Head to head · Open-weight models you run yourself · October 2026 research run
LFM2.5-2.6B vs LongCat-2.5
LongCat-2.5 and LFM2.5-2.6B score within a point of each other on agent readiness, 50.5 (D) and 50.3 (D). LFM2.5-2.6B leads on reach and documentation and upkeep. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best 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 35
- Documentation and upkeep, 57 against 46
Watch for
Liquid lists it as deprecated, replaced by LFM3-1.7B; deprecated models stay downloadable but get no further updates
Best for Operators with NPU clusters, and anyone tracking what the open flagships can do
Ahead on
- Risk, 60 against 50
Watch for
3,562 GB in BF16 and 1,336 GB at 4 bits
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach LFM2.5-2.6B +55
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. Runtimes earn 25, with the lfm2 architecture in released llama.cpp, vLLM, SGLang and Transformers, and in Ollama through the llama.cpp version it pins. Builds earn 15 for Liquid's own GGUF, MLX and ONNX builds at many precisions. Terms earn 10, a custom licence that is free for commercial use under $10M of annual revenue. Download earns 10, with no gate. Hosted earns nothing, as Hugging Face maps no inference provider and Liquid's docs list no hosted API for it.
LongCat-2.5 35
Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.
Documentation and upkeep LFM2.5-2.6B +11
LFM2.5-2.6B 57
The model card earns 17, with everything but a knowledge cut-off. Training data earns 5 for a general description without a cut-off. The last change earns 20, a card commit on 3 November 2026 naming LFM3-1.7B as the new version. Responsiveness earns 5, with Liquid replies in 2 of 16 discussions. Version policy earns 5, as Liquid now lists the model as deprecated with a replacement but no date. File health earns 5, since the files agree but a report about the card waits without a reply.
LongCat-2.5 46
Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.
Risk LongCat-2.5 +10
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. Provenance earns 15, files in the LiquidAI organisation Liquid links to, with a checksum for every file. Safety evaluation and agent guidance earn nothing, since Liquid publishes neither. Licence risk earns 10, losing 5 for the revenue threshold, with no use policy and no change since release. The security programme earns nothing, since no security.txt, disclosure policy or security contact was found.
LongCat-2.5 60
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.
Score by category
| Category | Weight this run | LFM2.5-2.6B | LongCat-2.5 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 35 | LFM2.5-2.6B +55 |
| Risk | 20%20 | 50 | 60 | LongCat-2.5 +10 |
| Agent interface | 10%10 | 75 | 75 | even |
| Documentation and upkeep | 10%10 | 57 | 46 | LFM2.5-2.6B +11 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 50.3/100 | D 50.5/100 |
Facts side by side
| Fact | LFM2.5-2.6B | LongCat-2.5 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Liquid AI | Meituan (LongCat) |
| 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) | MIT |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-11-03 | 2026-11-10 |
| Terms last updated | no document linked | not read yet |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 2.5/5 (4) | none |
How the scores have moved
LongCat-2.5
No score change since it was listed.
Verdicts
LFM2.5-2.6B
A small, fast model for tool calls, extraction and long-context steps on a laptop or phone, which Liquid now lists as deprecated in favour of LFM3-1.7B. Liquid's own 4-bit build is 1.6 GB and five common runtimes run it. It always thinks, and organisations over $10M of revenue need another licence. There are no safety results or security contact.
LongCat-2.5
Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.
Before you call either
LFM2.5-2.6B
- Quickest start is
ollama run LiquidAI/lfm2.5-2.6b, 1.7 GB, orllama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF - With vLLM or SGLang pass
--tool-call-parser lfm2; for vLLM add--reasoning-parser qwen3so the thinking comes back separately - Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set
- 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 - Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought
- Earlier thinking is dropped from the prompt unless
preserve_thinkingis true - Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid
- Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented
LongCat-2.5
- You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet
- If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format
- Pass
chat_template_kwargswithenable_thinkingtrue or false - With thinking on, keep earlier reasoning in the history (
save_history_reasoning_contenttrue), as the card recommends - The card's results use temperature 1.0 and top_p 0.95
- Its benchmark table is Meituan's own. The unmarked scores are in-house
Questions
Which is better for AI agents, LFM2.5-2.6B or LongCat-2.5?
LongCat-2.5 and LFM2.5-2.6B score within a point of each other on agent readiness, 50.5 (D) and 50.3 (D). LFM2.5-2.6B leads on reach and documentation and upkeep.
