Model Comparison

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

DeepSeek

DeepSeek-V2.5

DeepSeek

DeepSeek-V2.5 is a language model developed by DeepSeek. It achieves strong performance with an average score of 71.1% across 15 benchmarks. It excels particularly in GSM8k (95.1%), MT-Bench (90.2%), HumanEval (89.0%). The model is available through 3 API providers. Released in 2024, it represents DeepSeek's latest advancement in AI technology.

Google

Gemini 2.5 Pro Preview 06-05

Google

Gemini 2.5 Pro Preview 06-05 is a multimodal language model developed by Google. It achieves strong performance with an average score of 68.8% across 13 benchmarks. It excels particularly in Global-MMLU-Lite (89.2%), AIME 2025 (88.0%), FACTS Grounding (87.8%). 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.

DeepSeek

DeepSeek-V2.5

DeepSeek

2024-05-08

Google

Gemini 2.5 Pro Preview 06-05

Google

2025-06-05

1 year newer

Pricing Comparison

Cost per million tokens (USD)

DeepSeek

DeepSeek-V2.5

$10.83 cheaper
Input:$0.14
Output:$0.28
Google

Gemini 2.5 Pro Preview 06-05

Input:$1.25
Output:$10.00

Performance Metrics

Context window and performance specifications

DeepSeek

DeepSeek-V2.5

Max Context:16.4K
Parameters:236.0B
Google

Gemini 2.5 Pro Preview 06-05

Larger context
Max Context:1.1M

Average performance across 1 common benchmarks

DeepSeek

DeepSeek-V2.5

Average Score:16.8%
Google

Gemini 2.5 Pro Preview 06-05

+50.4%
Average Score:67.2%

Performance comparison across key benchmark categories

DeepSeek

DeepSeek-V2.5

general
68.4%
code
66.1%
Google

Gemini 2.5 Pro Preview 06-05

general
+1.4%
69.8%
code
+2.0%
68.1%
Benchmark Scores - Detailed View
Side-by-side comparison of all benchmark scores
Knowledge Cutoff
Training data recency comparison

Gemini 2.5 Pro Preview 06-05

2025-01-31

More recent knowledge cutoff means awareness of newer technologies and frameworks

Provider Availability & Performance

Available providers and their performance metrics

DeepSeek

DeepSeek-V2.5

3 providers

Hyperbolic

Throughput: 100 tok/s
Latency: 0.5ms

DeepInfra

Throughput: 63 tok/s
Latency: 0.5ms

DeepSeek

Throughput: 100 tok/s
Latency: 0.5ms
Google

Gemini 2.5 Pro Preview 06-05

1 providers

Google

Throughput: 85 tok/s
Latency: 0.7ms
DeepSeek

DeepSeek-V2.5

Avg Score:16.8%
Providers:3
Google

Gemini 2.5 Pro Preview 06-05

+50.4%
Avg Score:67.2%
Providers:1