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gemini-2.5-pro vs deepseek-r1

Side-by-side comparison of gemini-2.5-pro and deepseek-r1— benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. gemini-2.5-pro outperforms deepseek-r1 on 7 of 8 shared benchmarks.

Benchmark comparison

MMLU Proknowledge
gemini-2.5-pro86.2%
deepseek-r185.0%
GPQA Diamondreasoning
gemini-2.5-pro84.0%
deepseek-r171.5%
HumanEvalcoding
gemini-2.5-pro93.2%
deepseek-r192.0%
SWE-Bench Verifiedcoding
gemini-2.5-pro63.8%
deepseek-r149.2%
MATHmath
gemini-2.5-pro91.4%
deepseek-r192.2%
AIME 2024math
gemini-2.5-pro88.0%
deepseek-r179.8%
MMMUmultimodal
gemini-2.5-pro81.7%
deepseek-r174.8%
LiveBenchreasoning
gemini-2.5-pro73.6%
deepseek-r171.5%
τ-bench Retailagentic
gemini-2.5-pro69.8%
deepseek-r1

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

gemini-2.5-prodeepseek-r1
Input price / 1M$1.25$4.00
Output price / 1M$10.00$4.00
Context window1.0M tokens64K tokens
Max output66K tokens
Vision inputYes
Tool callingYesYes
ReasoningYes
Prompt cachingYes
Computer use
ProviderGoogle LLC (Gemini API)Novita AI

Questions people ask

Is gemini-2.5-pro better than deepseek-r1?
gemini-2.5-pro outperforms deepseek-r1 on 7 of 8 shared benchmarks. See the benchmark comparison above for specifics — gemini-2.5-pro and deepseek-r1 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper — gemini-2.5-pro or deepseek-r1?
deepseek-r1 is cheaper. gemini-2.5-pro costs $1.25/$10.00 per 1M input/output tokens, while deepseek-r1 costs $4.00/$4.00.
Can I use gemini-2.5-pro and deepseek-r1 through the same API?
Yes. Requesty provides a single OpenAI-compatible API that routes to both. Change just the "model" parameter to switch — "google/gemini-2.5-pro" or "novita/deepseek/deepseek-r1" — no other code changes needed.
What are the context windows?
gemini-2.5-pro supports up to 1.0M tokens of context. deepseek-r1 supports up to 64K 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 gemini-2.5-pro and deepseek-r1 with one line of code

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