Head to head · Open-weight models you run yourself · October 2026 research run
GLM-4.7-Flash vs LFM3-Audio-2B
GLM-4.7-Flash scores 63 (B) on agent readiness against LFM3-Audio-2B's 61 (C), and leads in 1 of 4 scored categories. LFM3-Audio-2B leads on 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
Best for Agents and developers who want a small, permissively licensed tool-calling model on one 24 to 32 GB GPU or Mac, in almost any runtime
Ahead on
- Risk, 55 against 40
Watch for
No change to the files since 29 January 2026 and no zai-org reply on discussions since 31 January
Best for Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model
Ahead on
- Documentation and upkeep, 52 against 28
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.
Documentation and upkeep LFM3-Audio-2B +24
GLM-4.7-Flash 28
The card covers architecture, parameters and languages and misses four of the seven items, for 8. Training data is 0, as the card describes none. The last change was on 1 February 2026 at the latest, within a year but past 180 days, for 5. Responsiveness is 5, since zai-org answered 6 of the 40 most recent discussions, all by 31 January, which is a few. Version policy is 5 for a first release with no files replaced, and file health is 5, as config.json and tokenizer_config.json disagree on context and a config report waits without a reply.
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.
Risk GLM-4.7-Flash +15
GLM-4.7-Flash 55
File safety is full, with safetensors only and the card loading it through Transformers' own code without remote code. 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, though the repository carries no LICENSE file. The security programme is 0, with no security.txt, no SECURITY file 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.
Score by category
| Category | Weight this run | GLM-4.7-Flash | LFM3-Audio-2B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 80 | 80 | even |
| Risk | 20%20 | 55 | 40 | GLM-4.7-Flash +15 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 28 | 52 | LFM3-Audio-2B +24 |
| Negative events | ≤15 | 0 | 0 | |
| Total | B 63/100 | C 61/100 |
Facts side by side
| Fact | GLM-4.7-Flash | LFM3-Audio-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Z.ai (Zhipu AI) | Liquid AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-01-29 | 2026-11-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.8/5 (4) | none |
Verdicts
GLM-4.7-Flash
A small MIT-licensed mixture-of-experts model with about 3B active parameters, for local agent and coding work with several tool calls per turn and optional thinking. It runs in Ollama at about 23 GB and in nearly every common runtime. The main risk is neglect, with no changes or replies since early February and no safety results.
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-4.7-Flash
- On our reference machine start it with
vllm serve zai-org/GLM-4.7-Flash --max-model-len 65536 --tool-call-parser glm47 --reasoning-parser glm45 --enable-auto-tool-choice --served-model-name glm-4.7-flash - Or run
ollama run glm-4.7-flashfor the 19 GB q4_K_M build - Without the glm47 and glm45 parsers, tool calls and reasoning come back as plain text
- Use a released vLLM (0.32.0 or later) or SGLang (0.5.22 or later); the card's nightly and pull-request builds date from January 2026
- Pass
chat_template_kwargs: {"enable_thinking": false}for short tasks, since thinking is on by default - Set
clear_thinking: falseto keep earlier reasoning in multi-turn agent work, as Z.ai does for τ²-Bench and Terminal Bench - Set the context length yourself, since config.json allows 202,752 tokens and tokenizer_config.json says 128,000
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
Questions
Which is better for AI agents, GLM-4.7-Flash or LFM3-Audio-2B?
GLM-4.7-Flash scores 63 (B) on agent readiness against LFM3-Audio-2B's 61 (C), and leads in 1 of 4 scored categories. LFM3-Audio-2B leads on documentation and upkeep.
