LFM3-Audio-2B vs Qwen3.6-35B-A3B

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.

Qwen3.6-35B-A3B 31

The model card earns 11, covering intended use, architecture, parameters and context but not limitations, languages or a cut-off. Training data earns 5 for a general description with no cut-off. The last change earns 5, a README edit on 24 April 2026, 192 days before this check, past 180 days and within a year, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness earns 0, with no Qwen reply in the 40 most recent of 62 discussions that aren't pull requests, recounted on 2 November. Version policy earns 5, the earlier version kept in its own repository without a note. File health earns 5, the files agree but reports about tool calling and the template wait unanswered.

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.

Qwen3.6-35B-A3B 55

File safety earns 25 for safetensors only, loaded by the runtimes' own code and a Jinja template with nothing run from the repository. Provenance earns 15, files in the Qwen organisation the publisher links and a hash for every file. Safety evaluation and agent guidance earn 0, since the card has neither safety results nor any guidance on refusals, injection or safeguards. Licence risk keeps all 15, as Apache 2.0 carries none of the four conditions and the late LICENSE file only wrote down what the card already said. The security programme earns 0, with no reporting route found on qwen.ai, the card or the GitHub repository, which is noted as unchecked for Alibaba's group-wide centre.

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.

Qwen3.6-35B-A3B 70

The chat template earns 20 as a Jinja file in the repository. The tool-calling format earns 10, because the card names the qwen3_coder parser but leaves the return format to the template. Trained tool calling earns 20, with several calls to a turn in the template. Structured output earns 0, as JSON output isn't documented for the model. Reasoning control earns 10, thinking off per request with enable_thinking and the reasoning returned in think tags for the qwen3 parser. Context earns 10 for 262,144 tokens native.

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.

Qwen3.6-35B-A3B 85

Memory earns 20 because the smallest 4-bit build from a runtime's own library is Ollama's Q4_K_M at 23 GB, which comes to 27.6 GB with the fifth added, so it needs a 32 GB machine rather than a 24 GB one. Runtimes earn the full 25, since released llama.cpp, Ollama, vLLM, SGLang and Transformers all run the architecture. Builds earn 10 for Alibaba's one quantised build, FP8, with no 4-bit or GGUF from the publisher. Terms earn 15 for plain Apache 2.0, download earns 10 for ungated files, and hosted earns 5 because DeepInfra, Featherless AI and Scaleway serve it through Hugging Face.