o3 vs Gemini 2.5 ProBenchmark Comparison 2026

Objective comparison based on public benchmarks updated weekly: Intelligence Index, GPQA Diamond, Chatbot Arena ELO, pricing and speed.

Overall winner (2026)

o3

4 of 7 criteria won

OpenAI

o3

Winner

Intelligence Index

38.4

Coding Index

38.4

4 criteria won

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Google

Gemini 2.5 Pro

Intelligence Index

34.6

Coding Index

32.0

3 criteria won

View full profile

Detailed Comparison

Critérioo3Gemini 2.5 Pro
Chatbot Arena ELO
Intelligence Index (AA)38.434.6
Coding Index (AA)38.432.0
GPQA Diamond83.0%84.4%
Input price ($/1M tok)$2.00$1.25
Output price ($/1M tok)$8.00$10.00
Context window200K tokens1.0M tokens
Speed (tokens/s)124 tok/s

✓ = winner in this criterion • Source: Artificial Analysis, LMArena, official APIs • Updated weekly

Technical Specifications

o3

Company
OpenAI
Context window
200K tokens
Input ($/1M tok)
$2.00
Output ($/1M tok)
$8.00
Speed
124 tok/s
Release
Apr 2025
Multimodal
Yes
Open Source
No
Official website
Visit

Gemini 2.5 Pro

Company
Google
Context window
1.0M tokens
Input ($/1M tok)
$1.25
Output ($/1M tok)
$10.00
Release
Jun 2025
Multimodal
Yes
Open Source
No
Official website
Visit

When to use o3 vs Gemini 2.5 Pro?

Choosing between o3 and Gemini 2.5 Pro depends on your use case, budget and technical requirements. Below, a practical guide based on benchmark data and each model's specifications.

Use o3 when:

OpenAI · Multimodal

  • Complex reasoning, math and advanced programming — reasoning models are optimized for problems requiring multiple logical steps
  • Processing images, PDFs and visual documents alongside text — useful for analyzing contracts, reports with charts and mixed content
  • Long document analysis — 200K tokens context window allows processing books, legal databases and extensive logs
  • AI agents with tool calling — workflow automation, external API integration and data pipelines
  • API integration in SaaS applications — direct API access with documented SLA
View full o3 profile

Use Gemini 2.5 Pro when:

Google · Multimodal

  • Complex reasoning, math and advanced programming — reasoning models are optimized for problems requiring multiple logical steps
  • High-volume token projects — at $1.25/1M input tokens, the cost per call is low enough for production use at scale
  • Processing images, PDFs and visual documents alongside text — useful for analyzing contracts, reports with charts and mixed content
  • Applications with audio input or output — transcription, call analysis and voice assistants
  • Long document analysis — 1.0M tokens context window allows processing books, legal databases and extensive logs
  • AI agents with tool calling — workflow automation, external API integration and data pipelines
  • API integration in SaaS applications — direct API access with documented SLA
View full Gemini 2.5 Pro profile
SWEN Verdict: o3 wins in more objective criteria in this comparison (4 vs 3). For most use cases, o3 offers better aggregate performance — but Gemini 2.5 Pro may be preferable if your project prioritizes complex reasoning, math and advanced programming.

Frequently Asked Questions

o3 or Gemini 2.5 Pro: which is better?

o3 wins in 4 of 7 criteria analyzed. Check the full table to choose based on your use case.

Where does this benchmark data come from?

Data is aggregated from Artificial Analysis (Intelligence Index, Coding Index) and Chatbot Arena/LMArena (ELO). Pricing and specs come from official APIs. Updated weekly.

What is the Intelligence Index?

The Intelligence Index is an aggregate score from Artificial Analysis that combines multiple academic benchmarks (MMLU, GPQA, LiveBench, etc.) into a single rating. The higher the score, the more capable the model is at reasoning tasks.

Is o3 cheaper than Gemini 2.5 Pro?

No. Gemini 2.5 Pro is cheaper: $1.25/1M input tokens vs $2/1M tokens for o3 — a 60% difference. For high-volume projects, Gemini 2.5 Pro can reduce costs substantially.

o3 or Gemini 2.5 Pro: which has a larger context window?

Gemini 2.5 Pro has the larger context window: 1.0M tokens vs 200K tokens. For long document analysis, extensive transcripts or full codebases, the larger context window is a decisive criterion.

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