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gemini-2.5-pro vs deepseek-ai/DeepSeek-R1

Side-by-side comparison of gemini-2.5-pro and deepseek-ai/DeepSeek-R1 — benchmarks, pricing, context window and capabilities. Both are accessible through Requesty's unified API. gemini-2.5-pro outperforms deepseek-ai/DeepSeek-R1 on 7 of 8 shared benchmarks.

Benchmark comparison

MMLU Proknowledge
gemini-2.5-pro86.2%
deepseek-ai/DeepSeek-R185.0%
GPQA Diamondreasoning
gemini-2.5-pro84.0%
deepseek-ai/DeepSeek-R171.5%
HumanEvalcoding
gemini-2.5-pro93.2%
deepseek-ai/DeepSeek-R192.0%
SWE-Bench Verifiedcoding
gemini-2.5-pro63.8%
deepseek-ai/DeepSeek-R149.2%
MATHmath
gemini-2.5-pro91.4%
deepseek-ai/DeepSeek-R192.2%
AIME 2024math
gemini-2.5-pro88.0%
deepseek-ai/DeepSeek-R179.8%
MMMUmultimodal
gemini-2.5-pro81.7%
deepseek-ai/DeepSeek-R174.8%
LiveBenchreasoning
gemini-2.5-pro73.6%
deepseek-ai/DeepSeek-R171.5%
τ-bench Retailagentic
gemini-2.5-pro69.8%
deepseek-ai/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-ai/DeepSeek-R1
Input price / 1M$1.25$3.00
Output price / 1M$10.00$7.00
Context window1.0M tokens64K tokens
Max output66K tokens8K tokens
Vision inputYes
Tool callingYes
ReasoningYes
Prompt cachingYes
Computer use
ProviderGoogle LLC (Gemini API)Together AI Inc.

Questions people ask

Is gemini-2.5-pro better than deepseek-ai/DeepSeek-R1?
gemini-2.5-pro outperforms deepseek-ai/DeepSeek-R1 on 7 of 8 shared benchmarks. See the benchmark comparison above for specifics — gemini-2.5-pro and deepseek-ai/DeepSeek-R1 have different strengths across reasoning, coding, math and multimodal tasks.
Which is cheaper — gemini-2.5-pro or deepseek-ai/DeepSeek-R1?
deepseek-ai/DeepSeek-R1 is cheaper. gemini-2.5-pro costs $1.25/$10.00 per 1M input/output tokens, while deepseek-ai/DeepSeek-R1 costs $3.00/$7.00.
Can I use gemini-2.5-pro and deepseek-ai/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 "together/deepseek-ai/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-ai/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-ai/DeepSeek-R1 with one line of code

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