Other comparisons with GLM-4.7-Flash or LFM3-Audio-2B
- Agents-A1-4B vs GLM-4.7-Flash
- Agents-A1-4B vs LFM3-Audio-2B
- Apertus v1.5 8B vs GLM-4.7-Flash
- Apertus v1.5 8B vs LFM3-Audio-2B
- Apodex-1.1-mini vs GLM-4.7-Flash
- Apodex-1.1-mini vs LFM3-Audio-2B
- Arete-27B vs GLM-4.7-Flash
- Arete-27B vs LFM3-Audio-2B
- BigBang-v1 vs GLM-4.7-Flash
- BigBang-v1 vs LFM3-Audio-2B
- Carbon-X vs GLM-4.7-Flash
- Carbon-X vs LFM3-Audio-2B
- Cosmos-Reason2-2B vs GLM-4.7-Flash
- Cosmos-Reason2-2B vs LFM3-Audio-2B
- Darwin-36B-Opus vs GLM-4.7-Flash
- Darwin-36B-Opus vs LFM3-Audio-2B
- DeepSeek-V3.2 vs GLM-4.7-Flash
- DeepSeek-V3.2 vs LFM3-Audio-2B
- DeepSeek V4.1 Flash vs GLM-4.7-Flash
- DeepSeek V4.1 Flash vs LFM3-Audio-2B
- DeepSeek-V4-Flash-0731 vs GLM-4.7-Flash
- DeepSeek-V4-Flash-0731 vs LFM3-Audio-2B
- DiffusionGemma 26B A4B IT vs GLM-4.7-Flash
- DiffusionGemma 26B A4B IT vs LFM3-Audio-2B
- Gemma 4 12B IT vs GLM-4.7-Flash
- Gemma 4 12B IT vs LFM3-Audio-2B
- Gemma 4 26B A4B IT vs GLM-4.7-Flash
- Gemma 4 26B A4B IT vs LFM3-Audio-2B
- Gemma 4 31B IT vs GLM-4.7-Flash
- Gemma 4 31B IT vs LFM3-Audio-2B
- Gemma 4 E2B IT vs GLM-4.7-Flash
- Gemma 4 E2B IT vs LFM3-Audio-2B
- Gemma 4 E4B IT vs GLM-4.7-Flash
- Gemma 4 E4B IT vs LFM3-Audio-2B
- GLM-4.5-Air vs GLM-4.7-Flash
- GLM-4.5-Air vs LFM3-Audio-2B
- GLM-4.7-Flash vs GLM-5.3
- GLM-4.7-Flash vs GLM-5.3-Flash
- GLM-4.7-Flash vs gpt-oss-120b
- GLM-4.7-Flash vs gpt-oss-20b
- GLM-4.7-Flash vs Granite 4.2 30B
- GLM-4.7-Flash vs Granite 4.2 3B
- GLM-4.7-Flash vs Granite 4.2 8B
- GLM-4.7-Flash vs Hy-Memory-30B-A3B
- GLM-4.7-Flash vs Hy3
- GLM-4.7-Flash vs Inkling
- GLM-4.7-Flash vs Inkling-Small
- GLM-4.7-Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- GLM-4.7-Flash vs Kimi K3
- GLM-4.7-Flash vs Kimi K3.1 Pro
- GLM-4.7-Flash vs Kolibri-1
- GLM-4.7-Flash vs Kronos
- GLM-4.7-Flash vs Laguna M.1
- GLM-4.7-Flash vs Laguna S 2.1
- GLM-4.7-Flash vs Laguna XS 2.1
- GLM-4.7-Flash vs LBS-27B
- GLM-4.7-Flash vs LFM2.5-2.6B
- GLM-4.7-Flash vs LFM3-1.7B
- GLM-4.7-Flash vs LFM3-Omni-2B
- GLM-4.7-Flash vs LFM3-VL-2B
- GLM-4.7-Flash vs Ling-3.0-tiny
- GLM-4.7-Flash vs Ling-3.1-flash-1019
- GLM-4.7-Flash vs MiMo-V2.6 Distill Qwen 9B
- GLM-4.7-Flash vs MiniCPM-o 4.5
- GLM-4.7-Flash vs MiniCPM-V 4.7 1B
- GLM-4.7-Flash vs MiniCPM-V 4.7 35B-A3B
- GLM-4.7-Flash vs MiniCPM5-2B
- GLM-4.7-Flash vs MiniMax-M2.5
- GLM-4.7-Flash vs MiniMax-M2.7
- GLM-4.7-Flash vs MiniMax-M3.1-Flash
- GLM-4.7-Flash vs Mistral Large 4
- GLM-4.7-Flash vs Muse Glimmer 30B
- GLM-4.7-Flash vs Nanbeige4.3-33B
