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

GLM-5.3 vs LFM3-Omni-2B

GLM-5.3 scores 64.6 (B) on agent readiness against LFM3-Omni-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Omni-2B leads on reach. 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 B

Best for Teams with an 8-GPU server who want an open coding and long-horizon agent model with a 1M-token context, or agents that call it through Z.ai or a hosted provider

Ahead on

  • Risk, 50 against 40
  • Documentation and upkeep, 58 against 49

Watch for

Z.ai's smallest build is 755.6 GB in FP8, with no local Ollama or LM Studio build

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

  • Reach, 80 against 55

Watch for

Only llama.cpp and Ollama run it in a release

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-Omni-2B +25

GLM-5.3 55

Memory earns nothing, since Z.ai's smallest build is the 755.6 GB FP8 one, about 907 GB with the fifth added, and Ollama only lists a cloud entry. Four runtimes run it in a released version, llama.cpp, vLLM, SGLang and Transformers, for 20, while Ollama and LM Studio have no local build. Z.ai publishes one quantised build, FP8, for 10, since the BF16 repository is the full precision. The GLM-5.3 License is a custom licence that is free for commercial use below a revenue threshold, for 10. The files download with no account or gate, for 10, and Z.ai's own API plus eight live providers on Hugging Face serve it, for 5.

LFM3-Omni-2B 80

Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 vision and audio projectors and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example), and Ollama's library has an lfm3-omni 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-omni-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.

Risk GLM-5.3 +10

GLM-5.3 50

File safety is full, with safetensors only, no Python in the repository and no remote code needed in Transformers 5.12.0 or later. Provenance is full, files in the zai-org organisation Z.ai links to with a SHA-256 for every shard. Safety evaluation is 0, because the blog mentions safety evaluation and hardening but publishes no results, and agent guidance is 0, with nothing on refusals, prompt injection or cautions. Licence risk is 10, losing 5 for the security review that model-as-a-service operators above US$10 billion of revenue must pass. The security programme is 0, with no security.txt, no SECURITY.md and no advisories.

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.

Documentation and upkeep GLM-5.3 +9

GLM-5.3 58

The card covers intended use, languages and context and misses limitations, architecture, parameters and cut-off, for 8. Training data is 5, a description of the GLM-5 corpus in the cited report with no cut-off. The last change, a chat template speed-up on 14 October in the FP8 repository and on 15 October in the BF16 one, is within 30 days on 2 November, for 20. Responsiveness is 5, as zai-org answered 2 of the 16 discussions that aren't pull requests, the last on 8 September, which is a few, and no discussion has been opened since 10 October. Version policy is the full 10, with each GLM-5 release in its own repository and a dated blog saying what changed from GLM-5.2. File health is the full 10, with files and card agreeing and no broken-file report waiting by title.

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.

Score by category

CategoryWeight this runGLM-5.3LFM3-Omni-2BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255580LFM3-Omni-2B +25
Risk20%205040GLM-5.3 +10
Agent interface10%107070even
Documentation and upkeep10%105849GLM-5.3 +9
Negative events≤1500
TotalB 64.6/100C 61/100

Facts side by side

FactGLM-5.3LFM3-Omni-2B
KindOpen-weight modelOpen-weight model
VendorZ.ai (Zhipu AI)Liquid AI
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceGLM-5.3 License (MIT-style, with a security review for the largest model-as-a-service operators)LFM Open License v1.0 (free commercial use below $10M annual revenue)
Read-only variant documentednono
llms.txtnoyes
Last release2026-10-152026-11-03
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

A large coding and long-horizon agent model with a 1M-token context, several tool calls per turn and three reasoning budgets. Z.ai's smallest build is 756 GB in FP8, so it needs an 8-GPU server, Z.ai's API or a provider. The main risks are no published safety work, no way to report a flaw, and thinking that can't be turned off.

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.

Before you call either

GLM-5.3

  1. Start it with vllm serve zai-org/GLM-5.3 --kv-cache-dtype fp8 --tensor-parallel-size 8 --speculative-config.method mtp --speculative-config.num_speculative_tokens 5 --tool-call-parser glm47 --reasoning-parser glm47 --enable-auto-tool-choice --served-model-name glm-5.3 (vLLM 0.28.0 or later, 8xH200 or 8xH20 per vLLM's recipe)
  2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; SGLang's cookbook says glm45 leaves the calls as plain text
  3. Pass chat_template_kwargs: {"reasoning_effort": "low"} for short tasks; anything other than low or high falls back to max
  4. Pass clear_thinking: true in chat use, since the template keeps earlier reasoning by default
  5. Don't send enable_thinking: false; the template ignores it and always opens a think block
  6. Validate JSON output yourself, since structured output is documented only for Z.ai's hosted API
  7. No Z.ai build fits a 128 GB machine; the full 1M context needs 8xB200 per vLLM's recipe

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

Questions

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

GLM-5.3 scores 64.6 (B) on agent readiness against LFM3-Omni-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Omni-2B leads on reach.

Other comparisons with GLM-5.3 or LFM3-Omni-2B

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