# LFM3-Omni-2B vs Qwen3.6-27B

> Qwen3.6-27B scores 66.7 (B) to LFM3-Omni-2B's 61 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-27b
- Markdown: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-27b.md (~9,050 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

Qwen3.6-27B scores 66.7 (B) on agent readiness against LFM3-Omni-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Omni-2B leads on documentation and upkeep. Both do open-weight models you run yourself.

- LFM3-Omni-2B: grade C, 61/100, rank #71 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-omni-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-omni-2b.json
- Qwen3.6-27B: grade B, 66.7/100, rank #16 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-6-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### LFM3-Omni-2B (C)

Good for: Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools

Ahead on:
- Documentation and upkeep, 49 against 31

Watch for: Only llama.cpp and Ollama run it in a release

### Qwen3.6-27B (B)

Good for: Agents that want an open coding and tool-calling model with vision on one 24 to 32 GB GPU or Mac, with plain licence terms.

Ahead on:
- Reach, 90 against 80
- Risk, 55 against 40

Watch for: No safety results, prompt-injection guidance or security contact


## Why the edge

### Documentation and upkeep: LFM3-Omni-2B +18

- 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.
- Qwen3.6-27B (31): Model card 11 of 20, intended use, architecture, parameters and context are there, but limitations, languages and a knowledge cut-off aren't. Training data 5, only a general family description without a cut-off, in the GitHub README. Last change 5, the files last changed on 24 April 2026, 191 days before this check, and Qwen's GitHub repository holds only a README and licence, so no reference code of Alibaba's changed later. Responsiveness 0, Qwen answered none of the 40 most recent questions and reports, recounted on 1 November. Version policy 5, earlier and later 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but template and tool-calling reports wait without a reply.

### Risk: Qwen3.6-27B +15

- 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.
- Qwen3.6-27B (55): File safety 25, safetensors only and nothing from the repository runs to load it or build prompts. Provenance 15, the files are in the Qwen organisation that Qwen's GitHub links to, and every file has a hash on the Hub. Safety evaluation 0, none published on the card or README. Agent guidance 0, nothing on prompt injection, refusals or safeguards. Licence risk 15, plain Apache 2.0 that hasn't changed. Security programme 0, no security.txt, SECURITY.md or reporting route was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.

### Reach: Qwen3.6-27B +10

- 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.
- Qwen3.6-27B (90): Memory 25 of 30, Ollama's text-only 4-bit build is 17 GB and the vision build 18 GB, about 20 to 22 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang, LM Studio and Transformers all run the qwen3_5 architecture. Builds 10 of 15, Alibaba's only quantised build is FP8. Terms 15, Apache 2.0 with nothing added. Download 10, neither repository is gated. Hosted 5, three inference providers are live on Hugging Face and Alibaba's Model Studio serves it per the card.

## Score by category

| Category | Weight | LFM3-Omni-2B | Qwen3.6-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 80 | 90 | Qwen3.6-27B +10 |
| Risk | 20% (20 this run) | 40 | 55 | Qwen3.6-27B +15 |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 49 | 31 | LFM3-Omni-2B +18 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **66.7 · B** | |

## Facts side by side

| Fact | LFM3-Omni-2B | Qwen3.6-27B |
| --- | --- | --- |
| 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 | 2026-11-03 | 2026-04-22 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3/5 (4) |

## How the scores have moved

### LFM3-Omni-2B

No score change since it was listed.

### Qwen3.6-27B

- -0.5 on 1 November 2026: time

## Verdicts

**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.

**Qwen3.6-27B.** A capable dense cousin for agentic coding and tool calls with images and video in, running in every major runtime. Ollama's 4-bit build needs about 22 GB, and three providers host it. The main risk is silence, with no safety results, no agent guidance, no answers in its repository and Qwen3.8-27B now ahead of it.

## Before you call either

### 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

### Qwen3.6-27B

1. Quickest start is `ollama run qwen3.6:27b`, about 18 GB of files with the vision projector
2. Alibaba's FP8 build with the card's parsers is `vllm serve Qwen/Qwen3.6-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, vllm 0.19.0 or later
3. Use the qwen3_coder tool parser, the template writes calls as function and parameter blocks inside tool_call
4. Thinking is on by default, pass `"chat_template_kwargs": {"enable_thinking": false}` for short tasks, and `/think` and `/nothink` aren't supported
5. `"preserve_thinking": true` in chat_template_kwargs keeps earlier reasoning in context, which helps long agent sessions but grows the prompt
6. Sampling per the card is temperature 1.0, top_p 0.95, top_k 20 when thinking, and temperature 0.7, top_p 0.8, presence_penalty 1.5 without
7. Keep at least 128K of context if you rely on thinking, and turn on YaRN only beyond 262,144 tokens
8. Validate any JSON it returns or use the runtime's guided decoding, since structured output isn't documented
9. Qwen3.8-27B (listed as qwen3-8-27b) is the newer model at this size on the same runtimes

## Questions

### Which is better for AI agents, LFM3-Omni-2B or Qwen3.6-27B?

Qwen3.6-27B scores 66.7 (B) on agent readiness against LFM3-Omni-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-Omni-2B leads on documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lfm3-omni-2b", "b": "qwen3-6-27b"}`. From a terminal: `anchor compare lfm3-omni-2b qwen3-6-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-omni-2b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-6-27b.json

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- [Qwen3.6-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-step-3-7-flash.md)
- [Qwen3.6-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.6-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3.6-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2.md)
- [Qwen3.6-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-2b.md)
- [Qwen3.6-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-wevisone-vl-4b.md)
- [Qwen3.6-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-zaya1-8b.md)

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