- GLM-4.7-Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- GLM-4.7-Flash vs Nemotron 3.5 Lightning 30B A3B
- GLM-4.7-Flash vs Nemotron 3 Nano 30B A3B
- GLM-4.7-Flash vs Nemotron 3 Nano 4B
- GLM-4.7-Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning
- GLM-4.7-Flash vs Nemotron 3 Super 120B A12B
- GLM-4.7-Flash vs Nemotron 3 Ultra 550B A55B
- GLM-4.7-Flash vs occamy-1.0
- GLM-4.7-Flash vs Olmo 3 7B Think
- GLM-4.7-Flash vs Ornith-1.5-122B
- GLM-4.7-Flash vs Ornith-1.5-35B-A3B
- GLM-4.7-Flash vs Ornith-1.5-397B
- GLM-4.7-Flash vs Ornith-1.5-9B
- GLM-4.7-Flash vs Qwen-AgentWorld-35B-A3B
- GLM-4.7-Flash vs Qwen3-30B-A3B-Instruct-2507
- GLM-4.7-Flash vs Qwen3-4B-Instruct-2507
- GLM-4.7-Flash vs Qwen3.5-0.8B
- GLM-4.7-Flash vs Qwen3.5-122B-A10B
- GLM-4.7-Flash vs Qwen3.5-2B
- GLM-4.7-Flash vs Qwen3.5-397B-A17B
- GLM-4.7-Flash vs Qwen3.5-4B
- GLM-4.7-Flash vs Qwen3.5-9B
- GLM-4.7-Flash vs Qwen3.6-27B
- GLM-4.7-Flash vs Qwen3.6-35B-A3B
- GLM-4.7-Flash vs Qwen3.8-27B
- GLM-4.7-Flash vs Qwen3.8-Flash-Next
- GLM-4.7-Flash vs Qwen3-Coder-30B-A3B-Instruct
- GLM-4.7-Flash vs Qwen3-Coder-Next
- GLM-4.7-Flash vs Qwen3-Omni-30B-A3B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-2B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-30B-A3B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-32B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-4B-Instruct
- GLM-4.7-Flash vs Qwen3-VL-8B-Instruct
- GLM-4.7-Flash vs Qwopus3.8-27B-Flash-V2
- GLM-4.7-Flash vs Recursive Flint
- GLM-4.7-Flash vs Ripple-65B Preview
- GLM-4.7-Flash vs SmolLM3-3B
- GLM-4.7-Flash vs Spark-X2.6-4B
- GLM-4.7-Flash vs Swift 1.5 Qwen3.8-27B
- GLM-4.7-Flash vs Swift 1.5 Qwen3.8-Flash-Next
- GLM-4.7-Flash vs ThinkingCap (Qwen3.8 27B)
- GLM-4.7-Flash vs Baidu Unlimited-OCR-2
- GLM-5.3-Flash vs LFM3-Audio-2B
- GLM-5.3 vs LFM3-Audio-2B
- gpt-oss-120b vs LFM3-Audio-2B
- gpt-oss-20b vs LFM3-Audio-2B
- Granite 4.2 30B vs LFM3-Audio-2B
- Granite 4.2 3B vs LFM3-Audio-2B
- Granite 4.2 8B vs LFM3-Audio-2B
- Hy-Memory-30B-A3B vs LFM3-Audio-2B
- Hy3 vs LFM3-Audio-2B
- Inkling-Small vs LFM3-Audio-2B
- Inkling vs LFM3-Audio-2B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-Audio-2B
- Kimi K3.1 Pro vs LFM3-Audio-2B
- Kimi K3 vs LFM3-Audio-2B
- Kolibri-1 vs LFM3-Audio-2B
- Kronos vs LFM3-Audio-2B
- Laguna M.1 vs LFM3-Audio-2B
- Laguna S 2.1 vs LFM3-Audio-2B
- Laguna XS 2.1 vs LFM3-Audio-2B
- LBS-27B vs LFM3-Audio-2B
- LFM2.5-2.6B vs LFM3-Audio-2B
- LFM3-1.7B vs LFM3-Audio-2B
- LFM3-Audio-2B vs LFM3-Omni-2B
- LFM3-Audio-2B vs LFM3-VL-2B
- LFM3-Audio-2B vs Ling-3.0-tiny
