# LFM3-VL-2B vs Qwen3.8-27B

> Qwen3.8-27B scores 68.6 (B) to LFM3-VL-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-vl-2b-vs-qwen3-8-27b
- Markdown: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-27b.md (~9,450 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

Qwen3.8-27B scores 68.6 (B) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-VL-2B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- LFM3-VL-2B: grade C, 61/100, rank #72 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json
- Qwen3.8-27B: grade B, 68.6/100, rank #11 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.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-VL-2B (C)

Good for: Agents on a laptop, phone or robot that need to read screens, documents, images and video from a model under 2 GB at 4 bits

Ahead on:
- Agent interface, 90 against 70
- Documentation and upkeep, 77 against 36

Watch for: vLLM and SGLang support is unreleased, which the card says itself

### Qwen3.8-27B (B)

Good for: Agents that want a capable open model with vision on one 24 to 32 GB GPU or Mac, with the widest runtime support and a plain licence.

Ahead on:
- Reach, 90 against 85
- Risk, 55 against 40

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


## Why the edge

### Documentation and upkeep: LFM3-VL-2B +41

- LFM3-VL-2B (77): Model card 17, Intended use (questions about video, documents, charts and app screens, finding objects, spatial reasoning), a limitation (not recommended for coding, multi-turn conversations or complex reasoning), architecture (language model, 14-layer vision encoder and projector), parameters (1.92B), context (128K) and 16 languages. Training data 5, A general description: the base's 22T tokens of pre-training with the kinds of data added since LFM2, then vision mid-training on image and video data, supervised fine-tuning, vision reinforcement learning and distillation. 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 20, Of the 5 discussions that aren't pull requests (27 to 31 October 2026), 3 have a reply from a LiquidAI member, which is most. 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_vl architecture, the 128,000-token context and the vision settings in processor_config.json. 17 + 5 + 20 + 20 + 5 + 10 = 77.
- Qwen3.8-27B (36): 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 0, not described. Last change 15, the files last changed on 14 August 2026, 79 days before this check, and Qwen's GitHub repository for the model 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 27B models stay in their own repositories without dated notes. File health 5, the files agree with the card but reports about the template and structured output wait without a reply.

### Agent interface: LFM3-VL-2B +20

- LFM3-VL-2B (90): Chat template 20, chat_template.jinja in the repository (12,129 characters), with roles, images and video, 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 its fine-tuning mix included tool calling and agentic traces. Structured output 5, the card's vLLM and SGLang commands set a structured-output backend and its fine-tuning table has a JSON extraction notebook, but nothing says how to ask this model for JSON. Liquid's API guide documents `response_format` with lfm3-1.7b as its example. Reasoning control 10, Liquid's chat-template page documents `enable_thinking=False` in apply_chat_template for LFM3, with reasoning on by default inside think tags, and the card documents `preserve_thinking` and reports a no-thinking configuration in its benchmarks. Context 10, 128K tokens of input and output per the card, and max_position_embeddings 128000 for the language model in config.json. 20 + 25 + 20 + 5 + 10 + 10 = 90.
- Qwen3.8-27B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, the format lives only in the template and the parser name only in serving commands, Alibaba doesn't describe how tools are declared or returned. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking turns off per request, reasoning_effort budgets it and the card shows the reasoning coming back in reasoning_content. Context 10, 262,144 tokens native.

### Risk: Qwen3.8-27B +15

- LFM3-VL-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.
- Qwen3.8-27B (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 checksum. Safety evaluation 0, none published. 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, disclosure policy or security contact for Qwen was found, and Alibaba's group security centre couldn't be checked, so it's scored as absent.

