Head to head · Open-weight models you run yourself · October 2026 research run
LFM3-VL-2B vs Nemotron 3 Super 120B A12B
Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 1 of 4 scored categories. LFM3-VL-2B leads on reach and documentation and upkeep. 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
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 75
- Documentation and upkeep, 77 against 70
Watch for
vLLM and SGLang support is unreleased, which the card says itself
Best for Agents on a 128 GB unified machine or Blackwell GPUs that want a long-context, tool-calling reasoning model with documented training data
Ahead on
- Risk, 65 against 40
Watch for
The card's serving commands pass --trust-remote-code
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 Super 120B A12B +25
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 Super 120B A12B 65
File safety earns 15, safetensors only but with 10 off because the documented serving commands pass trust-remote-code and the repository ships model and parser code. Provenance earns 15, files in NVIDIA's linked organisation with a hash for every file. Safety evaluation earns 0, since NVIDIA says it evaluated content safety but publishes no results. Agent guidance earns 10, a subcard that names prompt injection and says what safeguards to add, without an instruction hierarchy or concrete mitigations. Licence risk keeps all 15. The security programme earns 10 for the vulnerability submission route on the card and NVIDIA's public bulletins.
Reach LFM3-VL-2B +10
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.
Nemotron 3 Super 120B A12B 75
Memory earns 10, because the smallest 4-bit build, NVIDIA's 80.3 GB NVFP4, needs about 96 GB with the fifth added, which fits the reference machine but not 64 GB, and Ollama's 87 GB build lands in the same band. Runtimes earn the full 25 from released Transformers, vLLM, SGLang, llama.cpp and Ollama. Builds earn 15 for NVIDIA's own FP8 and NVFP4. Terms earn 10, since the Nemotron licence is custom even though it's free for commercial use with no threshold or use policy. Download earns 10, with no gate or account. Hosted earns 5, since NVIDIA's own API serves it.
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 Super 120B A12B 70
The model card earns 20, all seven items with limitations in the linked subcard. Training data earns 20, datasets listed with sources and token counts and the corpora published. Last change earns 20, counting NVIDIA's own inference code for the model, as on Nemotron 3 Ultra and Nano. The repositories last changed with a card edit on 25 August 2026, but NVIDIA's TensorRT-LLM, which the card's serving commands use, changed its Nemotron-H model code on 14 October, 18 days ago. Responsiveness earns 0, with no NVIDIA reply in any of the nine discussions, re-counted on 1 November. Version policy earns 5 as a first release. File health earns 5, since a generation report waits without a reply and one card link is dead. 20+20+20+0+5+5 = 70.
Score by category
| Category | Weight this run | LFM3-VL-2B | Nemotron 3 Super 120B A12B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 85 | 75 | LFM3-VL-2B +10 |
| Risk | 20%20 | 40 | 65 | Nemotron 3 Super 120B A12B +25 |
| Agent interface | 10%10 | 90 | 90 | even |
| Documentation and upkeep | 10%10 | 77 | 70 | LFM3-VL-2B +7 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | B 66.7/100 |
Facts side by side
| Fact | LFM3-VL-2B | Nemotron 3 Super 120B A12B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | NVIDIA |
| 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) | NVIDIA Nemotron Open Model License |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2026-03-11 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 2.8/5 (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.
Nemotron 3 Super 120B A12B
A long-context agent cousin with trained parallel tool calls, a reasoning switch and a full account of its training data, under a free commercial licence. It needs about 100 GB of GPU memory, a 128 GB unified machine or Blackwell hardware for NVIDIA's NVFP4. The main risks are serving commands that run repository code and no published safety results.
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
Nemotron 3 Super 120B A12B
- On the reference machine start it with
ollama run nemotron-3-super, the 87 GB q4_K_M build, the only one that fits (about 104 GB with the margin) - Raise the Strix Halo GPU memory limit first with
amd-ttm --set 100, since the default of about 62.8 GiB is far below what it needs - With vLLM serve it with
--reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder(SGLang uses--reasoning-parser nemotron_3) - Use
temperature=1.0andtop_p=0.95for every task, as the card says - Thinking is on by default. Pass
enable_thinking: falseorlow_effort: trueinchat_template_kwargsfor short tasks - For coding agents pass
force_nonempty_content: trueinchat_template_kwargs, so a cut-off reasoning trace still returns content - Raise context past 262,144 only with the card's flags (
VLLM_ALLOW_LONG_MAX_MODEL_LEN=1,--max-model-len 1048576)
Questions
Which is better for AI agents, LFM3-VL-2B or Nemotron 3 Super 120B A12B?
Nemotron 3 Super 120B A12B scores 66.7 (B) on agent readiness against LFM3-VL-2B's 61 (C), and leads in 1 of 4 scored categories. LFM3-VL-2B leads on reach and documentation and upkeep.
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Machine-readable
- This page as Markdown
/compare/lfm3-vl-2b-vs-nemotron-3-super-120b-a12b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm3-vl-2b.json·/api/v1/tools/nemotron-3-super-120b-a12b.json - From a terminal
anchor compare lfm3-vl-2b nemotron-3-super-120b-a12b(the CLI) - Over MCP
compare_tools {"a": "lfm3-vl-2b", "b": "nemotron-3-super-120b-a12b"}at/mcp, no key