Head to head · Open-weight models you run yourself · October 2026 research run
EXAONE 4.5 33B vs LFM3-Audio-2B
EXAONE 4.5 33B and LFM3-Audio-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-Audio-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 6359 open weights comparisons
Which one, for what
Best for Researchers and students working on documents, Korean or multimodal agents who don't need commercial rights
Ahead on
- Risk, 50 against 40
Watch for
Research and education only. No commercial use of the model or its output
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 65
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 +15
EXAONE 4.5 33B 65
Memory 20, the smallest build at 4 bits is LG's IQ4_XS GGUF, 18.03 GB with the 2.57 GB projector, and 20.60 GB × 1.2 = 24.7 GB, within 32 GB. Without the projector, for text only, it would fit 24 GB. Runtimes 15, Released vLLM 0.33.0 has exaone4_5.py, Transformers 5.22.2 has the exaone4_5 model, and llama.cpp b12242 has the architecture, with LG's own GGUF. Three at 5 each. Builds 15, LG publishes GGUF at five quantised precisions, an AWQ build and an FP8 build. Terms 5, Research and educational use only. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and no click-through, though the licence says downloading is acceptance. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API that serves it. 20 + 15 + 15 + 5 + 10 + 0 = 65.
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 EXAONE 4.5 33B +10
EXAONE 4.5 33B 50
File safety 25, safetensors and GGUF only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the LGAI-EXAONE organisation (LG AI Research on the Hub), which the card's GitHub repository shares a name with and the licence in the repository is LG's (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions: the limitation section lists inappropriate, biased, incorrect and outdated answers and tells users not to try to induce harmful output. Licence risk 5, 15 less 5 for field-of-use limits beyond the law (no commercial use, no building competing models) and 5 for terms the licensor can change or revoke (LG may amend the agreement at any time, and the licence is revocable). Security programme 0, no way to report a flaw on the card beyond a support contact, and www.lgresearch.ai has no security.txt. 25 + 15 + 0 + 5 + 5 + 0 = 50.
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 | EXAONE 4.5 33B | LFM3-Audio-2B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 80 | LFM3-Audio-2B +15 |
| Risk | 20%20 | 50 | 40 | EXAONE 4.5 33B +10 |
| Agent interface | 10%10 | 85 | 85 | even |
| Documentation and upkeep | 10%10 | 50 | 52 | LFM3-Audio-2B +2 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | EXAONE 4.5 33B | LFM3-Audio-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | LG AI Research | Liquid AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | EXAONE AI Model License Agreement 1.2 - NC (research and education only) | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-07-06 | 2026-11-03 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
Verdicts
EXAONE 4.5 33B
A well-documented vision-language model with a full set of builds, tool calling, switchable reasoning, a 256K context and a stated knowledge cut-off. The licence decides whether you can use it: research and education only, output included, and LG can change the terms. Nothing answered on the repository 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
EXAONE 4.5 33B
- Don't use it, or anything it writes, commercially. The licence covers the output too
- Serve it with vLLM and
--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser hermes - Set
enable_thinkingfalse for a direct answer. Thinking is on by default - Use temperature 1.0, top_p 0.95 and presence_penalty 1.5 in general, and temperature 0.6 with top_k 20 for documents and Korean
- For llama.cpp, load a projector file from LG's GGUF repository with the weights for image input
- Its knowledge stops at December 2024
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, EXAONE 4.5 33B or LFM3-Audio-2B?
EXAONE 4.5 33B and LFM3-Audio-2B score within a point of each other on agent readiness, 61 (C) and 61 (C). LFM3-Audio-2B leads on reach.
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Machine-readable
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anchor compare exaone-4-5-33b lfm3-audio-2b(the CLI) - Over MCP
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