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LFM3-1.7B vs LongCat-Flash-Thinking-2601

LongCat-Flash-Thinking-2601 leads the LLM Stats Score 34.7 to 25.0.

Liquid AI · Meituan · Updated for 2026

Which is better?

LongCat-Flash-Thinking-2601 leads the overall LLM Stats Score 34.7 to 25.0, ranking #115 overall.

In the 3 individual benchmarks reported for both models, LongCat-Flash-Thinking-2601 wins 3; 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-2601

  • overall performance matters — it scores 34.7 and ranks #115 on LLM Stats
  • your work emphasizes reasoning and coding — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 3 of 3 exact shared results

At a glance

The differences that matter most.

LFM3-1.7B
Liquid AI
LongCat-Flash-Thinking-2601
Meituan
Core performance indexes
LLM Stats Score
25.0
#191
34.7
#115
Reasoning
20.8
#216
34.8
#108
Coding
7.4
#218
18.9
#135
Agents
18.1
#97
8.3
#162
Cost, coverage & limits
Benchmark wins
0 of 3
3 of 3
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-2601
Math
7.6#285
30.3#84
Tool use
14.3#118
23.4#50
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

20 reported for LFM3-1.7B · 11 for LongCat-Flash-Thinking-2601

3 shared

LFM3-1.7B outperforms in 0 benchmarks, while LongCat-Flash-Thinking-2601 is better at 3 benchmarks (GPQA, Humanity's Last Exam, SWE-Bench Verified).

LongCat-Flash-Thinking-2601 significantly outperforms across most benchmarks.

Wed Nov 04 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-2601 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-2601
560.0Bparameters
1.7B
LFM3-1.7B
560.0B
LongCat-Flash-Thinking-2601

Context Window

Maximum input and output token capacity

Only LongCat-Flash-Thinking-2601 specifies input context (128,000 tokens). Only LongCat-Flash-Thinking-2601 specifies output context (128,000 tokens).

Liquid AI
LFM3-1.7B
Input- tokens
Output- tokens
Meituan
LongCat-Flash-Thinking-2601
Input128,000 tokens
Output128,000 tokens
Wed Nov 04 2026 • llm-stats.com

License

Usage and distribution terms

LFM3-1.7B is licensed under LFM Open License v1.0, while LongCat-Flash-Thinking-2601 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-2601

MIT

Open weights

Release Timeline

When each model was launched

LFM3-1.7B was released on 2026-10-27, while LongCat-Flash-Thinking-2601 was released on 2026-01-14.

LFM3-1.7B is 10 months newer than LongCat-Flash-Thinking-2601.

LFM3-1.7B

Oct 27, 2026

1 weeks ago

9mo newer
LongCat-Flash-Thinking-2601

Jan 14, 2026

9 months 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-2601 side-by-side, then vote on the output you prefer.

LFM3-1.7B
✓ Preferred
LongCat-Flash-Thinking-2601
Open in Playground

FAQ

Common questions about LFM3-1.7B vs LongCat-Flash-Thinking-2601.

Which is better, LFM3-1.7B or LongCat-Flash-Thinking-2601?

LongCat-Flash-Thinking-2601 leads the LLM Stats Score 34.7 to 25.0. LFM3-1.7B is made by Liquid AI and LongCat-Flash-Thinking-2601 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-2601 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-2601 scores AIME 2025: 99.6%, Tau2 Telecom: 99.3%, Tau2 Retail: 88.6%, LiveCodeBench: 82.8%, GPQA: 80.5%.

What are the context window sizes for LFM3-1.7B and LongCat-Flash-Thinking-2601?

LFM3-1.7B supports an unknown number of tokens and LongCat-Flash-Thinking-2601 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-2601?

Key differences include LLM Stats Score (25.0 vs 34.7), 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-2601?

LFM3-1.7B is developed by Liquid AI and LongCat-Flash-Thinking-2601 is developed by Meituan.

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