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Discover how DeepSeek's DeepSeek V3 and Google's Gemini 2.5 Pro stack up against each other in this comprehensive comparison of two leading AI
language models.
Released in December 2024 and March 2025 respectively, these models represent significant advancements in artificial intelligence,
with DeepSeek V3 offering a 64,000-token context
window and Gemini 2.5 Pro featuring a 1,000,000-token
capacity. Their distinct approaches to natural language processing are reflected in their
benchmark performances, with DeepSeek V3 achieving 88.5%
on MMLU and Gemini 2.5 Pro scoring 81.7%, making this comparison essential for developers and organizations seeking the right AI
solution for their specific needs.
Models Overview
 DeepSeek V3 |  Gemini 2.5 Pro | |
|---|---|---|
Provider Company that developed the model | DeepSeek | |
Context Length Maximum number of tokens the model can process | 64K | 1M |
Maximum Output Maximum number of tokens the model can generate in a single response | 8192 | 64K |
Release Date Date when the model was released | 26-12-2024 | 25-03-2025 |
Knowledge Cutoff Training data cutoff date | July 2024 | January 2025 |
Open Source Whether the model's code is open-source | TRUE | FALSE |
API Providers API providers that offer access to the model | DeepSeek, Fireworks AI, Hyperbolic | Vertex AI |
Pricing Comparison
Compare the pricing of DeepSeek's DeepSeek V3 and Google's Gemini 2.5 Pro to determine the most cost-effective solution for your AI needs.
| DeepSeek V3 | Gemini 2.5 Pro | |
|---|---|---|
Input Cost Cost per million input tokens | $0.27 / 1M tokens | $1.25 / 1M tokens |
Output Cost Cost per million tokens generated | $1.1 / 1M tokens | $10 / 1M tokens |
Comparing Benchmarks and Performance
Compare the performances of DeepSeek's DeepSeek V3 and Google's Gemini 2.5 Pro on industry benchmarks. This section provides a detailed comparison on MMLU, MMMU, HumanEval, MATH and other key benchmarks.
| DeepSeek V3 | Gemini 2.5 Pro | |
|---|---|---|
MMLU Evaluating LLM knowledge acquisition in zero-shot and few-shot settings. | 88.5% | 81.7% |
MMMU A wide ranging multi-discipline and multimodal benchmark. | Benchmark not available | Benchmark not available |
HellaSwag A challenging sentence completion benchmark. | Benchmark not available | Benchmark not available |
GSM8K Grade-school math problems benchmark. | Benchmark not available | Benchmark not available |
HumanEval A benchmark to measure functional correctness for synthesizing programs from docstrings. | 82.6% | Benchmark not available |
MATH Benchmark performance on Math problems ranging across 5 levels of difficulty and 7 sub-disciplines. | 90.2% | Benchmark not available |
