What is the difference between GPT-5.1-Codex and Gemini 2.5 Pro Preview 06-05?

GPT-5.1-Codex is developed by OpenAI while Gemini 2.5 Pro Preview 06-05 is developed by Google AI. GPT-5.1-Codex has a 400K token context window vs Gemini 2.5 Pro Preview 06-05's 1.0M. You can compare their actual outputs across 37 challenges on Rival to see how they differ in practice.

Which is better, GPT-5.1-Codex or Gemini 2.5 Pro Preview 06-05?

It depends on your use case. GPT-5.1-Codex and Gemini 2.5 Pro Preview 06-05 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 37 challenges so you can judge which fits your needs best.

How much does GPT-5.1-Codex cost compared to Gemini 2.5 Pro Preview 06-05?

GPT-5.1-Codex costs $1.25/M input tokens and Gemini 2.5 Pro Preview 06-05 costs $1.25/M input tokens. Gemini 2.5 Pro Preview 06-05 is $0.00/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare GPT-5.1-Codex and Gemini 2.5 Pro Preview 06-05 on Rival?

This page shows a side-by-side comparison of GPT-5.1-Codex and Gemini 2.5 Pro Preview 06-05 across shared challenges. You can vote on which model produced the better output, and Pro users can create custom challenges to test both models with their own prompts.

Rival

Updated Nov 13, 2025

GPT-5.1-CodexvsGemini 2.5 Pro Preview 06-05

37 fights queued

Why GPT-5.1-Codex?

Leads 1 of 1 benchmarks

Stronger on SWE-bench Verified73.7% vs 67.2%

5 months newerNov 2025

Why Gemini 2.5 Pro Preview 06-05?

2.6x more context1.0M

GPT-5.1-CodexGemini 2.5 Pro Preview 06-05

Input price

$1.25/M

Output price

$10.00/M

Context

400K

1.0M

Released

Nov 2025

Jun 2025

Benchmarks1 common

SWE-bench Verified

73.7%+8.8%

67.2%

Favorites

GPT-5.1-CodexGemini 2.5 Pro Preview 06-05

Movie

The Shawshank Redemption

19948.7

Khiam 2000-2007

2007

Album

Kind of Blue

Miles Davis

THE

tricot

Book

Le città invisibili

Italo Calvino

The Hitch Hiker's Guide to the Galaxy

Douglas Adams

City

Kyoto

Japan

Kyoto

Japan

Game

Portal 2

Shooter, Puzzle4.6

Portal

Action, Puzzle4.5

12 / 37

Estimate Complexityreasoning

Tests an AI's ability to make educated estimates based on technical knowledge

GPT-5.1-Codex

Openai · Nov 2025text

To estimate the total FLOPs (floating-point operations) required to train GPT-3, we can use a common approximation for transformer models:

Total FLOPs ≈ 6 × Nparams × Ntokens

Where:

Nparams is the number of model parameters.
Ntokens is the number of training tokens.
The factor of 6 accounts for forward and backward passes, along with other computations involved in training.

For GPT-3:

Nparams ≈ 175 billion = 1.75 × 10¹¹
Ntokens ≈ 300 billion = 3.0 × 10¹¹

Plugging in:

6 × (1.75 × 10¹¹) × (3.0 × 10¹¹)
= 6 × 5.25 × 10²²
= 3.15 × 10²³ FLOPs

So the total estimated compute to train GPT-3 is ~3.15 × 10²³ FLOPs.

This is a rough but widely used approximation for large-scale transformer training.

GPT-5.1-Codex vs Gemini 2.5 Pro Preview 06-05

Which is better, GPT-5.1-Codex or Gemini 2.5 Pro Preview 06-05?