Pairwise benchmark snapshot · Aug 16, 2026

Gemini 1.5 Pro vs OpenAI: o1 benchmark

Name: Gemini 1.5 Pro vs OpenAI: o1 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 1.5 Pro (Google) and OpenAI: o1 (OpenAI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, OpenAI: o1 is ahead by 252 points (1,512 vs 1,260; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, OpenAI: o1 resolves 48.9% versus 38% (seed-bootstrap (Aug 1, 2026)). Gemini 1.5 Pro streams faster (70 tok/s) while Gemini 1.5 Pro is the cheaper output token ($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: o1

Auto-discovered from OpenRouter (openai/o1). Preview until a second source matches.

Elo 1,512$60/1M 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 (5)vsOpenAI: o1 (5)

Gemini 1.5 Pro: 5W (50%)Overall: Even matchupOpenAI: o1: 5W (50%)

← Gemini 1.5 ProOpenAI: o1 →

OpenAI: o1 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,512

Coding Elo

—vs1,485

SWE-bench

38%vs48.9%

LiveBench

49.2%vs65.4%

GPQA Diamond

58%vs78%

Gemini 1.5 Pro: 0WOpenAI: o1: 5W

Gemini 1.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs45 tok/s

Time to first token

240 msvs420 ms

Gemini 1.5 Pro: 2WOpenAI: o1: 0W

Gemini 1.5 Pro (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$60/1M

Input price

$1.25/1Mvs$15/1M

Context window

2Mvs200k

Gemini 1.5 Pro: 3WOpenAI: o1: 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 AdvantageEven 3–3

Preference Elo

OpenAI: o1

252 pts advantage

Throughput Speed

Gemini 1.5 Pro

25 tok/s faster

Price Efficiency

Gemini 1.5 Pro

$55.00/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,260

EloHigh is better

1,512+20%

Code Elo

High is better

—

Code EloHigh is better

1,485

LiveBench

High is better

49.2%

LiveBenchHigh is better

65.4%+33%

SWE-bench

High is better

38%

SWE-benchHigh is better

48.9%+29%

GPQA

High is better

58%

GPQAHigh is better

78%+34%

TTFT

Low is better

+75%240 ms

TTFTLow is better

420 ms

Speed

High is better

+56%70 tok/s

SpeedHigh is better

45 tok/s

In $

Low is better

+1100%$1.25/1M

In $Low is better

$15/1M

Out $

Low is better

+1100%$5/1M

Out $Low is better

$60/1M

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 92% 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: o1$1,425.00 / mo

In: $525.00Out: $900.00

Estimated Cost Delta

Gemini 1.5 Pro is estimated to save $1,306.25/month ($15,675/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI: o1:Pick OpenAI: o1 when repo-level coding accuracy is the constraint.
2
Gemini 1.5 Pro:Pick Gemini 1.5 Pro when you are optimizing output cost.
3
Gemini 1.5 Pro:Pick Gemini 1.5 Pro when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsOpenAI: o1

Higher SWE-bench (48.9%).

High-volume chatGemini 1.5 Pro

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

Voice / low-latency UIGemini 1.5 Pro

Lower TTFT (240 ms).

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionOpenAI: o1

Both accept images. Defaulting to the higher-Elo side (OpenAI: o1).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI: o1	Higher SWE-bench (48.9%).
High-volume chat	Gemini 1.5 Pro	Lower output list price ($5/1M).
Voice / low-latency UI	Gemini 1.5 Pro	Lower TTFT (240 ms).
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	OpenAI: o1	Both accept images. Defaulting to the higher-Elo side (OpenAI: o1).

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-o1/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: o1

Elo 1,512

LiveBench65.4%

SWE-bench48.9%

Speed45 tok/s

Output cost$60/1M

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or OpenAI: o1?: OpenAI: o1 has the higher preference Elo in our latest snapshot (1,512). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Gemini 1.5 Pro or OpenAI: o1?: OpenAI: o1 leads SWE-bench at 48.9% vs 38%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Gemini 1.5 Pro output tokens are $5/1M versus $60/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 1.5 Pro or OpenAI: o1?: Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 420 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: o1 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: o1 matchups →

Gemini 1.5 Pro vs Gemini 2.5 Pro (next gemini-pro)OpenAI: o1 vs GPT-4o (previous gpt-flagship)OpenAI: o1 vs GPT-4.5 Orion (next gpt-flagship)GPT-5 vs OpenAI: o1 Gemini 3 Pro vs OpenAI: o1 Gemini 3 Flash vs OpenAI: o1

Gemini 1.5 Pro vs OpenAI: o1 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 1.5 Pro (5)vsOpenAI: o1 (5)

Gemini 1.5 Pro: 5W (50%)Overall: Even matchupOpenAI: o1: 5W (50%)

← Gemini 1.5 ProOpenAI: o1 →

OpenAI: o1 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,260vs1,512

Coding Elo

—vs1,485

SWE-bench

38%vs48.9%

LiveBench

49.2%vs65.4%

GPQA Diamond

58%vs78%

Gemini 1.5 Pro: 0WOpenAI: o1: 5W

Gemini 1.5 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

70 tok/svs45 tok/s

Time to first token

240 msvs420 ms

Gemini 1.5 Pro: 2WOpenAI: o1: 0W

Gemini 1.5 Pro (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$60/1M

Input price

$1.25/1Mvs$15/1M

Context window

2Mvs200k

Gemini 1.5 Pro: 3WOpenAI: o1: 0W

Recommended Workload Routing

Repo / coding agentsOpenAI: o1

Higher SWE-bench (48.9%).

High-volume chatGemini 1.5 Pro

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

Voice / low-latency UIGemini 1.5 Pro

Lower TTFT (240 ms).

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionOpenAI: o1

Both accept images. Defaulting to the higher-Elo side (OpenAI: o1).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI: o1	Higher SWE-bench (48.9%).
High-volume chat	Gemini 1.5 Pro	Lower output list price ($5/1M).
Voice / low-latency UI	Gemini 1.5 Pro	Lower TTFT (240 ms).
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	OpenAI: o1	Both accept images. Defaulting to the higher-Elo side (OpenAI: o1).

Frequently Asked Questions

Which is better overall, Gemini 1.5 Pro or OpenAI: o1?

OpenAI: o1 has the higher preference Elo in our latest snapshot (1,512). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Gemini 1.5 Pro or OpenAI: o1?

OpenAI: o1 leads SWE-bench at 48.9% vs 38%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Gemini 1.5 Pro output tokens are $5/1M versus $60/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 1.5 Pro or OpenAI: o1?

Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 420 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: o1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.