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LongCat-2.5 vs Qwen3 VL 4B Thinking

LongCat-2.5 leads the LLM Stats Score 50.0 to 12.4. Qwen3 VL 4B Thinking is 1.6x cheaper per token.

Meituan · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

LongCat-2.5 leads the overall LLM Stats Score 50.0 to 12.4, ranking #20 overall.

In the 2 individual benchmarks reported for both models, LongCat-2.5 wins 2; this is a narrower head-to-head signal than the composite indexes.

On price, Qwen3 VL 4B Thinking is roughly 1.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

LongCat-2.5 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.

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

Choose LongCat-2.5

  • overall performance matters — it scores 50.0 and ranks #20 on LLM Stats
  • your work emphasizes reasoning and agents — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 2 of 2 exact shared results
  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Nov 2026

Choose Qwen3 VL 4B Thinking

  • cost matters — it's about 1.6x cheaper per token
  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

LongCat-2.5
Meituan
Qwen3 VL 4B Thinking
Alibaba Cloud / Qwen Team
Core performance indexes
LLM Stats Score
50.0
#20
12.4
#289
Reasoning
48.3
#21
13.5
#270
Agents
36.3
#23
6.4
#174
Cost, coverage & limits
Benchmark wins
2 of 2
0 of 2
Input price
$0.30 / M
$0.10 / M
Output price
$1.20 / M
$1.00 / M
Context window
1,048,576
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

3 shared
Index
LongCat-2.5
Qwen3 VL 4B Thinking
Math
37.2#37
15.4#234
Vision
32.5#29
7.6#167
Multimodal
34.8#15
10.4#137
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

23 reported for LongCat-2.5 · 48 for Qwen3 VL 4B Thinking

2 shared

LongCat-2.5 outperforms in 2 benchmarks (CharXiv-R, MathVision), while Qwen3 VL 4B Thinking is better at 0 benchmarks.

LongCat-2.5 significantly outperforms across most benchmarks.

Wed Nov 04 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Qwen3 VL 4B Thinking costs less

For input processing, LongCat-2.5 ($0.30/1M tokens) is 3.0x more expensive than Qwen3 VL 4B Thinking ($0.10/1M tokens).

For output processing, LongCat-2.5 ($1.20/1M tokens) is 1.2x more expensive than Qwen3 VL 4B Thinking ($1.00/1M tokens).

In conclusion, LongCat-2.5 is more expensive than Qwen3 VL 4B Thinking.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Wed Nov 04 2026 • llm-stats.com
Meituan
LongCat-2.5
Input tokens$0.30
Output tokens$1.20
Best providerMeituan
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Input tokens$0.10
Output tokens$1.00
Best providerDeepinfra
Notice missing or incorrect data?

Model Size

Parameter count comparison

1596.0B diff

LongCat-2.5 has 1596.0B more parameters than Qwen3 VL 4B Thinking, making it 39900.0% larger.

Meituan
LongCat-2.5
1.6Tparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
4.0Bparameters
1600.0B
LongCat-2.5
4.0B
Qwen3 VL 4B Thinking

Context Window

Maximum input and output token capacity

LongCat-2.5 accepts 1,048,576 input tokens compared to Qwen3 VL 4B Thinking's 262,144 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while LongCat-2.5 is limited to 131,072 tokens.

Meituan
LongCat-2.5
Input1,048,576 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 4B Thinking
Input262,144 tokens
Output262,144 tokens
Wed Nov 04 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both LongCat-2.5 and Qwen3 VL 4B Thinking support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

LongCat-2.5

Text
Images
Audio
Video

Qwen3 VL 4B Thinking

Text
Images
Audio
Video

Release Timeline

When each model was launched

LongCat-2.5 was released on 2026-11-01, while Qwen3 VL 4B Thinking was released on 2025-09-22.

LongCat-2.5 is 14 months newer than Qwen3 VL 4B Thinking.

LongCat-2.5

Nov 1, 2026

3 days ago

1.1yr newer
Qwen3 VL 4B 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

Provider Availability

LongCat-2.5 is available from Meituan. Qwen3 VL 4B Thinking is available from DeepInfra.

LongCat-2.5

meituan logo
Meituan
Input Price:Input: $0.30/1MOutput Price:Output: $1.20/1M

Qwen3 VL 4B Thinking

deepinfra logo
Deepinfra
Input Price:Input: $0.10/1MOutput Price:Output: $1.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?

Judge for yourself.

Run your own prompts against LongCat-2.5 and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.

LongCat-2.5
✓ Preferred
Qwen3 VL 4B Thinking
Open in Playground

FAQ

Common questions about LongCat-2.5 vs Qwen3 VL 4B Thinking.

Which is better, LongCat-2.5 or Qwen3 VL 4B Thinking?

LongCat-2.5 leads the LLM Stats Score 50.0 to 12.4. LongCat-2.5 is made by Meituan and Qwen3 VL 4B Thinking is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does LongCat-2.5 compare to Qwen3 VL 4B Thinking in benchmarks?

LongCat-2.5 scores MathVision: 96.5%, BrowseComp: 91.9%, CharXiv-R: 90.2%, Terminal-Bench 2.1: 88.4%, Video-MME: 85.2%. Qwen3 VL 4B Thinking scores DocVQAtest: 94.2%, ScreenSpot: 92.9%, MMBench-V1.1: 86.7%, MMLU-Redux: 86.0%, AI2D: 84.9%.

Is LongCat-2.5 cheaper than Qwen3 VL 4B Thinking?

Qwen3 VL 4B Thinking is 3.0x cheaper for input tokens. LongCat-2.5 costs $0.30/M input and $1.20/M output via meituan. Qwen3 VL 4B Thinking costs $0.10/M input and $1.00/M output via deepinfra.

What are the context window sizes for LongCat-2.5 and Qwen3 VL 4B Thinking?

LongCat-2.5 supports 1.0M tokens and Qwen3 VL 4B Thinking supports 262K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between LongCat-2.5 and Qwen3 VL 4B Thinking?

Key differences include LLM Stats Score (50.0 vs 12.4), context window (1.0M vs 262K), input pricing ($0.30 vs $0.10/M), licensing (Unknown vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes LongCat-2.5 and Qwen3 VL 4B Thinking?

LongCat-2.5 is developed by Meituan and Qwen3 VL 4B Thinking is developed by Alibaba Cloud / Qwen Team.

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