Head to head · Open-weight models you run yourself · October 2026 research run
InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-VL-2B
LFM3-VL-2B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview 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
Which one, for what
InternVL3.5-GPT-OSS-20B-A4B-Preview D
Best for Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview.
Ahead on
- Risk, 55 against 40
Watch for
No quantised build from OpenGVLab, so about 51 GB at BF16
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
- Reach, 85 against 65
- Agent interface, 90 against 45
- Documentation and upkeep, 77 against 28
Watch for
vLLM and SGLang support is unreleased, which the card says itself
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-VL-2B +49
InternVL3.5-GPT-OSS-20B-A4B-Preview 28
Model card 8 of 20, the family card misses limitations, context, languages and a cut-off. Training data 5, described without a cut-off. Last change 0, the last commit to either repository is 8 September 2025 and to OpenGVLab's reference code 22 September 2025, over a year before 4 November 2026. Responsiveness 10, OpenGVLab answered 3 of 8 questions across the two repositories, none since September 2025. Version policy 5, a first release. File health 0, the template uses markers the tokenizer doesn't have and contradicts the twin's template, and the card names the wrong language model in its licence line.
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.
Agent interface LFM3-VL-2B +45
InternVL3.5-GPT-OSS-20B-A4B-Preview 45
Chat template 20, a Jinja file in the repository, though it doesn't match the tokenizer. Tool-calling format 10 of 25, only the twin's inherited harmony template mentions tools. Trained tool calling 0, no evidence it was trained to call tools. Structured output 0. Reasoning control 10, a system prompt turns thinking on and the card says the reasoning comes back in think tags. Context 5, trained at 32K tokens.
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.
Reach LFM3-VL-2B +20
InternVL3.5-GPT-OSS-20B-A4B-Preview 65
Memory 15 of 30, the only builds are BF16 at 42.5 GB by the repository's safetensors index, or 51 GB with a fifth added, which fits 64 GB. Runtimes 25, Transformers and vLLM load this repository, and SGLang, llama.cpp and Ollama (through llama.cpp b11963, which Ollama v0.40.14 pins) run the same weights in their GitHub format. Builds 0, OpenGVLab publishes no quantised build. Terms 15, Apache 2.0 with nothing added by the bases. Download 10, not gated. Hosted 0, no provider serves it.
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.
Risk InternVL3.5-GPT-OSS-20B-A4B-Preview +15
InternVL3.5-GPT-OSS-20B-A4B-Preview 55
File safety 25, safetensors only and no code in the repository. Provenance 15, the files are in the OpenGVLab organisation its GitHub links, with a checksum on every file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 over Apache and MIT bases, with gpt-oss's usage policy asking only for lawful use. Security programme 0, no policy, security.txt or advisories.
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.
Score by category
| Category | Weight this run | InternVL3.5-GPT-OSS-20B-A4B-Preview | LFM3-VL-2B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 85 | LFM3-VL-2B +20 |
| Risk | 20%20 | 55 | 40 | InternVL3.5-GPT-OSS-20B-A4B-Preview +15 |
| Agent interface | 10%10 | 45 | 90 | LFM3-VL-2B +45 |
| Documentation and upkeep | 10%10 | 28 | 77 | LFM3-VL-2B +49 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 53.2/100 | C 61/100 |
Facts side by side
| Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | LFM3-VL-2B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenGVLab (Shanghai AI Laboratory) | Liquid AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2025-09-08 | 2026-11-09 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 1.8/5 (4) | none |
How the scores have moved
LFM3-VL-2B
No score change since it was listed.
Verdicts
InternVL3.5-GPT-OSS-20B-A4B-Preview
An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results.
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.