Other comparisons with LFM2.5-2.6B or LongCat-2.5
- Agents-A1-4B vs LFM2.5-2.6B
- Agents-A1-4B vs LongCat-2.5
- Apertus v1.5 8B vs LFM2.5-2.6B
- Apertus v1.5 8B vs LongCat-2.5
- Apodex-1.1-mini vs LFM2.5-2.6B
- Apodex-1.1-mini vs LongCat-2.5
- Arete-27B vs LFM2.5-2.6B
- Arete-27B vs LongCat-2.5
- BigBang-v1 vs LFM2.5-2.6B
- BigBang-v1 vs LongCat-2.5
- Carbon-X vs LFM2.5-2.6B
- Carbon-X vs LongCat-2.5
- Cola2 vs LFM2.5-2.6B
- Cola2 vs LongCat-2.5
- Cosmos-Reason2-2B vs LFM2.5-2.6B
- Cosmos-Reason2-2B vs LongCat-2.5
- Darwin-36B-Opus vs LFM2.5-2.6B
- Darwin-36B-Opus vs LongCat-2.5
- DeepSeek-V3.2 vs LFM2.5-2.6B
- DeepSeek-V3.2 vs LongCat-2.5
- DeepSeek V4.1 Flash vs LFM2.5-2.6B
- DeepSeek V4.1 Flash vs LongCat-2.5
- DeepSeek-V4-Flash-0731 vs LFM2.5-2.6B
- DeepSeek-V4-Flash-0731 vs LongCat-2.5
- Devstral Small 2 vs LFM2.5-2.6B
- Devstral Small 2 vs LongCat-2.5
- DiffusionGemma 26B A4B IT vs LFM2.5-2.6B
- DiffusionGemma 26B A4B IT vs LongCat-2.5
- EXAONE 4.5 33B vs LFM2.5-2.6B
- EXAONE 4.5 33B vs LongCat-2.5
- Gemma 4 12B IT vs LFM2.5-2.6B
- Gemma 4 12B IT vs LongCat-2.5
- Gemma 4 26B A4B IT vs LFM2.5-2.6B
- Gemma 4 26B A4B IT vs LongCat-2.5
- Gemma 4 31B IT vs LFM2.5-2.6B
- Gemma 4 31B IT vs LongCat-2.5
- Gemma 4 E2B IT vs LFM2.5-2.6B
- Gemma 4 E2B IT vs LongCat-2.5
- Gemma 4 E4B IT vs LFM2.5-2.6B
- Gemma 4 E4B IT vs LongCat-2.5
- GLM-4.5-Air vs LFM2.5-2.6B
- GLM-4.5-Air vs LongCat-2.5
- GLM-4.7-Flash vs LFM2.5-2.6B
- GLM-4.7-Flash vs LongCat-2.5
- GLM-5.3-Flash vs LFM2.5-2.6B
- GLM-5.3-Flash vs LongCat-2.5
- GLM-5.3 vs LFM2.5-2.6B
- GLM-5.3 vs LongCat-2.5
- gpt-oss-120b vs LFM2.5-2.6B
- gpt-oss-120b vs LongCat-2.5
- gpt-oss-20b vs LFM2.5-2.6B
- gpt-oss-20b vs LongCat-2.5
- Granite 4.2 30B vs LFM2.5-2.6B
- Granite 4.2 30B vs LongCat-2.5
- Granite 4.2 3B vs LFM2.5-2.6B
- Granite 4.2 3B vs LongCat-2.5
- Granite 4.2 8B vs LFM2.5-2.6B
- Granite 4.2 8B vs LongCat-2.5
- Hy-Memory-30B-A3B vs LFM2.5-2.6B
- Hy-Memory-30B-A3B vs LongCat-2.5
- Hy3 vs LFM2.5-2.6B
- Hy3 vs LongCat-2.5
- Inkling-Small vs LFM2.5-2.6B
- Inkling-Small vs LongCat-2.5
- Inkling vs LFM2.5-2.6B
- Inkling vs LongCat-2.5
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs LongCat-2.5
- Kimi K3.1 Pro vs LFM2.5-2.6B
- Kimi K3.1 Pro vs LongCat-2.5
- Kimi K3 vs LFM2.5-2.6B
- Kimi K3 vs LongCat-2.5
- Kolibri-1 vs LFM2.5-2.6B
- Kolibri-1 vs LongCat-2.5
- Kronos vs LFM2.5-2.6B
- Kronos vs LongCat-2.5
- Laguna M.1 vs LFM2.5-2.6B
- Laguna M.1 vs LongCat-2.5
- Laguna S 2.1 vs LFM2.5-2.6B
- Laguna S 2.1 vs LongCat-2.5
- Laguna XS 2.1 vs LFM2.5-2.6B
- Laguna XS 2.1 vs LongCat-2.5
- LBS-27B vs LFM2.5-2.6B
- LBS-27B vs LongCat-2.5
- LFM2.5-2.6B vs LFM3-1.7B
