Head to head · Open-weight models you run yourself · October 2026 research run
InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM3-1.7B
LFM3-1.7B 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 small server that need tool calling and a long context from a model under 2 GB at 4 bits
Ahead on
- Reach, 85 against 65
- Agent interface, 100 against 45
- Documentation and upkeep, 67 against 28
Watch for
vLLM and SGLang have no released support, though the card lists both
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 LFM3-1.7B +55
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.
LFM3-1.7B 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.
Documentation and upkeep LFM3-1.7B +39
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.
LFM3-1.7B 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.
Reach LFM3-1.7B +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.
LFM3-1.7B 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.
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.
LFM3-1.7B 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.
Score by category
| Category | Weight this run | InternVL3.5-GPT-OSS-20B-A4B-Preview | LFM3-1.7B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 65 | 85 | LFM3-1.7B +20 |
| Risk | 20%20 | 55 | 40 | InternVL3.5-GPT-OSS-20B-A4B-Preview +15 |
| Agent interface | 10%10 | 45 | 100 | LFM3-1.7B +55 |
| Documentation and upkeep | 10%10 | 28 | 67 | LFM3-1.7B +39 |
| 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 | LFM3-1.7B |
|---|---|---|
| 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
LFM3-1.7B
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.
LFM3-1.7B
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.
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
LFM3-1.7B
- 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
Questions
Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or LFM3-1.7B?
LFM3-1.7B 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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- LFM3-1.7B vs North Mini Code 1.0
- LFM3-1.7B vs occamy-1.0
- LFM3-1.7B vs Olmo 3 7B Think
- LFM3-1.7B vs Ornith-1.5-122B
- LFM3-1.7B vs Ornith-1.5-35B-A3B
- LFM3-1.7B vs Ornith-1.5-397B
- LFM3-1.7B vs Ornith-1.5-9B
- LFM3-1.7B vs OTel-2.0-LLM-31B-IT
- LFM3-1.7B vs Qwen-AgentWorld-35B-A3B
- LFM3-1.7B vs Qwen3-30B-A3B-Instruct-2507
- LFM3-1.7B vs Qwen3-4B-Instruct-2507
- LFM3-1.7B vs Qwen3.5-0.8B
- LFM3-1.7B vs Qwen3.5-122B-A10B
- LFM3-1.7B vs Qwen3.5-2B
- LFM3-1.7B vs Qwen3.5-397B-A17B
- LFM3-1.7B vs Qwen3.5-4B
- LFM3-1.7B vs Qwen3.5-9B
- LFM3-1.7B vs Qwen3.6-27B
- LFM3-1.7B vs Qwen3.6-35B-A3B
- LFM3-1.7B vs Qwen3.8-27B
- LFM3-1.7B vs Qwen3.8-Flash-Next
- LFM3-1.7B vs Qwen3-Coder-30B-A3B-Instruct
- LFM3-1.7B vs Qwen3-Coder-Next
- LFM3-1.7B vs Qwen3-Omni-30B-A3B-Instruct
- LFM3-1.7B vs Qwen3-VL-2B-Instruct
- LFM3-1.7B vs Qwen3-VL-30B-A3B-Instruct
- LFM3-1.7B vs Qwen3-VL-32B-Instruct
- LFM3-1.7B vs Qwen3-VL-4B-Instruct
- LFM3-1.7B vs Qwen3-VL-8B-Instruct
- LFM3-1.7B vs Qwopus3.8-27B-Flash-V2
- LFM3-1.7B vs Recursive Flint
- LFM3-1.7B vs Ripple-65B Preview
- LFM3-1.7B vs SenseNova-Loop-9B-Preview
- LFM3-1.7B vs SmolLM3-3B
- LFM3-1.7B vs Spark-X2.6-4B
- LFM3-1.7B vs Step 3.7 Flash
- LFM3-1.7B vs Swift 1.5 Qwen3.8-27B
- LFM3-1.7B vs Swift 1.5 Qwen3.8-Flash-Next
- LFM3-1.7B vs ThinkingCap (Qwen3.8 27B)
- LFM3-1.7B vs Baidu Unlimited-OCR-2
- LFM3-1.7B vs WeVisOne-VL-2B
- LFM3-1.7B vs WeVisOne-VL-4B
- LFM3-1.7B vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm3-1-7b.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/lfm3-1-7b.json - From a terminal
anchor compare internvl3-5-gpt-oss-20b-a4b-preview lfm3-1-7b(the CLI) - Over MCP
compare_tools {"a": "internvl3-5-gpt-oss-20b-a4b-preview", "b": "lfm3-1-7b"}at/mcp, no key