### Reach: Qwen3.8-27B +5

- LFM3-VL-2B (85): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.20 GB projector file, and 1.30 GB × 1.2 = 1.6 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 15, Released llama.cpp has the lfm3 architecture and the LFM3-VL projector (src/models/lfm3.cpp and tools/mtmd at b12242), Ollama's library has an lfm3-vl entry, and LM Studio's catalogue lists liquid/lfm3-vl-2b. 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-vl-2b among its models, so the publisher hosts it. 30 + 15 + 15 + 10 + 10 + 5 = 85.
- Qwen3.8-27B (90): Memory 25 of 30, Ollama's smallest 4-bit build is 18 GB, or 21.6 GB with a fifth added, which fits 24 GB. Runtimes 25, released versions of llama.cpp, Ollama, vLLM, SGLang 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, seven inference providers are live for it on Hugging Face on 1 November, though Qwen Cloud's own version is still described as coming soon.

## Score by category

| Category | Weight | LFM3-VL-2B | Qwen3.8-27B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 85 | 90 | Qwen3.8-27B +5 |
| Risk | 20% (20 this run) | 40 | 55 | Qwen3.8-27B +15 |
| Agent interface | 10% (10 this run) | 90 | 70 | LFM3-VL-2B +20 |
| Documentation and upkeep | 10% (10 this run) | 77 | 36 | LFM3-VL-2B +41 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **68.6 · B** | |

## Facts side by side

| Fact | LFM3-VL-2B | Qwen3.8-27B |
| --- | --- | --- |
| 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 | 2026-11-09 | 2026-08-14 |
| 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-VL-2B

No score change since it was listed.

### Qwen3.8-27B

- +4.9 on 21 October 2026: checklist

## Verdicts

**LFM3-VL-2B.** A 1.92B vision model that reads images and video on an 8 GB machine, with a documented tool-call format and thinking. It runs today in llama.cpp, Ollama and LM Studio, while vLLM and SGLang support is still unreleased. Transformers needs repository code, and there are no safety results, security contact or data sources.

**Qwen3.8-27B.** A dense cousin for coding and agent work with image and video input that runs almost anywhere. Ollama's 4-bit build needs about 22 GB, and seven providers host it for agents without the hardware. The main risk is that nobody at Alibaba answers, there are no safety results or agent guidance, and the tool format is only in the template.

## Before you call either

### LFM3-VL-2B

1. Start it with `llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-vl`, with llama.cpp b11960 or later
2. Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card
3. Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg
4. Don't plan on vLLM or SGLang until a release carries LFM3-VL
5. Validate JSON output yourself
6. Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning

### Qwen3.8-27B

1. Quickest start is `ollama run qwen3.8`, about 18 GB of files
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3.8-27B --max-model-len 131072 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, since FP8 on gfx1151 isn't documented
3. Use the qwen3_coder tool parser, SGLang's cookbook says the Hermes parser leaves its calls unparsed
4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks, and read the reasoning from `reasoning_content`
5. `reasoning_effort` takes xhigh (default), medium or low, and any other value makes the template raise an error
6. Sampling per the card is temperature 1.0 and top_p 0.95 when thinking, temperature 0.7, top_p 0.8 and presence_penalty 1.5 without
7. Preserved thinking keeps earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions
8. Turn on YaRN only beyond 262,144 tokens, the card warns static YaRN can cost quality on short texts
9. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, LFM3-VL-2B or Qwen3.8-27B?

Qwen3.8-27B scores 68.6 (B) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 2 of 4 scored categories. LFM3-VL-2B leads on agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-qwen3-8-27b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lfm3-vl-2b", "b": "qwen3-8-27b"}`. From a terminal: `anchor compare lfm3-vl-2b qwen3-8-27b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json

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- [Qwen3.8-27B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-ripple-65b-preview.md)
- [Qwen3.8-27B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-sensenova-loop-9b-preview.md)
- [Qwen3.8-27B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-smollm3-3b.md)
- [Qwen3.8-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b.md)
- [Qwen3.8-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-step-3-7-flash.md)
- [Qwen3.8-27B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.8-27B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-swift-1-5-qwen3-8-flash-next.md)
- [Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.md)
- [Qwen3.8-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2.md)
- [Qwen3.8-27B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-2b.md)
- [Qwen3.8-27B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-wevisone-vl-4b.md)
- [Qwen3.8-27B vs ZAYA1-8B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-zaya1-8b.md)

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