# LFM2.5-2.6B vs Qwen-AgentWorld-35B-A3B (slim)

> Qwen-AgentWorld-35B-A3B scores 52.1 (D) to LFM2.5-2.6B's 50.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b.md (~9,950 tokens) · this version ~780 tokens · JSON https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b.json · canonical https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen-agentworld-35b-a3b
- 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)

Qwen-AgentWorld-35B-A3B scores 52.1 (D) on agent readiness against LFM2.5-2.6B's 50.3 (D), and leads in 1 of 4 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- LFM2.5-2.6B: D 50.3, rank #146 of 160 open-weight models · https://www.anchorterminal.com/tools/lfm2-5-2-6b.min.md
- Qwen-AgentWorld-35B-A3B: D 52.1, rank #141 of 160 open-weight models · https://www.anchorterminal.com/tools/qwen-agentworld-35b-a3b.min.md
- LFM2.5-2.6B, 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 Reach, 90 against 55; Agent interface, 75 against 50; Documentation and upkeep, 57 against 41.
- Qwen-AgentWorld-35B-A3B, good for Agent builders who want to train or test an agent against simulated tools, terminals, browsers and phones on a 128 GB machine Ahead on Risk, 55 against 50.

| Category | LFM2.5-2.6B | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Capability (35%) | pending | pending |
| Reach (25%) | 90 | 55 |
| Risk (20%) | 50 | 55 |
| Agent interface (10%) | 75 | 50 |
| Documentation and upkeep (10%) | 57 | 41 |

| Fact (where they differ) | LFM2.5-2.6B | Qwen-AgentWorld-35B-A3B |
| --- | --- | --- |
| Vendor | Liquid AI | Alibaba (Qwen team) |
| Licence | LFM Open License v1.0 (free commercial use below $10M annual revenue) | Apache-2.0 |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-06-25 |
| Agent reviews | 2.5/5 (4) | none |

Why the edge:
- Reach, LFM2.5-2.6B +35: LFM2.5-2.6B 90 (Memory earns the full 30, since Liquid's own 4-bit GGUF is 1.59 GB and fits 8 GB with room to spare.); Qwen-AgentWorld-35B-A3B 55 (Memory 10, Alibaba's only build is the BF16 safetensors, 69.32 GB, and 69.32 GB × 1.2 = 83.2 GB, above 64 GB and inside the reference machine's GPU share.)
- Agent interface, LFM2.5-2.6B +25: LFM2.5-2.6B 75 (The chat template earns 20 for chat_template.jinja in the repository.); Qwen-AgentWorld-35B-A3B 50 (Chat template 20, chat_template.jinja in the repository, the same as the template in tokenizer_config.json.)
- Documentation and upkeep, LFM2.5-2.6B +16: LFM2.5-2.6B 57 (The model card earns 17, with everything but a knowledge cut-off.); Qwen-AgentWorld-35B-A3B 41 (Model card 11, the card gives intended use (simulating agent environments), architecture, parameters and context length.)
- Risk, Qwen-AgentWorld-35B-A3B +5: LFM2.5-2.6B 50 (File safety earns 25, safetensors and GGUF only, and nothing from the repository runs to load it or build prompts.); Qwen-AgentWorld-35B-A3B 55 (File safety 25, 21 safetensors shards and no pickle or Python files, and config.json has no auto_map.)