, for example
temperature=1.0, top_p=1.0, top_k=20, presence_penalty=2.0for text without thinking - Use it for narrow steps or as a fine-tuning base, not as the main agent, as the card itself suggests
- Validate any JSON it returns, since structured output isn't documented
Questions
Which is better for AI agents, LFM3-Audio-2B or Qwen3.5-0.8B?
LFM3-Audio-2B scores 61 (C) on agent readiness against Qwen3.5-0.8B's 46.7 (D), and leads in 2 of 4 scored categories. Qwen3.5-0.8B leads on risk.
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Machine-readable
- This page as Markdown
/compare/lfm3-audio-2b-vs-qwen3-5-0-8b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-audio-2b.json·/api/v1/tools/qwen3-5-0-8b.json - From a terminal
anchor compare lfm3-audio-2b qwen3-5-0-8b(the CLI) - Over MCP
compare_tools {"a": "lfm3-audio-2b", "b": "qwen3-5-0-8b"}at/mcp, no key