Model Comparison

Kimi K2-Thinking-0905 vs QvQ-72B-Preview

Comparing Kimi K2-Thinking-0905 and QvQ-72B-Preview across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Kimi K2-Thinking-0905 and QvQ-72B-Preview don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Wed Apr 15 2026 • llm-stats.com
Moonshot AI
Kimi K2-Thinking-0905
Input tokens$0.47
Output tokens$2.00
Best providerDeepinfra
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

926.6B diff

Kimi K2-Thinking-0905 has 926.6B more parameters than QvQ-72B-Preview, making it 1262.4% larger.

Moonshot AI
Kimi K2-Thinking-0905
1000.0Bparameters
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
73.4Bparameters
1000.0B
Kimi K2-Thinking-0905
73.4B
QvQ-72B-Preview

Context Window

Maximum input and output token capacity

Only Kimi K2-Thinking-0905 specifies input context (262,144 tokens). Only Kimi K2-Thinking-0905 specifies output context (262,144 tokens).

Moonshot AI
Kimi K2-Thinking-0905
Input262,144 tokens
Output262,144 tokens
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
Input- tokens
Output- tokens
Wed Apr 15 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

QvQ-72B-Preview supports multimodal inputs, whereas Kimi K2-Thinking-0905 does not.

QvQ-72B-Preview can handle both text and other forms of data like images, making it suitable for multimodal applications.

Kimi K2-Thinking-0905

Text
Images
Audio
Video

QvQ-72B-Preview

Text
Images
Audio
Video

License

Usage and distribution terms

Kimi K2-Thinking-0905 is licensed under MIT, while QvQ-72B-Preview uses Qwen.

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

Kimi K2-Thinking-0905

MIT

Open weights

QvQ-72B-Preview

Qwen

Open weights

Release Timeline

When each model was launched

Kimi K2-Thinking-0905 was released on 2025-09-05, while QvQ-72B-Preview was released on 2024-12-25.

Kimi K2-Thinking-0905 is 8 months newer than QvQ-72B-Preview.

Kimi K2-Thinking-0905

Sep 5, 2025

7 months ago

8mo newer
QvQ-72B-Preview

Dec 25, 2024

1.3 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

Outputs Comparison

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Key Takeaways

Moonshot AI
Larger context window (262,144 tokens)
Alibaba Cloud / Qwen Team

QvQ-72B-Preview

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
Moonshot AI
Kimi K2-Thinking-0905
Alibaba Cloud / Qwen Team
QvQ-72B-Preview

FAQ

Common questions about Kimi K2-Thinking-0905 vs QvQ-72B-Preview

Kimi K2-Thinking-0905 (Moonshot AI) and QvQ-72B-Preview (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
Kimi K2-Thinking-0905 scores AIME 2025: 100.0%, HMMT 2025: 97.5%, MMLU-Redux: 94.4%, FRAMES: 87.0%, MMLU-Pro: 84.6%. QvQ-72B-Preview scores MathVista: 71.4%, MMMU: 70.3%, MathVision: 35.9%, OlympiadBench: 20.4%.
Kimi K2-Thinking-0905 supports 262K tokens and QvQ-72B-Preview supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes), licensing (MIT vs Qwen). See the full comparison above for benchmark-by-benchmark results.
Kimi K2-Thinking-0905 is developed by Moonshot AI and QvQ-72B-Preview is developed by Alibaba Cloud / Qwen Team.