# Granite 4.2 3B vs LFM3-VL-2B

> LFM3-VL-2B scores 61 (C) to Granite 4.2 3B's 59.8 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm3-vl-2b
- Markdown: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm3-vl-2b.md (~9,500 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

LFM3-VL-2B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 1 of 4 scored categories. Granite 4.2 3B leads on reach, risk and documentation and upkeep. Both do open-weight models you run yourself.

- Granite 4.2 3B: grade C, 59.8/100, rank #92 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/granite-4-2-3b.md · JSON https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json
- LFM3-VL-2B: grade C, 61/100, rank #72 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-vl-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Granite 4.2 3B (C)

Good for: Agents on small hardware that want a permissive reasoning model for tool calls and coding, with thinking they can switch off

Ahead on:
- Reach, 95 against 85
- Risk, 55 against 40
- Documentation and upkeep, 92 against 77

Watch for: No safety results published for 4.2, and only general safety cautions

### LFM3-VL-2B (C)

Good 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 85

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


## Why the edge

### Risk: Granite 4.2 3B +15

- Granite 4.2 3B (55): File safety gets 15 of 25, safetensors only, less 10 because the card's vLLM command loads the repository's parser plugin. Provenance gets 15 for files in IBM's own organisation with hashes and a sigstore signature. Safety evaluation is 0, since 4.2 publishes no safety results. Agent guidance gets 5 for general cautions and the advice to add Granite Guardian. Apache 2.0 keeps licence risk at 15, and IBM's security.txt gives 5 of the security programme's 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.

### Documentation and upkeep: Granite 4.2 3B +15

- Granite 4.2 3B (92): The card covers everything but a knowledge cut-off (17). IBM's disclosure lists the datasets with sources and sizes (20). The index file changed on 20 October 2026, 15 days before this check, and nothing in IBM's 4.2 repositories or the GitHub repository changed since (20). The two questions asked on IBM's 3B repositories have an IBM reply, and no new thread was opened after 21 October (20). Granite 4.1 3B stays in its own repository with no note (5), and the files agree with each other (10).
- 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.

### Reach: Granite 4.2 3B +10

- Granite 4.2 3B (95): Memory earns the full 30, since IBM's 4-bit MLX build is 2.07 GB and Ollama's Q4_K_M build 2.2 GB, about 2.5 to 2.6 GB with the fifth added. Five named runtimes run the granite architecture in released versions or their own libraries, so runtimes take the cap of 25, though LM Studio has no 4.2 entry. IBM's FP8, FP4, GGUF and MLX builds earn 15. Apache 2.0 with nothing added gives terms 15 and the ungated repositories download 10. Hosted too is 0, since only DeepInfra serves it and watsonx.ai stops at 4.1.
- 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.

### Agent interface: LFM3-VL-2B +5

- Granite 4.2 3B (85): The template ships in the repository (20). The card shows how tools are declared and how calls come back, and names qwen3_coder for vLLM and SGLang (25). The template renders several calls in a turn (20). JSON output isn't documented for 4.2 (0). Thinking turns off or goes to low effort per request, with the reasoning returned apart in SGLang (10), and 131,072 tokens are native (10).
- 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.

## Score by category

| Category | Weight | Granite 4.2 3B | LFM3-VL-2B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 95 | 85 | Granite 4.2 3B +10 |
| Risk | 20% (20 this run) | 55 | 40 | Granite 4.2 3B +15 |
| Agent interface | 10% (10 this run) | 85 | 90 | LFM3-VL-2B +5 |
| Documentation and upkeep | 10% (10 this run) | 92 | 77 | Granite 4.2 3B +15 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **59.8 · C** | **61 · C** | |

## Facts side by side

| Fact | Granite 4.2 3B | LFM3-VL-2B |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | IBM | Liquid AI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | LFM Open License v1.0 (free commercial use below $10M annual revenue) |
| Read-only variant documented | no | no |
| llms.txt | no | yes |
| Last release | 2026-10-20 | 2026-11-09 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.8/5 (4) | none |

## Verdicts

**Granite 4.2 3B.** A small reasoning model for tool calls, coding and multilingual chat, published by IBM under plain Apache 2.0 with signed files and a dataset disclosure, and run by llama.cpp, Ollama, vLLM, SGLang and Transformers from a 2.2 GB Ollama build. Thinking turns off or shortens per request. It has no published safety results and no documented JSON mode.

**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

### Granite 4.2 3B

1. Start it with `ollama run granite4.2:3b`; set options.num_ctx in the request, since Ollama's default context is short and the model takes up to 131,072
2. With vLLM 0.32.0 or later, serve `ibm-granite/granite-4.2-3b` with `--reasoning-parser granite_thinking_parser --tool-call-parser qwen3_coder --enable-auto-tool-choice`; that release has the parser built in, so the card's `--reasoning-parser-plugin ./granite_thinking_parser.py` isn't needed
3. With SGLang 0.5.18 or later, `--reasoning-parser auto --tool-call-parser auto` resolve to IBM's recommended parsers, and the reasoning comes back in reasoning_content
4. Use temperature 1.0 and top_p 0.95 for every mode, as IBM requires
5. Pass `enable_thinking` false for direct answers or `low_effort` true for short reasoning, through chat_template_kwargs on an OpenAI-compatible server
6. Earlier turns' thinking is stripped by default (`truncate_history_thinking`), so pass it false to keep reasoning in multi-turn history
7. For JSON, use the runtime's guided decoding, since IBM documents no JSON mode for 4.2
8. Verify the files with `python -m model_signing verify sigstore` and IBM's identity, per IBM's signature guide

### 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

## Questions

### Which is better for AI agents, Granite 4.2 3B or LFM3-VL-2B?

LFM3-VL-2B scores 61 (C) on agent readiness against Granite 4.2 3B's 59.8 (C), and leads in 1 of 4 scored categories. Granite 4.2 3B leads on reach, risk and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm3-vl-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm3-vl-2b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "granite-4-2-3b", "b": "lfm3-vl-2b"}`. From a terminal: `anchor compare granite-4-2-3b lfm3-vl-2b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/granite-4-2-3b.json and https://www.anchorterminal.com/api/v1/tools/lfm3-vl-2b.json

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- [LFM3-VL-2B vs Ripple-65B Preview](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-ripple-65b-preview.md)
- [LFM3-VL-2B vs SenseNova-Loop-9B-Preview](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-sensenova-loop-9b-preview.md)
- [LFM3-VL-2B vs SmolLM3-3B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-smollm3-3b.md)
- [LFM3-VL-2B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-spark-x2-6-4b.md)
- [LFM3-VL-2B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-step-3-7-flash.md)
- [LFM3-VL-2B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-27b.md)
- [LFM3-VL-2B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-swift-1-5-qwen3-8-flash-next.md)
- [LFM3-VL-2B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-thinkingcap-qwen3-8-27b.md)
- [LFM3-VL-2B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-unlimited-ocr-2.md)
- [LFM3-VL-2B vs WeVisOne-VL-2B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-2b.md)
- [LFM3-VL-2B vs WeVisOne-VL-4B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-wevisone-vl-4b.md)
- [LFM3-VL-2B vs ZAYA1-8B](https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-zaya1-8b.md)

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