Pairwise benchmark snapshot · Aug 16, 2026noindex (thin pair)

Google: Nano Banana Pro (Gemini 3 Pro Image Preview) vs OpenAI o1-mini benchmark

Name: Google: Nano Banana Pro (Gemini 3 Pro Image Preview) vs OpenAI o1-mini AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Google: Nano Banana Pro (Gemini 3 Pro Image Preview) (Google) and OpenAI o1-mini (OpenAI) using the latest snapshots we have as of Aug 16, 2026. OpenAI o1-mini wins on output price at $4.4/1M. OpenAI o1-mini has the larger context window (128k 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.

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Google

Google: Nano Banana Pro (Gemini 3 Pro Image Preview)

Auto-discovered from OpenRouter (google/gemini-3-pro-image-preview). Preview until a second source matches.

$12/1M out

OpenAI

OpenAI o1-mini

OpenAI high-speed, cost-effective reasoning model optimized for STEM, math, and code generation.

Elo 1,445$4.4/1M out

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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.

Google: Nano Banana Pro (Gemini 3 Pro Image Preview) (0)vsOpenAI o1-mini (10)

Google: Nano Banana Pro (Gemini 3 Pro Image Preview): 0W (0%)Overall: OpenAI o1-miniOpenAI o1-mini: 10W (100%)

← Google: Nano Banana Pro (Gemini 3 Pro Image Preview)OpenAI o1-mini →

OpenAI o1-mini (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,445

Coding Elo

—vs1,468

SWE-bench

—vs56.4%

LiveBench

—vs62%

GPQA Diamond

—vs76.8%

Google: Nano Banana Pro (Gemini 3 Pro Image Preview): 0WOpenAI o1-mini: 5W

OpenAI o1-mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs85 tok/s

Time to first token

—vs280 ms

Google: Nano Banana Pro (Gemini 3 Pro Image Preview): 0WOpenAI o1-mini: 2W

OpenAI o1-mini (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$12/1Mvs$4.4/1M

Input price

$2/1Mvs$1.1/1M

Context window

66kvs128k

Google: Nano Banana Pro (Gemini 3 Pro Image Preview): 0WOpenAI o1-mini: 3W

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 AdvantageOpenAI o1-mini 6/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

OpenAI o1-mini

$7.60/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,445

Code Elo

High is better

—

Code EloHigh is better

1,468

LiveBench

High is better

—

LiveBenchHigh is better

62%

SWE-bench

High is better

—

SWE-benchHigh is better

56.4%

GPQA

High is better

—

GPQAHigh is better

76.8%

TTFT

Low is better

—

TTFTLow is better

280 ms

Speed

High is better

—

SpeedHigh is better

85 tok/s

In $

Low is better

$2/1M

In $Low is better

$1.1/1M+82%

Out $

Low is better

$12/1M

Out $Low is better

$4.4/1M+173%

Context

High is better

66k

ContextHigh is better

128k+95%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 58% with OpenAI o1-mini

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Google: Nano Banana Pro (Gemini 3 Pro Image Preview)$250.00 / mo

In: $70.00Out: $180.00

OpenAI o1-mini$104.50 / mo

In: $38.50Out: $66.00

Estimated Cost Delta

OpenAI o1-mini is estimated to save $145.50/month ($1,746/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI o1-mini:Pick OpenAI o1-mini when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI o1-mini

Lower output list price ($4.4/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGOpenAI o1-mini

Larger window (128k).

Screenshots / visionOpenAI o1-mini

OpenAI o1-mini 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	OpenAI o1-mini	Lower output list price ($4.4/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	OpenAI o1-mini	Larger window (128k).
Screenshots / vision	OpenAI o1-mini	OpenAI o1-mini is the side marked multimodal in the catalog.

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LIVE

Google

Google: Nano Banana Pro (Gemini 3 Pro Image Preview)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$12/1M

OpenAI

OpenAI o1-mini

Elo 1,445

LiveBench62%

SWE-bench56.4%

Speed85 tok/s

Output cost$4.4/1M

Frequently Asked Questions

Which is better overall, Google: Nano Banana Pro (Gemini 3 Pro Image Preview) or OpenAI o1-mini?: 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, Google: Nano Banana Pro (Gemini 3 Pro Image Preview) or OpenAI o1-mini?: 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?: OpenAI o1-mini output tokens are $4.4/1M versus $12/1M. Input prices and retry rates still move the real bill.
Which is faster, Google: Nano Banana Pro (Gemini 3 Pro Image Preview) or OpenAI o1-mini?: We do not have TTFT for both models.
Which has the larger context window?: OpenAI o1-mini accepts 128k tokens versus 66k.
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. Google: Nano Banana Pro (Gemini 3 Pro Image Preview) and OpenAI o1-mini Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Google: Nano Banana Pro (Gemini 3 Pro Image Preview) matchups →|All OpenAI o1-mini matchups →

OpenAI o1-mini vs GPT-4o mini (previous gpt-mini)OpenAI o1-mini vs OpenAI: o3 Mini (next gpt-mini)GPT-5 vs OpenAI o1-mini Gemini 3 Pro vs OpenAI o1-mini Gemini 3 Flash vs OpenAI o1-mini GPT-4.5 Orion vs OpenAI o1-mini

Google: Nano Banana Pro (Gemini 3 Pro Image Preview) vs OpenAI o1-mini benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Google: Nano Banana Pro (Gemini 3 Pro Image Preview) (0)vsOpenAI o1-mini (10)

Google: Nano Banana Pro (Gemini 3 Pro Image Preview): 0W (0%)Overall: OpenAI o1-miniOpenAI o1-mini: 10W (100%)

← Google: Nano Banana Pro (Gemini 3 Pro Image Preview)OpenAI o1-mini →

OpenAI o1-mini (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,445

Coding Elo

—vs1,468

SWE-bench

—vs56.4%

LiveBench

—vs62%

GPQA Diamond

—vs76.8%

Google: Nano Banana Pro (Gemini 3 Pro Image Preview): 0WOpenAI o1-mini: 5W

OpenAI o1-mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs85 tok/s

Time to first token

—vs280 ms

Google: Nano Banana Pro (Gemini 3 Pro Image Preview): 0WOpenAI o1-mini: 2W

OpenAI o1-mini (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$12/1Mvs$4.4/1M

Input price

$2/1Mvs$1.1/1M

Context window

66kvs128k

Google: Nano Banana Pro (Gemini 3 Pro Image Preview): 0WOpenAI o1-mini: 3W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI o1-mini

Lower output list price ($4.4/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGOpenAI o1-mini

Larger window (128k).

Screenshots / visionOpenAI o1-mini

OpenAI o1-mini 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	OpenAI o1-mini	Lower output list price ($4.4/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	OpenAI o1-mini	Larger window (128k).
Screenshots / vision	OpenAI o1-mini	OpenAI o1-mini is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Google: Nano Banana Pro (Gemini 3 Pro Image Preview) or OpenAI o1-mini?

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, Google: Nano Banana Pro (Gemini 3 Pro Image Preview) or OpenAI o1-mini?

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?

OpenAI o1-mini output tokens are $4.4/1M versus $12/1M. Input prices and retry rates still move the real bill.

Which is faster, Google: Nano Banana Pro (Gemini 3 Pro Image Preview) or OpenAI o1-mini?

We do not have TTFT for both models.

Which has the larger context window?

OpenAI o1-mini accepts 128k tokens versus 66k.

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. Google: Nano Banana Pro (Gemini 3 Pro Image Preview) and OpenAI o1-mini Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.