- LFM2.5-2.6B vs LFM3-Audio-2B
- LFM2.5-2.6B vs LFM3-Omni-2B
- LFM2.5-2.6B vs LFM3-VL-2B
- LFM2.5-2.6B vs Ling-3.0-tiny
- LFM2.5-2.6B vs Ling-3.1-flash-1019
- LFM2.5-2.6B vs MiMo-V2.6 Distill Qwen 9B
- LFM2.5-2.6B vs MiMo-V2.6-Flash-RL
- LFM2.5-2.6B vs MiniCPM-o 4.5
- LFM2.5-2.6B vs MiniCPM-V 4.7 1B
- LFM2.5-2.6B vs MiniCPM-V 4.7 35B-A3B
- LFM2.5-2.6B vs MiniCPM5-2B
- LFM2.5-2.6B vs MiniMax-M2.5
- LFM2.5-2.6B vs MiniMax-M2.7
- LFM2.5-2.6B vs MiniMax-M3.1-Flash
- LFM2.5-2.6B vs Mistral Large 4
- LFM2.5-2.6B vs Muse Glimmer 30B
- LFM2.5-2.6B vs Nanbeige4.2-3B
- LFM2.5-2.6B vs Nanbeige4.3-33B
- LFM2.5-2.6B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B
- LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B
- LFM2.5-2.6B vs Nemotron 3 Nano 4B
- LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning
- LFM2.5-2.6B vs Nemotron 3 Super 120B A12B
- LFM2.5-2.6B vs Nemotron 3 Ultra 550B A55B
- LFM2.5-2.6B vs North Mini Code 1.0
- LFM2.5-2.6B vs occamy-1.0
- LFM2.5-2.6B vs Olmo 3 7B Think
- LFM2.5-2.6B vs Ornith-1.5-122B
- LFM2.5-2.6B vs Ornith-1.5-35B-A3B
- LFM2.5-2.6B vs Ornith-1.5-397B
- LFM2.5-2.6B vs Ornith-1.5-9B
- LFM2.5-2.6B vs OTel-2.0-LLM-31B-IT
- LFM2.5-2.6B vs Qwen-AgentWorld-35B-A3B
- LFM2.5-2.6B vs Qwen3-30B-A3B-Instruct-2507
- LFM2.5-2.6B vs Qwen3-4B-Instruct-2507
- LFM2.5-2.6B vs Qwen3.5-0.8B
- LFM2.5-2.6B vs Qwen3.5-122B-A10B
- LFM2.5-2.6B vs Qwen3.5-2B
- LFM2.5-2.6B vs Qwen3.5-397B-A17B
- LFM2.5-2.6B vs Qwen3.5-4B
- LFM2.5-2.6B vs Qwen3.5-9B
- LFM2.5-2.6B vs Qwen3.6-27B
- LFM2.5-2.6B vs Qwen3.6-35B-A3B
- LFM2.5-2.6B vs Qwen3.8-27B
- LFM2.5-2.6B vs Qwen3.8-Flash-Next
- LFM2.5-2.6B vs Qwen3-Coder-30B-A3B-Instruct
- LFM2.5-2.6B vs Qwen3-Coder-Next
- LFM2.5-2.6B vs Qwen3-Omni-30B-A3B-Instruct
- LFM2.5-2.6B vs Qwen3-VL-2B-Instruct
- LFM2.5-2.6B vs Qwen3-VL-30B-A3B-Instruct
- LFM2.5-2.6B vs Qwen3-VL-32B-Instruct
- LFM2.5-2.6B vs Qwen3-VL-4B-Instruct
- LFM2.5-2.6B vs Qwen3-VL-8B-Instruct
- LFM2.5-2.6B vs Qwopus3.8-27B-Flash-V2
- LFM2.5-2.6B vs Recursive Flint
- LFM2.5-2.6B vs Ripple-65B Preview
- LFM2.5-2.6B vs SenseNova-Loop-9B-Preview
- LFM2.5-2.6B vs SmolLM3-3B
- LFM2.5-2.6B vs Spark-X2.6-4B
- LFM2.5-2.6B vs Step 3.7 Flash
- LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B
- LFM2.5-2.6B vs Swift 1.5 Qwen3.8-Flash-Next
- LFM2.5-2.6B vs ThinkingCap (Qwen3.8 27B)
- LFM2.5-2.6B vs Baidu Unlimited-OCR-2
- LFM2.5-2.6B vs WeVisOne-VL-2B
- LFM2.5-2.6B vs WeVisOne-VL-4B
- LFM2.5-2.6B vs ZAYA1-8B
- LFM3-1.7B vs LongCat-2.5
- LFM3-Audio-2B vs LongCat-2.5
- LFM3-Omni-2B vs LongCat-2.5
- LFM3-VL-2B vs LongCat-2.5
- Ling-3.0-tiny vs LongCat-2.5
- Ling-3.1-flash-1019 vs LongCat-2.5
