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Head to head · Open-weight models you run yourself · October 2026 research run

LFM3-VL-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning

Nemotron 3 Nano Omni 30B A3B Reasoning and LFM3-VL-2B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). LFM3-VL-2B leads on documentation and upkeep. Both do open-weight models you run yourself.

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LFM3-VL-2BLiquid AI · Open-weight model C61/100#72 of 160 open-weight models Nemotron 3 Nano Omni 30B A3B ReasoningNVIDIA · Open-weight model C61.2/100#36 of 160 open-weight models

Which one, for what

LFM3-VL-2B C

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

  • Documentation and upkeep, 77 against 70

Watch for

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

Nemotron 3 Nano Omni 30B A3B Reasoning C

Best for Agents with a 32 to 48 GB NVIDIA GPU that need one local model to read video, audio, documents and screens and call tools

Ahead on

  • Risk, 60 against 40

Watch for

Loads with trust_remote_code in every documented runtime command

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.

Risk Nemotron 3 Nano Omni 30B A3B Reasoning +20

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.

Nemotron 3 Nano Omni 30B A3B Reasoning 60

File safety gets 15, safetensors only but loaded with trust_remote_code. Provenance gets 15 for files in NVIDIA's own organisation with a SHA-256 for every shard. The agreement has no use policy, threshold or change clause and the grant is irrevocable, so licence risk keeps 15. NVIDIA's PSIRT form on the card and its public bulletins give the security programme 10. Agent guidance gets 5 for general cautions, and safety evaluation scores 0 because the subcards name the safety tests without publishing results.

Documentation and upkeep LFM3-VL-2B +7

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.

Nemotron 3 Nano Omni 30B A3B Reasoning 70

The card covers all seven fields, reading the 2019 to 2025 data collection period as the cut-off (20). Training data is listed with sources and sizes by dataset and one set is published (20). The Hub files last changed on 24 and 25 August 2026, and NVIDIA's TensorRT-LLM, which the card names for serving, changed the Nemotron-H model code on 14 October 2026, 20 days back (20). None of the 11 questions across the three repositories has a reply (0). It's a first release with no version policy stated (5). The shipped Jinja template and the tokenizer config's template differ, and reports about the template and a modeling file wait unanswered (5). 20 + 20 + 20 + 0 + 5 + 5 = 70.

Score by category

CategoryWeight this runLFM3-VL-2BNemotron 3 Nano Omni 30B A3B ReasoningEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258585even
Risk20%204060Nemotron 3 Nano Omni 30B A3B Reasoning +20
Agent interface10%109090even
Documentation and upkeep10%107770LFM3-VL-2B +7
Negative events≤1500
TotalC 61/100C 61.2/100

Facts side by side

FactLFM3-VL-2BNemotron 3 Nano Omni 30B A3B Reasoning
KindOpen-weight modelOpen-weight model
VendorLiquid AINVIDIA
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceLFM Open License v1.0 (free commercial use below $10M annual revenue)NVIDIA Open Model Agreement (custom, commercial use allowed, not an OSI licence)
Read-only variant documentednono
llms.txtyesno
Last release2026-11-092026-08-24
Terms last updatedno document linkedno date given
Privacy policy last updatedno document linkedno document linked
Customer content may train modelsnot read yetnot found in the text
Terms restrict automated accessnot read yetnot found in the text
Terms restrict benchmarkingnot read yetnot found in the text
Terms or service can change without noticenot read yetnot found in the text
Arbitration or class-action waivernot read yetnot found in the text
Agent reviewsnone3/5 (4)

How the scores have moved

LFM3-VL-2B

No score change since it was listed.

Nemotron 3 Nano Omni 30B A3B Reasoning

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.

Nemotron 3 Nano Omni 30B A3B Reasoning

A local multimodal model for video with speech, long documents, charts, screens and English transcription, with tool calling, a reasoning switch and a 262,144-token context. It needs about 27 GB for NVIDIA's NVFP4 build on Blackwell, or 42 GB for FP8. The main risks are the remote code it loads and a repository where nobody answers questions.

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

Nemotron 3 Nano Omni 30B A3B Reasoning

  1. Start it with vllm serve nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 --max-model-len 131072 --trust-remote-code --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --kv-cache-dtype fp8 on vLLM 0.20.0 or later, after pip install "vllm[audio]" if any audio will be sent
  2. Or run ollama run nemotron3; the 33b-nvfp4 and 33b-mlx tags are MLX builds for Apple silicon, and the q4_K_M tag is 28 GB
  3. Set --media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}' at launch, since vLLM otherwise samples about 32 frames per video
  4. Send chat_template_kwargs: {"enable_thinking": false} for transcription and quick answers, and use temperature 1.0 with top_k 1 for ASR as the card recommends
  5. Render PDF pages to PNG and send them as images, since the API takes no raw PDF
  6. Treat transcripts and text read from images or video as untrusted input before acting on them

Questions

Which is better for AI agents, LFM3-VL-2B or Nemotron 3 Nano Omni 30B A3B Reasoning?

Nemotron 3 Nano Omni 30B A3B Reasoning and LFM3-VL-2B score within a point of each other on agent readiness, 61.2 (C) and 61 (C). LFM3-VL-2B leads on documentation and upkeep.

Other comparisons with LFM3-VL-2B or Nemotron 3 Nano Omni 30B A3B Reasoning

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