Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-1.7B vs Qwen3-Omni-30B-A3B-Instruct
LFM3-1.7B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories. Qwen3-Omni-30B-A3B-Instruct leads on risk. 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
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Which one, for what
Best for Agents on a laptop, phone or small server that need tool calling and a long context from a model under 2 GB at 4 bits
Ahead on
- Reach, 85 against 55
- Agent interface, 100 against 60
- Documentation and upkeep, 67 against 26
Watch for
vLLM and SGLang have no released support, though the card lists both
Best for Agents on a machine with about 96 GB to spare that need one local model to hear, see and answer by voice
Ahead on
- Risk, 55 against 40
Watch for
Only a 70.5 GB BF16 build, about 85 GB to run, with no quantised build from Alibaba
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 LFM3-1.7B +41
LFM3-1.7B 67
Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), a limitation (not recommended for agentic coding or complex multi-step reasoning), architecture (24 layers: 13 gated short convolutions, 5 sliding-window and 6 global attention), parameters (1.73B), context (256K) and 16 languages. Training data 5, A general description: 22T tokens of pre-training in two stages with the kinds of data added since LFM2, 1.75T tokens of supervised fine-tuning, then mixed reinforcement learning, distillation from six specialist teachers and agentic reinforcement learning. Last change 20, the last commit, on 9 November 2026, added the OpenVINO checkpoint to the card's inference table, a day before this check. Responsiveness 10, Of the 22 discussions that aren't pull requests (27 October to 8 November 2026), 7 have a reply from a LiquidAI member, which is some. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 10, Config, template and card agree on the lfm3 architecture, the 262,144-token context and the thinking switch. 17 + 5 + 20 + 10 + 5 + 10 = 67.
Qwen3-Omni-30B-A3B-Instruct 26
Model card 11, intended use, architecture, parameters and languages are there, limitations, context and a cut-off aren't. Training data 5, the report's description has no cut-off. Last change 0, nothing that runs the model has changed for over a year. Responsiveness 0, Qwen answered none of the 40 most recent discussions. Version policy 5, Qwen2.5-Omni kept without notes. File health 5, contradictory licence and language metadata and an unanswered report about long audio.
Agent interface LFM3-1.7B +40
LFM3-1.7B 100
Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, tools, deferred tools, tool results and the thinking switch. 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 says the model was trained on tool calls and agentic traces and with reinforcement learning inside agent harnesses. Structured output 15, Liquid's API guide documents response_format with a JSON Schema, with lfm3-1.7b as the model in its examples, and says the schema is checked before generation starts. Reasoning control 10, the card documents enable_thinking=False in the chat template to turn reasoning off per request, with reasoning on by default inside think tags, and preserve_thinking for earlier turns. Context 10, 256K tokens per the card, which says the mid-training stage extended the context window to 256K, and max_position_embeddings 262144 in config.json. 20 + 25 + 20 + 15 + 10 + 10 = 100.
Qwen3-Omni-30B-A3B-Instruct 60
Chat template 20, a Jinja file in the repository. Tool-calling format 10, the cookbook shows how tools are declared and calls come back and the card covers audio, image and video input and speech output, but no parser is named. Trained tool calling 20, two calls in one turn from speech in the cookbook. Structured output 5, speech in a chosen voice and text-only output are documented, with no JSON. Reasoning control 0, no thinking in this edition. Context 5, 65,536 tokens in the config and trained at 32,768.
Reach LFM3-1.7B +30
LFM3-1.7B 85
Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB, and 1.10 GB × 1.2 = 1.3 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture (src/models/lfm3.cpp at b12242), Ollama's library has an lfm3 entry, and LM Studio's catalogue runs the architecture (its liquid/lfm3-vl-2b entry; lmstudio-community publishes a GGUF of this model). 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 lfm3-1.7b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.
Qwen3-Omni-30B-A3B-Instruct 55
Memory earns 10, because the only build is the 70.5 GB BF16 checkpoint, about 85 GB with a fifth added, which fits only the reference machine's band. Runtimes earn 20. Transformers runs the whole model, and released vLLM, SGLang and llama.cpp run its Thinker with text out, while Ollama and LM Studio have nothing. Builds score 0, with no FP8, 4-bit or GGUF build from Alibaba. Apache 2.0 with nothing added gives terms the full 15 and the ungated repository gives download the full 10. Hosted too scores 0, since no provider serves this checkpoint and Alibaba's API serves a different Flash model.
Risk Qwen3-Omni-30B-A3B-Instruct +15
LFM3-1.7B 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-Omni-30B-A3B-Instruct 55
File safety 25, safetensors only and no code in the repository, so the cookbook's trust_remote_code flag runs nothing. Provenance 15, files in the Qwen organisation with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published, though spoken input can trigger tool calls. Licence risk 15, plain Apache 2.0. Security programme 0, no security contact, disclosure policy or advisories for Qwen.
Score by category
| Category | Weight this run | LFM3-1.7B | Qwen3-Omni-30B-A3B-Instruct | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 55 | LFM3-1.7B +30 |
| Risk | 20%20 | 40 | 55 | Qwen3-Omni-30B-A3B-Instruct +15 |
| Agent interface | 10%10 | 100 | 60 | LFM3-1.7B +40 |
| Documentation and upkeep | 10%10 | 67 | 26 | LFM3-1.7B +41 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | E 41.8/100 |
Facts side by side
| Fact | LFM3-1.7B | Qwen3-Omni-30B-A3B-Instruct |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | Alibaba (Qwen team) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2025-09-22 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2/5 (4) |
Verdicts
LFM3-1.7B
A 1.73B model that fits an 8 GB machine with room to spare, with a documented tool-call format, deferred tools, a thinking switch and a 256K context. It runs today in llama.cpp, Ollama and LM Studio only, loads in Transformers through repository code, and has no safety results, security contact or data sources.
