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.

Alibaba

QwQ-32B-Preview

Alibaba

QwQ-32B-Preview is a language model developed by Alibaba. It achieves strong performance with an average score of 64.0% across 4 benchmarks. It excels particularly in MATH-500 (90.6%), GPQA (65.2%), AIME 2024 (50.0%). The model is available through 4 API providers. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2024, it represents Alibaba's latest advancement in AI technology.

Alibaba

QwQ-32B-Preview

Alibaba

2024-11-28

Google

Gemini 2.5 Pro

Google

2025-05-20

5 months newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.5 Pro

Input:$1.25
Output:$10.00
Alibaba

QwQ-32B-Preview

$10.90 cheaper
Input:$0.15
Output:$0.20

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Pro

Larger context
Max Context:1.1M
Alibaba

QwQ-32B-Preview

Max Context:65.5K
Parameters:32.5B

Average performance across 2 common benchmarks

Google

Gemini 2.5 Pro

+29.9%
Average Score:87.5%
Alibaba

QwQ-32B-Preview

Average Score:57.6%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Pro

code
+20.6%
70.6%
general
+11.8%
69.4%
Alibaba

QwQ-32B-Preview

code
50.0%
general
57.6%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

QwQ-32B-Preview

2024-11-28

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
Alibaba

QwQ-32B-Preview

4 providers

Together

Throughput: 62.14 tok/s
Latency: 0.74ms

Hyperbolic

Throughput: 31.9 tok/s
Latency: 1.05ms

DeepInfra

Throughput: 76.04 tok/s
Latency: 0.44ms

Fireworks

Throughput: 99.15 tok/s
Latency: 0.53ms
Google

Gemini 2.5 Pro

+29.9%
Avg Score:87.5%
Providers:1
Alibaba

QwQ-32B-Preview

Avg Score:57.6%
Providers:4