Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-VL-2B vs Qwopus3.8-27B-Flash-V2
LFM3-VL-2B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on risk. 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 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 60
- Documentation and upkeep, 77 against 51
Watch for
vLLM and SGLang support is unreleased, which the card says itself
Best for Hobbyist and research agents that want a Qwen3.8-27B tuned for shorter reasoning, for local coding loops on a 24 GB GPU.
Ahead on
- Risk, 50 against 40
Watch for
Published from a personal account, with no organisation, security contact or safety evaluation
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.
Agent interface LFM3-VL-2B +30
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.
Qwopus3.8-27B-Flash-V2 60
Chat template 20, a Jinja file in the repository. Tool-calling format 10 of 25, only the template shows the Hermes-style JSON calls, which differ from the base's format. Trained tool calling 20, several calls a turn. Structured output 0, not documented. Reasoning control 0, the switches are documented but not where the reasoning comes back. Context 10, 262,144 tokens from the base.
Documentation and upkeep LFM3-VL-2B +26
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.
Qwopus3.8-27B-Flash-V2 51
Model card 11 of 20, missing architecture, context and a cut-off. Training data 5, a description with no cut-off. Last change 15, the files went up on 21 September 2026, 43 days ago, with no commit since. Responsiveness 10, Jackrong answered 2 of 8 discussions across the model's two repositories. Version policy 5, the first Flash release stays up without a dated note. File health 5, no LICENSE file, an experiment note in the template and open reports of looping and a grammar failure.
Risk Qwopus3.8-27B-Flash-V2 +10
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.
Qwopus3.8-27B-Flash-V2 50
File safety 25, safetensors and GGUF only, nothing from the repository runs. Provenance 5 of 15, the files are on a personal account with no site or organisation behind it, though every file has a checksum. Safety evaluation 0, the card says none was done. Agent guidance 5, general cautions about loops and tool reliability. Licence risk 15, plain Apache 2.0. Security programme 0, no contact or policy.
Score by category
| Category | Weight this run | LFM3-VL-2B | Qwopus3.8-27B-Flash-V2 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 85 | even |
| Risk | 20%20 | 40 | 50 | Qwopus3.8-27B-Flash-V2 +10 |
| Agent interface | 10%10 | 90 | 60 | LFM3-VL-2B +30 |
| Documentation and upkeep | 10%10 | 77 | 51 | LFM3-VL-2B +26 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 61/100 |
Facts side by side
| Fact | LFM3-VL-2B | Qwopus3.8-27B-Flash-V2 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | Jackrong |
| 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 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2026-09-21 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.5/5 (4) |
How the scores have moved
LFM3-VL-2B
No score change since it was listed.
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.
Qwopus3.8-27B-Flash-V2
A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's.
Before you call either
LFM3-VL-2B
- Start it with
llama-server -hf LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --jinjaorollama run lfm3-vl, with llama.cpp b11960 or later - Use temperature 0.1, top_k 50 and repetition penalty 1.1, per the card
- Video is sampled at 2 frames a second up to 768 frames, and each frame costs context. Video input in llama.cpp needs ffmpeg
- Don't plan on vLLM or SGLang until a release carries LFM3-VL
- Validate JSON output yourself
- Liquid doesn't recommend it for coding, multi-turn conversations or complex reasoning
Qwopus3.8-27B-Flash-V2
- Quickest start is
llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja, about 17 GB, and add mmproj-F32.gguf for images - The card's tests used Q5_K_M with the bundled MTP head and
--spec-type draft-mtpin llama.cpp - The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder
- For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0
- reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error
- Earlier reasoning stays in the context unless preserve_thinking is false
- Cap max_tokens, since a report says it loops more than V1
Questions
Which is better for AI agents, LFM3-VL-2B or Qwopus3.8-27B-Flash-V2?
LFM3-VL-2B and Qwopus3.8-27B-Flash-V2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Qwopus3.8-27B-Flash-V2 leads on risk.
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Machine-readable
- This page as Markdown
/compare/lfm3-vl-2b-vs-qwopus3-8-27b-flash-v2.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-vl-2b.json·/api/v1/tools/qwopus3-8-27b-flash-v2.json - From a terminal
anchor compare lfm3-vl-2b qwopus3-8-27b-flash-v2(the CLI) - Over MCP
compare_tools {"a": "lfm3-vl-2b", "b": "qwopus3-8-27b-flash-v2"}at/mcp, no key