Before you call either
InternVL3.5-GPT-OSS-20B-A4B-Preview
- Serve with
vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768, which is our command; the card gives none for vLLM - Check the rendered prompt before relying on it, since the shipped template's <|im_start|> markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative
- Use Transformers 4.55.0 or later, as the card requires for this size
- For thinking, send the card's thinking system prompt and parse the reasoning out of the <think> tags, with temperature 0.6 and sampling on as the card advises
- Keep inputs within 32K tokens, the length it was trained at, whatever the config says
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
Questions
Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or LFM3-VL-2B?
LFM3-VL-2B scores 61 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 53.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on risk.
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- LFM3-VL-2B vs Nemotron 3 Nano 4B
- LFM3-VL-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- LFM3-VL-2B vs Nemotron 3 Super 120B A12B
- LFM3-VL-2B vs Nemotron 3 Ultra 550B A55B
- LFM3-VL-2B vs North Mini Code 1.0
- LFM3-VL-2B vs occamy-1.0
- LFM3-VL-2B vs Olmo 3 7B Think
- LFM3-VL-2B vs Ornith-1.5-122B
- LFM3-VL-2B vs Ornith-1.5-35B-A3B
- LFM3-VL-2B vs Ornith-1.5-397B
- LFM3-VL-2B vs Ornith-1.5-9B
- LFM3-VL-2B vs OTel-2.0-LLM-31B-IT
- LFM3-VL-2B vs Qwen-AgentWorld-35B-A3B
- LFM3-VL-2B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-VL-2B vs Qwen3-4B-Instruct-2507
- LFM3-VL-2B vs Qwen3.5-0.8B
- LFM3-VL-2B vs Qwen3.5-122B-A10B
- LFM3-VL-2B vs Qwen3.5-2B
- LFM3-VL-2B vs Qwen3.5-397B-A17B
- LFM3-VL-2B vs Qwen3.5-4B
- LFM3-VL-2B vs Qwen3.5-9B
- LFM3-VL-2B vs Qwen3.6-27B
- LFM3-VL-2B vs Qwen3.6-35B-A3B
- LFM3-VL-2B vs Qwen3.8-27B
- LFM3-VL-2B vs Qwen3.8-Flash-Next
- LFM3-VL-2B vs Qwen3-Coder-30B-A3B-Instruct
- LFM3-VL-2B vs Qwen3-Coder-Next
- LFM3-VL-2B vs Qwen3-Omni-30B-A3B-Instruct
- LFM3-VL-2B vs Qwen3-VL-2B-Instruct
- LFM3-VL-2B vs Qwen3-VL-30B-A3B-Instruct
- LFM3-VL-2B vs Qwen3-VL-32B-Instruct
- LFM3-VL-2B vs Qwen3-VL-4B-Instruct
- LFM3-VL-2B vs Qwen3-VL-8B-Instruct
- LFM3-VL-2B vs Qwopus3.8-27B-Flash-V2
- LFM3-VL-2B vs Recursive Flint
- LFM3-VL-2B vs Ripple-65B Preview
- LFM3-VL-2B vs SenseNova-Loop-9B-Preview
- LFM3-VL-2B vs SmolLM3-3B
- LFM3-VL-2B vs Spark-X2.6-4B
- LFM3-VL-2B vs Step 3.7 Flash
- LFM3-VL-2B vs Swift 1.5 Qwen3.8-27B
- LFM3-VL-2B vs Swift 1.5 Qwen3.8-Flash-Next
- LFM3-VL-2B vs ThinkingCap (Qwen3.8 27B)
- LFM3-VL-2B vs Baidu Unlimited-OCR-2
- LFM3-VL-2B vs WeVisOne-VL-2B
- LFM3-VL-2B vs WeVisOne-VL-4B
- LFM3-VL-2B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-vl-2b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json·/api/v1/tools/lfm3-vl-2b.json - From a terminal
anchor compare internvl3-5-gpt-oss-20b-a4b-preview lfm3-vl-2b(the CLI) - Over MCP
compare_tools {"a": "internvl3-5-gpt-oss-20b-a4b-preview", "b": "lfm3-vl-2b"}at/mcp, no key