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

GLM-5.3-Flash vs LFM3-Audio-2B

GLM-5.3-Flash scores 65.1 (B) on agent readiness against LFM3-Audio-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Audio-2B leads on reach and agent interface. 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

GLM-5.3-Flash B

Best for Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider

Ahead on

  • Risk, 55 against 40
  • Documentation and upkeep, 58 against 52

Watch for

Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists

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

  • Reach, 80 against 60
  • Agent interface, 85 against 70

Watch for

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

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.

Reach LFM3-Audio-2B +20

GLM-5.3-Flash 60

Memory earns nothing, since Z.ai's smallest build is the 328.4 GB FP8 one, about 394 GB with the fifth added, and neither Ollama nor LM Studio has a local build (both list it as cloud only). Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio don't. Z.ai publishes one quantised build, FP8, for 10. The MIT licence with nothing added gives the full 15, the files download with no account or gate for 10, and Z.ai's own API plus six providers on Hugging Face serve it, for 5.

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.

Risk GLM-5.3-Flash +15

GLM-5.3-Flash 55

File safety is full, with safetensors only, no Python and no remote code needed in Transformers 5.16.1 or later. Provenance is full, files in the zai-org organisation Z.ai links from GitHub with a SHA-256 for every shard. Safety evaluation and agent guidance are 0, as nothing is published on either. Licence risk is the full 15, since MIT adds no policy, threshold or changeable terms. The security programme is 0, with no security.txt on z.ai or docs.z.ai, no SECURITY.md on GitHub and no advisories found.

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.

Agent interface LFM3-Audio-2B +15

GLM-5.3-Flash 70

A Jinja chat template sits in both repositories, for 20. The tool-calling format is 10, because it is defined only by the template and the glm47 parser is named by vLLM and SGLang rather than by Z.ai. The template takes several calls in one turn, for 20. Structured output is 0, with nothing on the card and only the hosted API documented for JSON. Reasoning control is 10, a per-request budget through reasoning_effort, with the reasoning coming back in a think block, though thinking can't be turned off. Context is 10, 1,048,576 tokens native with no rotary scaling.

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.

Documentation and upkeep GLM-5.3-Flash +6

GLM-5.3-Flash 58

The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 5, a 30T-token corpus described with no sources or cut-off. The last change, a chat template speed-up on 15 October in both repositories, is within 30 days on 2 November, for 20; Z.ai's GLM-5 GitHub repository has changed only its docs since. Responsiveness is 10, as zai-org answered 14 of the 38 recent discussions others opened (26 August to 30 October), the last on 28 September, which is some but not most. Version policy is 5 for a first release, and file health is the full 10, with files and card agreeing and no broken-file report waiting as far as the research record shows.

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.

Score by category

CategoryWeight this runGLM-5.3-FlashLFM3-Audio-2BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256080LFM3-Audio-2B +20
Risk20%205540GLM-5.3-Flash +15
Agent interface10%107085LFM3-Audio-2B +15
Documentation and upkeep10%105852GLM-5.3-Flash +6
Negative events≤1500
TotalB 65.1/100C 61/100

Facts side by side

FactGLM-5.3-FlashLFM3-Audio-2B
KindOpen-weight modelOpen-weight model
VendorZ.aiLiquid AI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITLFM Open License v1.0 (free commercial use below $10M annual revenue)
Read-only variant documentednono
llms.txtnoyes
Last release2026-08-262026-11-03
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Agent reviews2.8/5 (4)none

Verdicts

GLM-5.3-Flash

An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw.

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.

Before you call either

GLM-5.3-Flash

  1. Start it with vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47 (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe)
  2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs
  3. Pass chat_template_kwargs: {"reasoning_effort": "low"} for short tasks; anything other than low or high falls back to max
  4. Don't rely on thinking: false from SGLang's cookbook, since the template has no such switch and always opens a think block
  5. Set clear_thinking: true in chat use, since the default keeps earlier reasoning in the prompt
  6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai

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

Questions

Which is better for AI agents, GLM-5.3-Flash or LFM3-Audio-2B?

GLM-5.3-Flash scores 65.1 (B) on agent readiness against LFM3-Audio-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Audio-2B leads on reach and agent interface.

Other comparisons with GLM-5.3-Flash or LFM3-Audio-2B

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