Pairwise benchmark snapshot · Aug 16, 2026

Gemini 1.5 Pro vs OpenAI: o3 Mini benchmark

Name: Gemini 1.5 Pro vs OpenAI: o3 Mini AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 1.5 Pro (Google) and OpenAI: o3 Mini (OpenAI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, OpenAI: o3 Mini is ahead by 300 points (1,560 vs 1,260; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, OpenAI: o3 Mini resolves 78.5% versus 38% (seed-bootstrap (Aug 1, 2026)). OpenAI: o3 Mini streams faster (92 tok/s) while OpenAI: o3 Mini is the cheaper output token ($4.4/1M). 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.

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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: o3 Mini

Auto-discovered from OpenRouter (openai/o3-mini). Preview until a second source matches.

Elo 1,560$4.4/1M out

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Model A

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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (2)vsOpenAI: o3 Mini (8)

Gemini 1.5 Pro: 2W (20%)Overall: OpenAI: o3 MiniOpenAI: o3 Mini: 8W (80%)

← Gemini 1.5 ProOpenAI: o3 Mini →

OpenAI: o3 Mini (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,560

Coding Elo

—vs1,585

SWE-bench

38%vs78.5%

LiveBench

49.2%vs72.4%

GPQA Diamond

58%vs87.2%

Gemini 1.5 Pro: 0WOpenAI: o3 Mini: 5W

Tied (1/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs92 tok/s

Time to first token

240 msvs290 ms

Gemini 1.5 Pro: 1WOpenAI: o3 Mini: 1W

OpenAI: o3 Mini (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$4.4/1M

Input price

$1.25/1Mvs$1.1/1M

Context window

2Mvs200k

Gemini 1.5 Pro: 1WOpenAI: o3 Mini: 2W

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: o3 Mini 5/6

Preference Elo

OpenAI: o3 Mini

300 pts advantage

Throughput Speed

OpenAI: o3 Mini

22 tok/s faster

Price Efficiency

OpenAI: o3 Mini

$0.60/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,260

EloHigh is better

1,560+24%

Code Elo

High is better

—

Code EloHigh is better

1,585

LiveBench

High is better

49.2%

LiveBenchHigh is better

72.4%+47%

SWE-bench

High is better

38%

SWE-benchHigh is better

78.5%+107%

GPQA

High is better

58%

GPQAHigh is better

87.2%+50%

TTFT

Low is better

+21%240 ms

TTFTLow is better

290 ms

Speed

High is better

70 tok/s

SpeedHigh is better

92 tok/s+31%

In $

Low is better

$1.25/1M

In $Low is better

$1.1/1M+14%

Out $

Low is better

$5/1M

Out $Low is better

$4.4/1M+14%

Context

High is better

+900%2M

ContextHigh is better

200k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 12% with OpenAI: o3 Mini

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: o3 Mini$104.50 / mo

In: $38.50Out: $66.00

Estimated Cost Delta

OpenAI: o3 Mini is estimated to save $14.25/month ($171/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI: o3 Mini:Pick OpenAI: o3 Mini when repo-level coding accuracy is the constraint.
2
Gemini 1.5 Pro:Pick Gemini 1.5 Pro when time-to-first-token matters more than peak Elo.
3
Gemini 1.5 Pro:Pick Gemini 1.5 Pro for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsOpenAI: o3 Mini

Higher SWE-bench (78.5%).

High-volume chatOpenAI: o3 Mini

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

Voice / low-latency UIGemini 1.5 Pro

Lower TTFT (240 ms).

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	OpenAI: o3 Mini	Higher SWE-bench (78.5%).
High-volume chat	OpenAI: o3 Mini	Lower output list price ($4.4/1M).
Voice / low-latency UI	Gemini 1.5 Pro	Lower TTFT (240 ms).
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.

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LIVE

Google

Gemini 1.5 Pro

Elo 1,260

LiveBench49.2%

SWE-bench38%

Speed70 tok/s

Output cost$5/1M

OpenAI

OpenAI: o3 Mini

Elo 1,560

LiveBench72.4%

SWE-bench78.5%

Speed92 tok/s

Output cost$4.4/1M

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or OpenAI: o3 Mini?: OpenAI: o3 Mini has the higher preference Elo in our latest snapshot (1,560). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Gemini 1.5 Pro or OpenAI: o3 Mini?: OpenAI: o3 Mini leads SWE-bench at 78.5% vs 38%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: OpenAI: o3 Mini output tokens are $4.4/1M versus $5/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 1.5 Pro or OpenAI: o3 Mini?: Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Gemini 1.5 Pro accepts 2M tokens versus 200k.
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: o3 Mini 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: o3 Mini matchups →

Gemini 1.5 Pro vs Gemini 2.5 Pro (next gemini-pro)OpenAI: o3 Mini vs OpenAI o1-mini (previous gpt-mini)OpenAI: o3 Mini vs GPT-5 mini (next gpt-mini)GPT-5 vs OpenAI: o3 Mini Gemini 3 Pro vs OpenAI: o3 Mini Gemini 3 Flash vs OpenAI: o3 Mini

Gemini 1.5 Pro vs OpenAI: o3 Mini benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (2)vsOpenAI: o3 Mini (8)

Gemini 1.5 Pro: 2W (20%)Overall: OpenAI: o3 MiniOpenAI: o3 Mini: 8W (80%)

← Gemini 1.5 ProOpenAI: o3 Mini →

OpenAI: o3 Mini (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,560

Coding Elo

—vs1,585

SWE-bench

38%vs78.5%

LiveBench

49.2%vs72.4%

GPQA Diamond

58%vs87.2%

Gemini 1.5 Pro: 0WOpenAI: o3 Mini: 5W

Tied (1/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs92 tok/s

Time to first token

240 msvs290 ms

Gemini 1.5 Pro: 1WOpenAI: o3 Mini: 1W

OpenAI: o3 Mini (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$4.4/1M

Input price

$1.25/1Mvs$1.1/1M

Context window

2Mvs200k

Gemini 1.5 Pro: 1WOpenAI: o3 Mini: 2W

Recommended Workload Routing

Repo / coding agentsOpenAI: o3 Mini

Higher SWE-bench (78.5%).

High-volume chatOpenAI: o3 Mini

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

Voice / low-latency UIGemini 1.5 Pro

Lower TTFT (240 ms).

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	OpenAI: o3 Mini	Higher SWE-bench (78.5%).
High-volume chat	OpenAI: o3 Mini	Lower output list price ($4.4/1M).
Voice / low-latency UI	Gemini 1.5 Pro	Lower TTFT (240 ms).
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: o3 Mini?

OpenAI: o3 Mini has the higher preference Elo in our latest snapshot (1,560). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Gemini 1.5 Pro or OpenAI: o3 Mini?

OpenAI: o3 Mini leads SWE-bench at 78.5% vs 38%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

OpenAI: o3 Mini output tokens are $4.4/1M versus $5/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 1.5 Pro or OpenAI: o3 Mini?

Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Gemini 1.5 Pro accepts 2M tokens versus 200k.

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: o3 Mini Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.