Qwen3-Omni-30B-A3B-Instruct
An omni-modal cousin that listens, watches and reads, then answers in text or one of three voices, with tool calls from spoken requests, under plain Apache 2.0. Its only build, BF16, needs about 85 GB, and speech out needs Transformers or vLLM-Omni. The main risk is no answers from Qwen and no safety work for speech-driven actions.
Before you call either
LFM3-1.7B
- Start it with
llama-server -hf LiquidAI/LFM3-1.7B-GGUF:Q4_K_M --jinjaorollama run lfm3 - Use temperature 0.1, top_k 50 and repetition penalty 1.1 for chat, tools and agents, per the card
- Pass
enable_thinking=Falsefor direct answers, and expect it to reason anyway on hard tasks - For a large tool catalogue mark tools
defer_loadingand give it a search tool - Don't plan on vLLM or SGLang until a release carries LFM3
- Liquid doesn't recommend it for agentic coding or complex multi-step reasoning
Qwen3-Omni-30B-A3B-Instruct
- For text answers,
mkdir -p media && vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct --dtype bfloat16 --max-model-len 32768 --allowed-local-media-path "$(pwd)/media"with vLLM 0.11.1 or later serves the Thinker on an OpenAI-compatible API - The card's command passes
--allowed-local-media-path /, which lets anyone who can reach the server have it read any file on the machine through a file:// URL. Point it at a dedicated folder that holds only the media you mean to send, as above, and give vLLM an absolute path - Send audio as
audio_urland images asimage_urlcontent parts; vLLM serve doesn't take use_audio_in_video, so pass a video's audio separately - For speech out, load Qwen3OmniMoeForConditionalGeneration in Transformers 4.57.0 or later with qwen-omni-utils, pick speaker Ethan, Chelsie or Aiden, and write the returned waveform at 24 kHz
- Call model.disable_talker() or pass return_audio=False when you only need text, which saves about 10 GB and time
- Parse tool calls as JSON inside
<tool_call>tags yourself, since no parser is named, and confirm actions triggered by speech before running them - Keep use_audio_in_video the same across every step of a conversation, the card warns results go wrong otherwise
- Budget memory for video, the card's table rises from 78.85 GB for 15 seconds to 144.81 GB for 120 seconds in BF16
Questions
Which is better for AI agents, LFM3-1.7B or Qwen3-Omni-30B-A3B-Instruct?
LFM3-1.7B scores 61 (C) on agent readiness against Qwen3-Omni-30B-A3B-Instruct's 41.8 (E), and leads in 3 of 4 scored categories. Qwen3-Omni-30B-A3B-Instruct leads on risk.
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- Qwen3.6-35B-A3B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.8-27B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3.8-Flash-Next vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Coder-30B-A3B-Instruct vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Coder-Next vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-2B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-32B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-4B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwen3-VL-8B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Qwopus3.8-27B-Flash-V2
- Qwen3-Omni-30B-A3B-Instruct vs Recursive Flint
- Qwen3-Omni-30B-A3B-Instruct vs Ripple-65B Preview
- Qwen3-Omni-30B-A3B-Instruct vs SenseNova-Loop-9B-Preview
- Qwen3-Omni-30B-A3B-Instruct vs SmolLM3-3B
- Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B
- Qwen3-Omni-30B-A3B-Instruct vs Step 3.7 Flash
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-27B
- Qwen3-Omni-30B-A3B-Instruct vs Swift 1.5 Qwen3.8-Flash-Next
- Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-2B
- Qwen3-Omni-30B-A3B-Instruct vs WeVisOne-VL-4B
- Qwen3-Omni-30B-A3B-Instruct vs ZAYA1-8B
- Cohere Transcribe 03-2026 vs Qwen3-Omni-30B-A3B-Instruct
- Granite Speech 5.0 2B vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-asr-tiny vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-audio-flash vs Qwen3-Omni-30B-A3B-Instruct
- Ming-3.0-audio-tiny vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3.5 ASR Streaming 0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3.5 Transcribe 30B-A4B vs Qwen3-Omni-30B-A3B-Instruct
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Qwen3-Omni-30B-A3B-Instruct
- Parakeet TDT 0.6B v3 vs Qwen3-Omni-30B-A3B-Instruct
- Parakeet Unified EN 0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-ASR-0.6B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-ASR-1.7B vs Qwen3-Omni-30B-A3B-Instruct
- Qwen3-Omni-30B-A3B-Instruct vs Underdog Bark 3.0
- Qwen3-Omni-30B-A3B-Instruct vs Voxtral Mini 4B Realtime 2602
Machine-readable
- This page as Markdown
/compare/lfm3-1-7b-vs-qwen3-omni-30b-a3b-instruct.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-1-7b.json·/api/v1/tools/qwen3-omni-30b-a3b-instruct.json - From a terminal
anchor compare lfm3-1-7b qwen3-omni-30b-a3b-instruct(the CLI) - Over MCP
compare_tools {"a": "lfm3-1-7b", "b": "qwen3-omni-30b-a3b-instruct"}at/mcp, no key