- LFM3-Audio-2B vs Ling-3.1-flash-1019
- LFM3-Audio-2B vs MiMo-V2.6 Distill Qwen 9B
- LFM3-Audio-2B vs MiniCPM-o 4.5
- LFM3-Audio-2B vs MiniCPM-V 4.7 1B
- LFM3-Audio-2B vs MiniCPM-V 4.7 35B-A3B
- LFM3-Audio-2B vs MiniCPM5-2B
- LFM3-Audio-2B vs MiniMax-M2.5
- LFM3-Audio-2B vs MiniMax-M2.7
- LFM3-Audio-2B vs MiniMax-M3.1-Flash
- LFM3-Audio-2B vs Mistral Large 4
- LFM3-Audio-2B vs Muse Glimmer 30B
- LFM3-Audio-2B vs Nanbeige4.3-33B
- LFM3-Audio-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- LFM3-Audio-2B vs Nemotron 3.5 Lightning 30B A3B
- LFM3-Audio-2B vs Nemotron 3 Nano 30B A3B
- LFM3-Audio-2B vs Nemotron 3 Nano 4B
- LFM3-Audio-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- LFM3-Audio-2B vs Nemotron 3 Super 120B A12B
- LFM3-Audio-2B vs Nemotron 3 Ultra 550B A55B
- LFM3-Audio-2B vs occamy-1.0
- LFM3-Audio-2B vs Olmo 3 7B Think
- LFM3-Audio-2B vs Ornith-1.5-122B
- LFM3-Audio-2B vs Ornith-1.5-35B-A3B
- LFM3-Audio-2B vs Ornith-1.5-397B
- LFM3-Audio-2B vs Ornith-1.5-9B
- LFM3-Audio-2B vs Qwen-AgentWorld-35B-A3B
- LFM3-Audio-2B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-Audio-2B vs Qwen3-4B-Instruct-2507
- LFM3-Audio-2B vs Qwen3.5-0.8B
- LFM3-Audio-2B vs Qwen3.5-122B-A10B
- LFM3-Audio-2B vs Qwen3.5-2B
- LFM3-Audio-2B vs Qwen3.5-397B-A17B
- LFM3-Audio-2B vs Qwen3.5-4B
- LFM3-Audio-2B vs Qwen3.5-9B
- LFM3-Audio-2B vs Qwen3.6-27B
- LFM3-Audio-2B vs Qwen3.6-35B-A3B
- LFM3-Audio-2B vs Qwen3.8-27B
- LFM3-Audio-2B vs Qwen3.8-Flash-Next
- LFM3-Audio-2B vs Qwen3-Coder-30B-A3B-Instruct
- LFM3-Audio-2B vs Qwen3-Coder-Next
- LFM3-Audio-2B vs Qwen3-Omni-30B-A3B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-2B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-30B-A3B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-32B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-4B-Instruct
- LFM3-Audio-2B vs Qwen3-VL-8B-Instruct
- LFM3-Audio-2B vs Qwopus3.8-27B-Flash-V2
- LFM3-Audio-2B vs Recursive Flint
- LFM3-Audio-2B vs Ripple-65B Preview
- LFM3-Audio-2B vs SmolLM3-3B
- LFM3-Audio-2B vs Spark-X2.6-4B
- LFM3-Audio-2B vs Swift 1.5 Qwen3.8-27B
- LFM3-Audio-2B vs Swift 1.5 Qwen3.8-Flash-Next
- LFM3-Audio-2B vs ThinkingCap (Qwen3.8 27B)
- LFM3-Audio-2B vs Baidu Unlimited-OCR-2
Machine-readable
- This page as Markdown
/compare/glm-4-7-flash-vs-lfm3-audio-2b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/glm-4-7-flash.json·/api/v1/tools/lfm3-audio-2b.json - From a terminal
anchor compare glm-4-7-flash lfm3-audio-2b(the CLI) - Over MCP
compare_tools {"a": "glm-4-7-flash", "b": "lfm3-audio-2b"}at/mcp, no key