- LongCat-2.5 vs MiMo-V2.6 Distill Qwen 9B
- LongCat-2.5 vs MiMo-V2.6-Flash-RL
- LongCat-2.5 vs MiniCPM-o 4.5
- LongCat-2.5 vs MiniCPM-V 4.7 1B
- LongCat-2.5 vs MiniCPM-V 4.7 35B-A3B
- LongCat-2.5 vs MiniCPM5-2B
- LongCat-2.5 vs MiniMax-M2.5
- LongCat-2.5 vs MiniMax-M2.7
- LongCat-2.5 vs MiniMax-M3.1-Flash
- LongCat-2.5 vs Mistral Large 4
- LongCat-2.5 vs Muse Glimmer 30B
- LongCat-2.5 vs Nanbeige4.2-3B
- LongCat-2.5 vs Nanbeige4.3-33B
- LongCat-2.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB
- LongCat-2.5 vs Nemotron 3.5 Lightning 30B A3B
- LongCat-2.5 vs Nemotron 3 Nano 30B A3B
- LongCat-2.5 vs Nemotron 3 Nano 4B
- LongCat-2.5 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- LongCat-2.5 vs Nemotron 3 Super 120B A12B
- LongCat-2.5 vs Nemotron 3 Ultra 550B A55B
- LongCat-2.5 vs North Mini Code 1.0
- LongCat-2.5 vs occamy-1.0
- LongCat-2.5 vs Olmo 3 7B Think
- LongCat-2.5 vs Ornith-1.5-122B
- LongCat-2.5 vs Ornith-1.5-35B-A3B
- LongCat-2.5 vs Ornith-1.5-397B
- LongCat-2.5 vs Ornith-1.5-9B
- LongCat-2.5 vs OTel-2.0-LLM-31B-IT
- LongCat-2.5 vs Qwen-AgentWorld-35B-A3B
- LongCat-2.5 vs Qwen3-30B-A3B-Instruct-2507
- LongCat-2.5 vs Qwen3-4B-Instruct-2507
- LongCat-2.5 vs Qwen3.5-0.8B
- LongCat-2.5 vs Qwen3.5-122B-A10B
- LongCat-2.5 vs Qwen3.5-2B
- LongCat-2.5 vs Qwen3.5-397B-A17B
- LongCat-2.5 vs Qwen3.5-4B
- LongCat-2.5 vs Qwen3.5-9B
- LongCat-2.5 vs Qwen3.6-27B
- LongCat-2.5 vs Qwen3.6-35B-A3B
- LongCat-2.5 vs Qwen3.8-27B
- LongCat-2.5 vs Qwen3.8-Flash-Next
- LongCat-2.5 vs Qwen3-Coder-30B-A3B-Instruct
- LongCat-2.5 vs Qwen3-Coder-Next
- LongCat-2.5 vs Qwen3-Omni-30B-A3B-Instruct
- LongCat-2.5 vs Qwen3-VL-2B-Instruct
- LongCat-2.5 vs Qwen3-VL-30B-A3B-Instruct
- LongCat-2.5 vs Qwen3-VL-32B-Instruct
- LongCat-2.5 vs Qwen3-VL-4B-Instruct
- LongCat-2.5 vs Qwen3-VL-8B-Instruct
- LongCat-2.5 vs Qwopus3.8-27B-Flash-V2
- LongCat-2.5 vs Recursive Flint
- LongCat-2.5 vs Ripple-65B Preview
- LongCat-2.5 vs SenseNova-Loop-9B-Preview
- LongCat-2.5 vs SmolLM3-3B
- LongCat-2.5 vs Spark-X2.6-4B
- LongCat-2.5 vs Step 3.7 Flash
- LongCat-2.5 vs Swift 1.5 Qwen3.8-27B
- LongCat-2.5 vs Swift 1.5 Qwen3.8-Flash-Next
- LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)
- LongCat-2.5 vs Baidu Unlimited-OCR-2
- LongCat-2.5 vs WeVisOne-VL-2B
- LongCat-2.5 vs WeVisOne-VL-4B
- LongCat-2.5 vs ZAYA1-8B
Machine-readable
- This page as Markdown
/compare/lfm2-5-2-6b-vs-longcat-2-5.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/lfm2-5-2-6b.json·/api/v1/tools/longcat-2-5.json - From a terminal
anchor compare lfm2-5-2-6b longcat-2-5(the CLI) - Over MCP
compare_tools {"a": "lfm2-5-2-6b", "b": "longcat-2-5"}at/mcp, no key