# LFM2.5-2.6B vs Qwen3.5-397B-A17B

> Qwen3.5-397B-A17B scores 64.6 (B) 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-qwen3-5-397b-a17b
- Markdown: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.md (~6,250 tokens)
- Slim: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.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

Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 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: 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
- Qwen3.5-397B-A17B: grade B, 64.6/100, rank #18 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-397b-a17b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.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:
- Reach, 95 against 70
- Agent interface, 75 against 70
- Documentation and upkeep, 57 against 39

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

### Qwen3.5-397B-A17B (B)

Good for: Agents with access to an 8-GPU server, or calling a hosted endpoint, that want the strongest open Qwen3.5 with vision and a plain licence.

Ahead on:
- Capability, 72 against 13
- Risk, 55 against 50

Watch for: No build fits a 256 GB machine, and Ollama has no local build


## Score by category

| Category | Weight | LFM2.5-2.6B | Qwen3.5-397B-A17B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 13 | 72 | Qwen3.5-397B-A17B +59 |
| Reach | 25% (25 this run) | 95 | 70 | LFM2.5-2.6B +25 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3.5-397B-A17B +5 |
| Agent interface | 10% (10 this run) | 75 | 70 | LFM2.5-2.6B +5 |
| Documentation and upkeep | 10% (10 this run) | 57 | 39 | LFM2.5-2.6B +18 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **51.5 · D** | **64.6 · B** | |

## Facts side by side

| Fact | LFM2.5-2.6B | Qwen3.5-397B-A17B |
| --- | --- | --- |
| 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 | 2026-02-16 |
| 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.

**Qwen3.5-397B-A17B.** Alibaba's biggest open Qwen3.5, a cousin for hard coding, agent and vision work. Even at 4 bits it needs about 270 GB, so most agents will call it through Alibaba's Model Studio or one of the many providers that host it. The main risk is silence, with no safety results, no agent guidance and template reports without a reply.

## 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

### Qwen3.5-397B-A17B

1. The card's server command is `vllm serve Qwen/Qwen3.5-397B-A17B --port 8000 --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, and the FP8 build halves the memory
2. It doesn't fit our 128 GB reference machine at any precision, so call a hosted endpoint instead (Model Studio's Qwen3.5-Plus, or a provider serving Qwen/Qwen3.5-397B-A17B)
3. On Model Studio pass `"enable_thinking": false` directly, while vLLM and SGLang take `"chat_template_kwargs": {"enable_thinking": false}`
4. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without
5. For multi-token prediction in vLLM add `--speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}'`
6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
7. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, LFM2.5-2.6B or Qwen3.5-397B-A17B?

Qwen3.5-397B-A17B scores 64.6 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "lfm2-5-2-6b", "b": "qwen3-5-397b-a17b"}`. From a terminal: `anchor compare lfm2-5-2-6b qwen3-5-397b-a17b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-397b-a17b.json

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- [LFM2.5-2.6B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next.md)
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- [LFM2.5-2.6B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.md)
- [LFM2.5-2.6B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2.md)
- [LFM2.5-2.6B vs SmolLM3-3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b.md)
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- [LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.md)
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- [Ornith-1.5-35B-A3B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-qwen3-5-397b-a17b.md)
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- [Qwen3.5-397B-A17B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwen3-coder-next.md)
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- [Qwen3.5-397B-A17B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-qwopus3-8-27b-flash-v2.md)
- [Qwen3.5-397B-A17B vs SmolLM3-3B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-smollm3-3b.md)
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- [Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-27b.md)
- [Qwen3.5-397B-A17B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-swift-1-5-qwen3-8-flash-next.md)
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