Model Comparison

Comprehensive side-by-side analysis of model capabilities and performance

Google

Gemini 2.5 Pro

Google

Gemini 2.5 Pro is a multimodal language model developed by Google. It achieves strong performance with an average score of 67.1% across 16 benchmarks. It excels particularly in MRCR (93.0%), AIME 2024 (92.0%), Global-MMLU-Lite (88.6%). The model shows particular specialization in vision tasks with an average performance of 82.2%. With a 1.1M token context window, it can handle extensive documents and complex multi-turn conversations. The model is available through 1 API provider. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Google's latest advancement in AI technology.

Moonshot AI

Kimi-k1.5

Moonshot AI

Kimi-k1.5 is a multimodal language model developed by Moonshot AI. This model demonstrates exceptional performance with an average score of 81.7% across 9 benchmarks. It excels particularly in MATH-500 (96.2%), CLUEWSC (91.4%), C-Eval (88.3%). The model shows particular specialization in math tasks with an average performance of 85.5%. As a multimodal model, it can process and understand text, images, and other input formats seamlessly. Released in 2025, it represents Moonshot AI's latest advancement in AI technology.

Moonshot AI

Kimi-k1.5

Moonshot AI

2025-01-20

Google

Gemini 2.5 Pro

Google

2025-05-20

4 months newer

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Pro

Larger context
Max Context:1.1M
Moonshot AI

Kimi-k1.5

Max Context:-

Average performance across 2 common benchmarks

Google

Gemini 2.5 Pro

+12.1%
Average Score:85.8%
Moonshot AI

Kimi-k1.5

Average Score:73.8%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Pro

general
69.4%
vision
+12.2%
82.2%
code
70.6%
Moonshot AI

Kimi-k1.5

general
+16.0%
85.4%
vision
70.0%
code
+8.7%
79.3%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 2.5 Pro

2025-01-31

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

Google

Gemini 2.5 Pro

1 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms
Moonshot AI

Kimi-k1.5

0 providers
Google

Gemini 2.5 Pro

+12.1%
Avg Score:85.8%
Providers:1
Moonshot AI

Kimi-k1.5

Avg Score:73.8%
Providers:0