Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

Gemini 1.5 Pro vs OpenAI: GPT-5.4 Image 2 benchmark

Name: Gemini 1.5 Pro vs OpenAI: GPT-5.4 Image 2 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 1.5 Pro (Google) and OpenAI: GPT-5.4 Image 2 (OpenAI) using the latest snapshots we have as of Aug 17, 2026. Gemini 1.5 Pro wins on output price at $5/1M. Gemini 1.5 Pro has the larger context window (2M tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

Share Analysis:

WhatsApp Telegram X / Post LinkedIn Reddit

Google

Gemini 1.5 Pro

First million-token Gemini Pro. Baseline for 1.5 vs 2.5 vs 3.x Pro.

Elo 1,260$5/1M out

OpenAI

OpenAI: GPT-5.4 Image 2

Auto-discovered from OpenRouter (openai/gpt-5.4-image-2). Preview until a second source matches.

$15/1M out

Swap or Add Any Model to this Matchup

Instant pairwise benchmark recalculation across 400+ LLMs

Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (9)vsOpenAI: GPT-5.4 Image 2 (0)

Gemini 1.5 Pro: 9W (100%)Overall: Gemini 1.5 ProOpenAI: GPT-5.4 Image 2: 0W (0%)

← Gemini 1.5 ProOpenAI: GPT-5.4 Image 2 →

Gemini 1.5 Pro (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,260vs—

Coding Elo

—vs—

SWE-bench

38%vs—

LiveBench

49.2%vs—

GPQA Diamond

58%vs—

Gemini 1.5 Pro: 4WOpenAI: GPT-5.4 Image 2: 0W

Gemini 1.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs—

Time to first token

240 msvs—

Gemini 1.5 Pro: 2WOpenAI: GPT-5.4 Image 2: 0W

Gemini 1.5 Pro (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$15/1M

Input price

$1.25/1Mvs$8/1M

Context window

2Mvs272k

Gemini 1.5 Pro: 3WOpenAI: GPT-5.4 Image 2: 0W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageGemini 1.5 Pro 6/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Gemini 1.5 Pro

$10.00/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,260

EloHigh is better

—

LiveBench

High is better

49.2%

LiveBenchHigh is better

—

SWE-bench

High is better

38%

SWE-benchHigh is better

—

GPQA

High is better

58%

GPQAHigh is better

—

TTFT

Low is better

240 ms

TTFTLow is better

—

Speed

High is better

70 tok/s

SpeedHigh is better

—

In $

Low is better

+540%$1.25/1M

In $Low is better

$8/1M

Out $

Low is better

+200%$5/1M

Out $Low is better

$15/1M

Context

High is better

+635%2M

ContextHigh is better

272k

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 76% with Gemini 1.5 Pro

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

Gemini 1.5 Pro$118.75 / mo

In: $43.75Out: $75.00

OpenAI: GPT-5.4 Image 2$505.00 / mo

In: $280.00Out: $225.00

Estimated Cost Delta

Gemini 1.5 Pro is estimated to save $386.25/month ($4,635/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Gemini 1.5 Pro:Pick Gemini 1.5 Pro when you are optimizing output cost.
2
Gemini 1.5 Pro:Pick Gemini 1.5 Pro for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatGemini 1.5 Pro

Lower output list price ($5/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionGemini 1.5 Pro

Gemini 1.5 Pro is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Gemini 1.5 Pro	Lower output list price ($5/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Gemini 1.5 Pro	Gemini 1.5 Pro is the side marked multimodal in the catalog.

Community Sentiment

Cast Your Matchup Vote

0 total votes

Community Discussions (0)

No comments posted on this matchup yet. Be the first to share an evaluation note!

Share card

The real PNG is generated at /compare/gemini-1-5-pro-vs-gpt-5-4-image-2/opengraph-image for crawlers.

PNG

CompareLLM Matrix

Verified head-to-head card

LIVE

Google

Gemini 1.5 Pro

Elo 1,260

LiveBench49.2%

SWE-bench38%

Speed70 tok/s

Output cost$5/1M

OpenAI

OpenAI: GPT-5.4 Image 2

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$15/1M

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or OpenAI: GPT-5.4 Image 2?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, Gemini 1.5 Pro or OpenAI: GPT-5.4 Image 2?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: Gemini 1.5 Pro output tokens are $5/1M versus $15/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 1.5 Pro or OpenAI: GPT-5.4 Image 2?: We do not have TTFT for both models.
Which has the larger context window?: Gemini 1.5 Pro accepts 2M tokens versus 272k.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. Gemini 1.5 Pro and OpenAI: GPT-5.4 Image 2 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Gemini 1.5 Pro matchups →|All OpenAI: GPT-5.4 Image 2 matchups →

Gemini 1.5 Pro vs Gemini 2.5 Pro (next gemini-pro)GPT-5 vs Gemini 1.5 Pro Gemini 3 Pro vs Gemini 1.5 Pro Gemini 3 Flash vs Gemini 1.5 Pro GPT-4.5 Orion vs Gemini 1.5 Pro Gemini 2.0 Flash Thinking vs Gemini 1.5 Pro

Gemini 1.5 Pro vs OpenAI: GPT-5.4 Image 2 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (9)vsOpenAI: GPT-5.4 Image 2 (0)

Gemini 1.5 Pro: 9W (100%)Overall: Gemini 1.5 ProOpenAI: GPT-5.4 Image 2: 0W (0%)

← Gemini 1.5 ProOpenAI: GPT-5.4 Image 2 →

Gemini 1.5 Pro (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,260vs—

Coding Elo

—vs—

SWE-bench

38%vs—

LiveBench

49.2%vs—

GPQA Diamond

58%vs—

Gemini 1.5 Pro: 4WOpenAI: GPT-5.4 Image 2: 0W

Gemini 1.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs—

Time to first token

240 msvs—

Gemini 1.5 Pro: 2WOpenAI: GPT-5.4 Image 2: 0W

Gemini 1.5 Pro (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$15/1M

Input price

$1.25/1Mvs$8/1M

Context window

2Mvs272k

Gemini 1.5 Pro: 3WOpenAI: GPT-5.4 Image 2: 0W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatGemini 1.5 Pro

Lower output list price ($5/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionGemini 1.5 Pro

Gemini 1.5 Pro is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Gemini 1.5 Pro	Lower output list price ($5/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Gemini 1.5 Pro	Gemini 1.5 Pro is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or OpenAI: GPT-5.4 Image 2?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, Gemini 1.5 Pro or OpenAI: GPT-5.4 Image 2?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

Gemini 1.5 Pro output tokens are $5/1M versus $15/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 1.5 Pro or OpenAI: GPT-5.4 Image 2?

We do not have TTFT for both models.

Which has the larger context window?

Gemini 1.5 Pro accepts 2M tokens versus 272k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. Gemini 1.5 Pro and OpenAI: GPT-5.4 Image 2 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.