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

Qwen3 235B A22B

Alibaba

Qwen3 235B A22B is a language model developed by Alibaba. It achieves strong performance with an average score of 76.2% across 23 benchmarks. It excels particularly in Arena Hard (95.6%), GSM8k (94.4%), BBH (88.9%). The model shows particular specialization in math tasks with an average performance of 83.3%. It supports a 256K token context window for handling large documents. The model is available through 4 API providers. It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents Alibaba's latest advancement in AI technology.

Alibaba

Qwen3 235B A22B

Alibaba

2025-04-29

Google

Gemini 2.5 Pro

Google

2025-05-20

21 days newer

Pricing Comparison

Cost per million tokens (USD)

Google

Gemini 2.5 Pro

Input:$1.25
Output:$10.00
Alibaba

Qwen3 235B A22B

$11.05 cheaper
Input:$0.10
Output:$0.10

Performance Metrics

Context window and performance specifications

Google

Gemini 2.5 Pro

Larger context
Max Context:1.1M
Alibaba

Qwen3 235B A22B

Max Context:256.0K
Parameters:235.0B

Average performance across 3 common benchmarks

Google

Gemini 2.5 Pro

+14.4%
Average Score:86.0%
Alibaba

Qwen3 235B A22B

Average Score:71.6%

Performance comparison across key benchmark categories

Google

Gemini 2.5 Pro

code
70.6%
general
69.4%
Alibaba

Qwen3 235B A22B

code
+6.0%
76.6%
general
+5.4%
74.8%
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
Alibaba

Qwen3 235B A22B

4 providers

Together

Throughput: 23.74 tok/s
Latency: 0.79ms

DeepInfra

Throughput: 21.74 tok/s
Latency: 1.23ms

Fireworks

Throughput: 68.17 tok/s
Latency: 0.78ms

Novita

Throughput: 38.51 tok/s
Latency: 1.02ms
Google

Gemini 2.5 Pro

+14.4%
Avg Score:86.0%
Providers:1
Alibaba

Qwen3 235B A22B

Avg Score:71.6%
Providers:4