AI model comparison

DeepSeek Reasoner vs Gemini 1.5 Pro

Compare recorded price, context, output limits, speed, latency, capabilities, and input/output support. Every missing value stays visible, and unsourced benchmarks are excluded.

DeepSeek Reasoner and Gemini 1.5 Pro both come up when teams are choosing a model for production work, and the honest answer usually depends on your workload rather than a leaderboard. This page puts their published specifications side by side — context window, token pricing, supported inputs and outputs — so you can see where they actually differ.

Side-by-sideReviewed by our team

DeepSeek

DeepSeek Reasoner

DeepSeek reasoning model for multi-step analysis, math, coding, and tools

Context
1M
Max output
384K
Input / 1M
$0.14
Output / 1M
$0.28
Output speed

Accepts

  • Text input

Produces

  • Text output
VS

Google

Gemini 1.5 Pro

Google's long-context multimodal model with up to 2M token windows.

Context
2M
Max output
8.2K
Input / 1M
$1.25
Output / 1M
$5.00
Output speed
65 t/s

Accepts

  • Text input
  • Image input
  • Audio input
  • Video input

Produces

  • Text output

At a glance

Decision snapshot

This is a directional summary of the values recorded in this directory—not a universal quality verdict. Test the finalists on your own prompts before committing.

Where DeepSeek Reasoner leads in recorded data

  • Maximum output: 384K
  • Input price: $0.14
  • Output price: $0.28

Where Gemini 1.5 Pro leads in recorded data

  • Context capacity: 2M

Context capacity

Higher listed limit

DeepSeek Reasoner:1M

Gemini 1.5 Pro:2M

Gemini 1.5 Pro

Maximum output

Higher listed limit

DeepSeek Reasoner:384K

Gemini 1.5 Pro:8.2K

DeepSeek Reasoner

Input price

Lower recorded price

DeepSeek Reasoner:$0.14

Gemini 1.5 Pro:$1.25

DeepSeek Reasoner

Output price

Lower recorded price

DeepSeek Reasoner:$0.28

Gemini 1.5 Pro:$5.00

DeepSeek Reasoner

Output speed

Higher recorded throughput

DeepSeek Reasoner:

Gemini 1.5 Pro:65 t/s

No complete comparison

Time to first token

Lower recorded latency

DeepSeek Reasoner:

Gemini 1.5 Pro:0.90s

No complete comparison

Intelligence Index

Higher directory index

DeepSeek Reasoner:

Gemini 1.5 Pro:75.8

No complete comparison

Evidence

Verified shared benchmarks

A score appears only when both records include a verified source. It is not treated as a universal ranking.

These two models haven't been measured on the same benchmark yet, so there's nothing meaningful to chart. Comparing scores from different tests would tell you more about the tests than the models — the specs and pricing above are the better guide here.

Specifications

Pricing, limits and capabilities

SpecificationDeepSeek ReasonerGemini 1.5 Pro
ProviderDeepSeekGoogle
Model familydeepseek-thinkingGemini 1.5
Accepted inputstexttext, image, audio, video
Produced outputstexttext
Context window1M2MRecorded lead
Max output384KRecorded lead8.2K
Intelligence Index75.8
Input price / 1M$0.14Recorded lead$1.25
Output price / 1M$0.28Recorded lead$5.00
Output speed65 t/s
Time to first token0.90s
Reasoning modeDocumentedNot documented
Tool callingDocumentedDocumented
Image inputNot documentedDocumented
Audio inputNot documentedDocumented
Structured outputNot documentedDocumented
LicenseOpen weightsProprietary
ReleasedDec 1, 2025May 14, 2024

Workload guidance

Which model should you choose?

On the published numbers, Gemini 1.5 Pro accepts the larger context window and DeepSeek Reasoner is cheaper per input token. Which matters more depends on whether your bottleneck is document size or spend — the table above has the exact figures.

Provenance

Sources and data limits

Prices and hosted performance can change. SyncDev records source references and clearly separates documented specifications from measured values; confirm commercial terms with the provider before deployment.

Questions people ask

It compares recorded context and output limits, token prices, output speed, time to first token, Intelligence Index, modality support, tool and structured-output capabilities, release information, and only source-linked benchmarks verified on both records. Missing information is labeled rather than inferred.

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Choose any two approved model records and review them with the same evidence rules.

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