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LongCat-2.5 vs Qwen3.5-9B

LongCat-2.5 leads the LLM Stats Score 50.0 to 23.9. Qwen3.5-9B is 4.7x 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 23.9, ranking #20 overall.

On price, Qwen3.5-9B is roughly 4.7x 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 process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Nov 2026

Choose Qwen3.5-9B

  • cost matters — it's about 4.7x cheaper per token
  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Core performance indexes
50.0
#20
23.9
#200
48.3
#21
23.7
#194
36.3
#23
8.1
#164
Cost, coverage & limits
Benchmark wins
—
—
Input price
$0.30 / M
$0.10 / M
Output price
$1.20 / M
$0.15 / M
Context window
1,048,576
262,144

Capability indexes

Additional strengths measured across groups of related public benchmarks

2 shared
Index
LongCat-2.5
Qwen3.5-9B
37.2#37
23.8#135
30.2#15
9.6#158
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

23 reported for LongCat-2.5 · 25 for Qwen3.5-9B

No common benchmarks found

LongCat-2.5 and Qwen3.5-9Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Qwen3.5-9B costs less

For input processing, LongCat-2.5 ($0.30/1M tokens) is 3.0x more expensive than Qwen3.5-9B ($0.10/1M tokens).

For output processing, LongCat-2.5 ($1.20/1M tokens) is 8.0x more expensive than Qwen3.5-9B ($0.15/1M tokens).

In conclusion, LongCat-2.5 is more expensive than Qwen3.5-9B.*

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

Lowest available price from all providers
Fri Nov 06 2026 • llm-stats.com
Meituan
LongCat-2.5
Input tokens$0.30
Output tokens$1.20
Best providerMeituan
Alibaba Cloud / Qwen Team
Qwen3.5-9B
Input tokens$0.10
Output tokens$0.15
Best providerDeepinfra
Notice missing or incorrect data?

Model Size

Parameter count comparison

1591.0B diff

LongCat-2.5 has 1591.0B more parameters than Qwen3.5-9B, making it 17677.8% larger.

Meituan
LongCat-2.5
1.6Tparameters
Alibaba Cloud / Qwen Team
Qwen3.5-9B
9.0Bparameters
1600.0B
LongCat-2.5
9.0B
Qwen3.5-9B

Context Window

Maximum input and output token capacity

LongCat-2.5 accepts 1,048,576 input tokens compared to Qwen3.5-9B's 262,144 tokens. Qwen3.5-9B 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
Qwen3.5-9B
Input262,144 tokens
Output262,144 tokens
Fri Nov 06 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Both LongCat-2.5 and Qwen3.5-9B support multimodal inputs.

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

LongCat-2.5

Text
Images
Audio
Video

Qwen3.5-9B

Text
Images
Audio
Video

Release Timeline

When each model was launched

LongCat-2.5 was released on 2026-11-01, while Qwen3.5-9B was released on 2026-03-02.

LongCat-2.5 is 8 months newer than Qwen3.5-9B.

LongCat-2.5

Nov 1, 2026

4 days ago

8mo newer
Qwen3.5-9B

Mar 2, 2026

8 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

Provider Availability

LongCat-2.5 is available from Meituan. Qwen3.5-9B is available from DeepInfra.

LongCat-2.5

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

Qwen3.5-9B

deepinfra logo
Deepinfra
Input Price:Input: $0.10/1MOutput Price:Output: $0.15/1M
* 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 Qwen3.5-9B side-by-side, then vote on the output you prefer.

LongCat-2.5
✓ Preferred
Qwen3.5-9B
Open in Playground

FAQ

Common questions about LongCat-2.5 vs Qwen3.5-9B.

Which is better, LongCat-2.5 or Qwen3.5-9B?

LongCat-2.5 leads the LLM Stats Score 50.0 to 23.9. LongCat-2.5 is made by Meituan and Qwen3.5-9B 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 Qwen3.5-9B 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%. Qwen3.5-9B scores IFEval: 91.5%, MMLU-Redux: 91.1%, C-Eval: 88.2%, MAXIFE: 83.4%, Global PIQA: 83.2%.

Is LongCat-2.5 cheaper than Qwen3.5-9B?

Qwen3.5-9B is 3.0x cheaper for input tokens. LongCat-2.5 costs $0.30/M input and $1.20/M output via meituan. Qwen3.5-9B costs $0.10/M input and $0.15/M output via deepinfra.

What are the context window sizes for LongCat-2.5 and Qwen3.5-9B?

LongCat-2.5 supports 1.0M tokens and Qwen3.5-9B 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 Qwen3.5-9B?

Key differences include LLM Stats Score (50.0 vs 23.9), 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 Qwen3.5-9B?

LongCat-2.5 is developed by Meituan and Qwen3.5-9B is developed by Alibaba Cloud / Qwen Team.