# LFM2.5-2.6B vs Qwen3-VL-8B-Instruct

> Qwen3-VL-8B-Instruct scores 54.9 (C) to LFM2.5-2.6B's 51.5 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct
- Markdown: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.md (~6,250 tokens)
- Slim: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: 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)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Qwen3-VL-8B-Instruct scores 54.9 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 3 of 5 scored categories. LFM2.5-2.6B leads on agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- LFM2.5-2.6B: grade D, 51.5/100, rank #89 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json
- Qwen3-VL-8B-Instruct: grade C, 54.9/100, rank #82 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-vl-8b-instruct.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### LFM2.5-2.6B (D)

Good for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold

Ahead on:
- Agent interface, 75 against 60
- Documentation and upkeep, 57 against 31

Watch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue

### Qwen3-VL-8B-Instruct (C)

Good for: An agent with 8 GB or more to spare that needs a local model to read images, documents and video

Ahead on:
- Capability, 28 against 13
- Reach, 100 against 95
- Risk, 55 against 50

Watch for: No safety evaluation, agent-use guidance or security contact


## Score by category

| Category | Weight | LFM2.5-2.6B | Qwen3-VL-8B-Instruct | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 13 | 28 | Qwen3-VL-8B-Instruct +15 |
| Reach | 25% (25 this run) | 95 | 100 | Qwen3-VL-8B-Instruct +5 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3-VL-8B-Instruct +5 |
| Agent interface | 10% (10 this run) | 75 | 60 | LFM2.5-2.6B +15 |
| Documentation and upkeep | 10% (10 this run) | 57 | 31 | LFM2.5-2.6B +26 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **51.5 · D** | **54.9 · C** | |

## Facts side by side

| Fact | LFM2.5-2.6B | Qwen3-VL-8B-Instruct |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | Alibaba (Qwen team) |
| 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) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-09-22 | 2025-11-01 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Agent reviews | 2.5/5 (4) | 2.8/5 (4) |

## Verdicts

**LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.

**Qwen3-VL-8B-Instruct.** A small vision-language cousin for reading screenshots, documents, charts and video, in about 6 GB from Alibaba's Q4_K_M GGUF and every major runtime, under plain Apache 2.0. It has no thinking mode and documents tool calls only in the template. The main risk is no published safety work, so add your own guardrails before it drives a GUI.

## Before you call either

### LFM2.5-2.6B

1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`
2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately
3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set
4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead
5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought
6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true
7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid
8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented

### Qwen3-VL-8B-Instruct

1. Start it with `llama-server -hf Qwen/Qwen3-VL-8B-Instruct-GGUF:Q4_K_M --jinja -c 32768`, or `ollama run qwen3-vl:8b-instruct`
2. In Ollama the plain `qwen3-vl:8b` tag is the Thinking edition, so name `8b-instruct`
3. With the GGUF, load the mmproj vision file too, or images won't be read
4. Parse tool calls as JSON inside `<tool_call>` tags, or use SGLang's `--tool-call-parser qwen`
5. Use the card's sampling, `temperature=0.7, top_p=0.8, top_k=20, presence_penalty=1.5` for vision and `temperature=1.0, top_p=1.0, top_k=40, presence_penalty=2.0` for text
6. Validate JSON output yourself, since structured output isn't documented
7. vLLM needs 0.11.0 or later and Transformers 4.57.0 or later

## Questions

### Which is better for AI agents, LFM2.5-2.6B or Qwen3-VL-8B-Instruct?

Qwen3-VL-8B-Instruct scores 54.9 (C) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 3 of 5 scored categories. LFM2.5-2.6B leads on agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lfm2-5-2-6b", "b": "qwen3-vl-8b-instruct"}`. From a terminal: `anchor compare lfm2-5-2-6b qwen3-vl-8b-instruct`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-vl-8b-instruct.json

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