Kimi K2-Instruct-0905 vs Qwen2.5 14B Instruct Comparison

Comparing Kimi K2-Instruct-0905 and Qwen2.5 14B Instruct across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

Kimi K2-Instruct-0905 outperforms in 5 benchmarks (GPQA, MMLU, MMLU-Pro, MMLU-Redux, MultiPL-E), while Qwen2.5 14B Instruct is better at 0 benchmarks.

Kimi K2-Instruct-0905 significantly outperforms across most benchmarks.

Tue Mar 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Tue Mar 17 2026 • llm-stats.com
Moonshot AI
Kimi K2-Instruct-0905
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Alibaba Cloud / Qwen Team
Qwen2.5 14B Instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Model Size

Parameter count comparison

985.3B diff

Kimi K2-Instruct-0905 has 985.3B more parameters than Qwen2.5 14B Instruct, making it 6702.7% larger.

Moonshot AI
Kimi K2-Instruct-0905
1000.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5 14B Instruct
14.7Bparameters
1000.0B
Kimi K2-Instruct-0905
14.7B
Qwen2.5 14B Instruct

License

Usage and distribution terms

Kimi K2-Instruct-0905 is licensed under MIT, while Qwen2.5 14B Instruct uses Apache 2.0.

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

Kimi K2-Instruct-0905

MIT

Open weights

Qwen2.5 14B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Kimi K2-Instruct-0905 was released on 2025-09-05, while Qwen2.5 14B Instruct was released on 2024-09-19.

Kimi K2-Instruct-0905 is 12 months newer than Qwen2.5 14B Instruct.

Kimi K2-Instruct-0905

Sep 5, 2025

6 months ago

11mo newer
Qwen2.5 14B Instruct

Sep 19, 2024

1.5 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

Higher GPQA score (75.1% vs 45.5%)
Higher MMLU score (89.5% vs 79.7%)
Higher MMLU-Pro score (81.1% vs 63.7%)
Higher MMLU-Redux score (92.7% vs 80.0%)
Higher MultiPL-E score (85.7% vs 72.8%)
Alibaba Cloud / Qwen Team

Qwen2.5 14B Instruct

View details

Alibaba Cloud / Qwen Team

Detailed Comparison

AI Model Comparison Table
Feature
Moonshot AI
Kimi K2-Instruct-0905
Alibaba Cloud / Qwen Team
Qwen2.5 14B Instruct