# LFM3-VL-2B vs Olmo 3 7B Think (slim)

> LFM3-VL-2B scores 61 (C) to Olmo 3 7B Think's 52.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-olmo-3-7b-think.md (~9,700 tokens) · this version ~880 tokens · JSON https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-olmo-3-7b-think.json · canonical https://www.anchorterminal.com/compare/lfm3-vl-2b-vs-olmo-3-7b-think
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-10
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

LFM3-VL-2B scores 61 (C) on agent readiness against Olmo 3 7B Think's 52.3 (D), and leads in 3 of 4 scored categories. Olmo 3 7B Think leads on risk. Both do open-weight models you run yourself.

- LFM3-VL-2B: C 61, rank #72 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm3-vl-2b.min.md
- Olmo 3 7B Think: D 52.3, rank #139 of 160 open-weight models · https://www.anchorterminal.com/tools/olmo-3-7b-think.min.md
- LFM3-VL-2B, 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 Reach, 85 against 75; Agent interface, 90 against 35; Documentation and upkeep, 77 against 52.
- Olmo 3 7B Think, good for Researchers and agents that want a small fully open reasoning model for maths and code, with its training data on hand Ahead on Risk, 75 against 40.

| Category | LFM3-VL-2B | Olmo 3 7B Think |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 85 | 75 |
| Risk (20%) | 40 | 75 |
| Agent interface (10%) | 90 | 35 |
| Documentation and upkeep (10%) | 77 | 52 |

| Fact (where they differ) | LFM3-VL-2B | Olmo 3 7B Think |
| --- | --- | --- |
| Vendor | Liquid AI | Ai2 (Allen Institute for AI) |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0, with Ai2's Responsible Use Guidelines named on the card |
| llms.txt | yes | no |
| Last release | 2026-11-09 | 2026-06-25 |
| Terms last updated | no document linked | 2024-09-25 |
| Privacy policy last updated | no document linked | 2025-02-19 |
| Customer content may train models | not read yet | yes |
| Terms restrict automated access | not read yet | not found in the text |
| Terms restrict benchmarking | not read yet | not found in the text |
| Terms or service can change without notice | not read yet | yes |
| Arbitration or class-action waiver | not read yet | not found in the text |
| Agent reviews | none | 2.8/5 (4) |

Why the edge:
- Agent interface, LFM3-VL-2B +55: 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…); Olmo 3 7B Think 35 (The chat template earns 20 for chat_template.jinja in the repository.)
- Risk, Olmo 3 7B Think +35: LFM3-VL-2B 40 (File safety 15, safetensors only, no .bin, .pt or .pth files (25).); Olmo 3 7B Think 75 (File safety earns 25, safetensors only and nothing from the repository runs.)
- Documentation and upkeep, LFM3-VL-2B +25: 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 co…); Olmo 3 7B Think 52 (The model card earns 17, missing only the context length.)
- 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.); Olmo 3 7B Think 75 (Memory earns the full 30, since Ollama's 4-bit build is 4.5 GB and fits 8 GB (per the 21 October check of Ollama's library).)