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Kimi K2.5 vs QwQ-32B

Kimi K2.5 leads the LLM Stats Score 38.9 to 14.9.

Moonshot AI · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Kimi K2.5 leads the overall LLM Stats Score 38.9 to 14.9, ranking #60 overall.

The models split the 2 individual benchmarks reported for both models evenly.

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

Choose Kimi K2.5

  • overall performance matters — it scores 38.9 and ranks #60 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you want the most recent training data — it shipped Jan 2026

Choose QwQ-32B

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem

At a glance

The differences that matter most.

Core performance indexes
38.9
#60
14.9
#230
38.8
#58
15.7
#221
25.3
#68
10.3
#162
Cost, coverage & limits
Benchmark wins
1 of 2
1 of 2
Input price
$0.60 / M
— / M
Output price
$3.00 / M
— / M
Context window
262,100

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Kimi K2.5
QwQ-32B
36.6#36
18.1#185
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

40 reported for Kimi K2.5 · 7 for QwQ-32B

2 shared

Kimi K2.5 outperforms in 1 benchmarks (GPQA), while QwQ-32B is better at 1 benchmark (LiveBench).

Both models are evenly matched across the benchmarks.

Thu Sep 10 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

967.5B diff

Kimi K2.5 has 967.5B more parameters than QwQ-32B, making it 2976.9% larger.

Moonshot AI
Kimi K2.5
1.0Tparameters
Alibaba Cloud / Qwen Team
QwQ-32B
32.5Bparameters
1000.0B
Kimi K2.5
32.5B
QwQ-32B

Context Window

Maximum input and output token capacity

Only Kimi K2.5 specifies input context (262,100 tokens). Only Kimi K2.5 specifies output context (262,100 tokens).

Moonshot AI
Kimi K2.5
Input262,100 tokens
Output262,100 tokens
Alibaba Cloud / Qwen Team
QwQ-32B
Input- tokens
Output- tokens
Thu Sep 10 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Kimi K2.5 supports multimodal inputs, whereas QwQ-32B does not.

Kimi K2.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.

Kimi K2.5

Text
Images
Audio
Video

QwQ-32B

Text
Images
Audio
Video

License

Usage and distribution terms

Kimi K2.5 is licensed under MIT, while QwQ-32B uses Apache 2.0.

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

Kimi K2.5

MIT

Open weights

QwQ-32B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Kimi K2.5 was released on 2026-01-27, while QwQ-32B was released on 2025-03-05.

Kimi K2.5 is 11 months newer than QwQ-32B.

Kimi K2.5

Jan 27, 2026

7 months ago

10mo newer
QwQ-32B

Mar 5, 2025

1.5 years ago

Knowledge Cutoff

When training data ends

QwQ-32B has a documented knowledge cutoff of 2024-11-28, while Kimi K2.5's cutoff date is not specified.

We can confirm QwQ-32B's training data extends to 2024-11-28, but cannot make a direct comparison without Kimi K2.5's cutoff date.

Kimi K2.5

QwQ-32B

Nov 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Kimi K2.5 and QwQ-32B side-by-side, then vote on the output you prefer.

Kimi K2.5
✓ Preferred
QwQ-32B
Open in Playground

FAQ

Common questions about Kimi K2.5 vs QwQ-32B.

Which is better, Kimi K2.5 or QwQ-32B?

Kimi K2.5 leads the LLM Stats Score 38.9 to 14.9. Kimi K2.5 is made by Moonshot AI and QwQ-32B 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 Kimi K2.5 compare to QwQ-32B in benchmarks?

Kimi K2.5 scores AIME 2025: 96.1%, HMMT 2025: 95.4%, InfoVQAtest: 92.6%, OCRBench: 92.3%, MathVista-Mini: 90.1%. QwQ-32B scores MATH-500: 90.6%, IFEval: 83.9%, AIME 2024: 79.5%, LiveBench: 73.1%, BFCL: 66.4%.

What are the context window sizes for Kimi K2.5 and QwQ-32B?

Kimi K2.5 supports 262K tokens and QwQ-32B supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Kimi K2.5 and QwQ-32B?

Key differences include LLM Stats Score (38.9 vs 14.9), multimodal support (yes vs no), licensing (MIT vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.

Who makes Kimi K2.5 and QwQ-32B?

Kimi K2.5 is developed by Moonshot AI and QwQ-32B is developed by Alibaba Cloud / Qwen Team.