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deepseek-v4-pro vs gemini-2.5-pro

Side-by-side comparison of deepseek-v4-pro and gemini-2.5-pro: benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. deepseek-v4-pro outperforms gemini-2.5-pro on 7 of 7 shared benchmarks.

Benchmark comparison

Intelligence Indexreasoning
deepseek-v4-pro51.5%
gemini-2.5-pro34.6%
Coding Indexcoding
deepseek-v4-pro47.5%
gemini-2.5-pro32.0%
Math Indexmath
deepseek-v4-proN/A
gemini-2.5-pro87.7%
GPQA Diamondreasoning
deepseek-v4-pro88.8%
gemini-2.5-pro84.4%
AIME 2025math
deepseek-v4-proN/A
gemini-2.5-pro87.7%
LiveCodeBenchcoding
deepseek-v4-proN/A
gemini-2.5-pro80.1%
Terminal-Bench Hardagentic
deepseek-v4-pro46.2%
gemini-2.5-pro26.5%
τ²-Benchagentic
deepseek-v4-pro96.2%
gemini-2.5-pro54.1%
SciCodecoding
deepseek-v4-pro50.0%
gemini-2.5-pro42.8%
MMLU Proknowledge
deepseek-v4-proN/A
gemini-2.5-pro86.2%
Humanity's Last Examreasoning
deepseek-v4-pro35.9%
gemini-2.5-pro21.1%

Scores sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and don't capture every aspect of model quality.

Pricing & specifications

deepseek-v4-progemini-2.5-pro
Input price / 1M$0.43$1.25
Output price / 1M$0.87$10.00
Context window1M tokens1.0M tokens
Max output384K tokens66K tokens
Vision inputN/AYes
Tool callingYesYes
ReasoningN/AYes
Prompt cachingYesYes
Computer useN/AN/A
ProviderDeepSeekGoogle LLC (Gemini API)

Questions people ask

Is deepseek-v4-pro better than gemini-2.5-pro?
deepseek-v4-pro outperforms gemini-2.5-pro on 7 of 7 shared benchmarks. See the benchmark comparison above for specifics: deepseek-v4-pro and gemini-2.5-pro have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper, deepseek-v4-pro or gemini-2.5-pro?
deepseek-v4-pro is cheaper. deepseek-v4-pro costs $0.43/$0.87 per 1M input/output tokens, while gemini-2.5-pro costs $1.25/$10.00.
Can I use deepseek-v4-pro and gemini-2.5-pro through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch between "deepseek/deepseek-v4-pro" and "google/gemini-2.5-pro", no other code changes needed.
What are the context windows?
deepseek-v4-pro supports up to 1M tokens of context. gemini-2.5-pro supports up to 1.0M tokens. Longer context means you can feed larger documents or codebases in a single prompt, though quality often degrades past 128K for most models.

Switch between deepseek-v4-pro and gemini-2.5-pro with one line of code

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