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LFM2.5-VL-3B vs LongCat-Flash-Thinking

Comparing LFM2.5-VL-3B and LongCat-Flash-Thinking across benchmarks, pricing, and capabilities.

Liquid AI · Meituan · Updated for 2026

Which is better?

LFM2.5-VL-3B and LongCat-Flash-Thinking trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose LFM2.5-VL-3B

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

Choose LongCat-Flash-Thinking

  • you want predictable pricing at $0.30/M input and $1.20/M output

At a glance

The differences that matter most.

LFM2.5-VL-3B
Liquid AI
LongCat-Flash-Thinking
Meituan
Benchmark wins
—
—
Input price
— / M
$0.30 / M
Output price
— / M
$1.20 / M
Context window
—
128,000
Released
Aug 2026
Sep 2025
License
LFM Open License v1.0
MIT

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

LFM2.5-VL-3B and LongCat-Flash-Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

556.9B diff

LongCat-Flash-Thinking has 556.9B more parameters than LFM2.5-VL-3B, making it 17828.7% larger.

Liquid AI
LFM2.5-VL-3B
3.1Bparameters
Meituan
LongCat-Flash-Thinking
560.0Bparameters
3.1B
LFM2.5-VL-3B
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
LFM2.5-VL-3B
Input- tokens
Output- tokens
Meituan
LongCat-Flash-Thinking
Input128,000 tokens
Output128,000 tokens
Tue Aug 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

LFM2.5-VL-3B supports multimodal inputs, whereas LongCat-Flash-Thinking does not.

LFM2.5-VL-3B can handle both text and other forms of data like images, making it suitable for multimodal applications.

LFM2.5-VL-3B

Text
Images
Audio
Video

LongCat-Flash-Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

LFM2.5-VL-3B 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.

LFM2.5-VL-3B

LFM Open License v1.0

Open weights

LongCat-Flash-Thinking

MIT

Open weights

Release Timeline

When each model was launched

LFM2.5-VL-3B was released on 2026-08-12, while LongCat-Flash-Thinking was released on 2025-09-22.

LFM2.5-VL-3B is 11 months newer than LongCat-Flash-Thinking.

LFM2.5-VL-3B

Aug 12, 2026

1 weeks ago

10mo newer
LongCat-Flash-Thinking

Sep 22, 2025

11 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?Start an Issue discussion→

Judge for yourself.

Run your own prompts against LFM2.5-VL-3B and LongCat-Flash-Thinking side-by-side, then vote on the output you prefer.

LFM2.5-VL-3B
✓ Preferred
LongCat-Flash-Thinking
Open in Playground

FAQ

Common questions about LFM2.5-VL-3B vs LongCat-Flash-Thinking.

Which is better, LFM2.5-VL-3B or LongCat-Flash-Thinking?

LFM2.5-VL-3B (Liquid AI) and LongCat-Flash-Thinking (Meituan) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does LFM2.5-VL-3B compare to LongCat-Flash-Thinking in benchmarks?

LFM2.5-VL-3B scores DocVQA: 91.1%, POPE: 88.7%, RefCOCO-avg: 87.9%, TextVQA: 84.3%, OCRBench: 84.2%. 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 LFM2.5-VL-3B and LongCat-Flash-Thinking?

LFM2.5-VL-3B 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 LFM2.5-VL-3B and LongCat-Flash-Thinking?

Key differences include multimodal support (yes vs no), licensing (LFM Open License v1.0 vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes LFM2.5-VL-3B and LongCat-Flash-Thinking?

LFM2.5-VL-3B is developed by Liquid AI and LongCat-Flash-Thinking is developed by Meituan.

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