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LFM3-1.7B vs LongCat-Flash-Thinking

LFM3-1.7B and LongCat-Flash-Thinking are closely matched at 25.0 and 28.1 on the LLM Stats Score.

Liquid AI · Meituan · Updated for 2026

Which is better?

LFM3-1.7B and LongCat-Flash-Thinking are closely matched on the overall LLM Stats Score at 25.0 and 28.1.

In the 2 individual benchmarks reported for both models, LongCat-Flash-Thinking wins 2; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose LFM3-1.7B

  • you want the most recent training data — it shipped Oct 2026

Choose LongCat-Flash-Thinking

  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 2 of 2 exact shared results

At a glance

The differences that matter most.

LFM3-1.7B
Liquid AI
LongCat-Flash-Thinking
Meituan
Core performance indexes
LLM Stats Score
25.0
#191
28.1
#168
Reasoning
20.8
#216
28.2
#159
Coding
7.4
#218
13.8
#165
Agents
18.1
#97
12.7
#128
Cost, coverage & limits
Benchmark wins
0 of 2
2 of 2
Input price
— / M
$0.30 / M
Output price
— / M
$1.20 / M
Context window
—
128,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
LFM3-1.7B
LongCat-Flash-Thinking
Math
7.6#285
28.1#106
Tool use
14.3#119
15.2#114
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for LFM3-1.7B · 14 for LongCat-Flash-Thinking

2 shared

LFM3-1.7B outperforms in 0 benchmarks, while LongCat-Flash-Thinking is better at 2 benchmarks (GPQA, SWE-Bench Verified).

LongCat-Flash-Thinking significantly outperforms across most benchmarks.

Fri Nov 06 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

558.3B diff

LongCat-Flash-Thinking has 558.3B more parameters than LFM3-1.7B, making it 32269.9% larger.

Liquid AI
LFM3-1.7B
1.7Bparameters
Meituan
LongCat-Flash-Thinking
560.0Bparameters
1.7B
LFM3-1.7B
560.0B
LongCat-Flash-Thinking

Context Window

Maximum input and output token capacity

Only LongCat-Flash-Thinking specifies input context (128,000 tokens). Only LongCat-Flash-Thinking specifies output context (128,000 tokens).

Liquid AI
LFM3-1.7B
Input- tokens
Output- tokens
Meituan
LongCat-Flash-Thinking
Input128,000 tokens
Output128,000 tokens
Fri Nov 06 2026 • llm-stats.com

License

Usage and distribution terms

LFM3-1.7B is licensed under LFM Open License v1.0, while LongCat-Flash-Thinking uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

LFM3-1.7B

LFM Open License v1.0

Open weights

LongCat-Flash-Thinking

MIT

Open weights

Release Timeline

When each model was launched

LFM3-1.7B was released on 2026-10-27, while LongCat-Flash-Thinking was released on 2025-09-22.

LFM3-1.7B is 13 months newer than LongCat-Flash-Thinking.

LFM3-1.7B

Oct 27, 2026

1 weeks ago

1.1yr newer
LongCat-Flash-Thinking

Sep 22, 2025

1.1 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against LFM3-1.7B and LongCat-Flash-Thinking side-by-side, then vote on the output you prefer.

LFM3-1.7B
✓ Preferred
LongCat-Flash-Thinking
Open in Playground

FAQ

Common questions about LFM3-1.7B vs LongCat-Flash-Thinking.

Which is better, LFM3-1.7B or LongCat-Flash-Thinking?

LFM3-1.7B and LongCat-Flash-Thinking are closely matched on the LLM Stats Score at 25.0 and 28.1. LFM3-1.7B is made by Liquid AI and LongCat-Flash-Thinking is made by Meituan. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does LFM3-1.7B compare to LongCat-Flash-Thinking in benchmarks?

LFM3-1.7B scores IFEval: 96.7%, Multi-IF: 89.1%, LiveCodeBench v6: 73.5%, IFBench: 73.0%, BFCL-V4: 71.4%. LongCat-Flash-Thinking scores MATH-500: 99.2%, ZebraLogic: 95.5%, AIME 2024: 93.3%, AIME 2025: 90.6%, MMLU-Redux: 89.3%.

What are the context window sizes for LFM3-1.7B and LongCat-Flash-Thinking?

LFM3-1.7B supports an unknown number of tokens and LongCat-Flash-Thinking supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between LFM3-1.7B and LongCat-Flash-Thinking?

Key differences include LLM Stats Score (25.0 vs 28.1), licensing (LFM Open License v1.0 vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes LFM3-1.7B and LongCat-Flash-Thinking?

LFM3-1.7B is developed by Liquid AI and LongCat-Flash-Thinking is developed by Meituan.

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