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

Gemini 3.6 Pro vs OpenAI: GPT-4o-mini (batch) benchmark

Name: Gemini 3.6 Pro vs OpenAI: GPT-4o-mini (batch) AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 3.6 Pro (Google) and OpenAI: GPT-4o-mini (batch) (OpenAI) using the latest snapshots we have as of Aug 17, 2026. OpenAI: GPT-4o-mini (batch) wins on output price at $0.3/1M. Gemini 3.6 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 3.6 Pro

Current Google Pro-class multimodal model. Long context, strong coding, billed like the 3.x Pro tier.

Elo 1,570$5/1M out

OpenAI

OpenAI: GPT-4o-mini (batch)

Auto-discovered from OpenRouter (openai/gpt-4o-mini:batch). Preview until a second source matches.

$0.3/1M out

Gemini 3.6 Pro Inference Compute Profile

Inference-Time Compute & Reasoning EffortDynamic CoT

Gemini 3.6 Pro Reasoning Scaler & Token Billing Simulator

Param: thinking_budget

Reasoning models scale test-time compute by generating hidden chain-of-thought (CoT) tokens. These tokens are billed at standard output rates and directly expand latency (TTFT) in exchange for higher GPQA Diamond and SWE-bench Verified problem resolve rates.

Estimated TTFT

205 ms

1.0x (Standard eval)

CoT Thinking Tokens

~2,400

Billed as output tokens

Est. Cost / 1k Calls

$14.13

6.6x base invoice

STEM / SWE Boost

+4.8%

GPQA & SWE-bench scaling

Medium (8k tokens) Operational Profile

Balanced reasoning for coding and multimodal comprehension.

Direct Provider APIGoogle

"thinking": {
  "type": "enabled",
  "budget_tokens": 16384
}

OpenRouter Unified APIUnified Gateway

"reasoning": {
  "effort": "medium",
  "max_tokens": 2400
}

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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 3.6 Pro (8)vsOpenAI: GPT-4o-mini (batch) (2)

Gemini 3.6 Pro: 8W (80%)Overall: Gemini 3.6 ProOpenAI: GPT-4o-mini (batch): 2W (20%)

← Gemini 3.6 ProOpenAI: GPT-4o-mini (batch) →

Gemini 3.6 Pro (5/5)

Intelligence & Reasoning

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

Preference Elo

1,570vs—

Coding Elo

1,566vs—

SWE-bench

74.6%vs—

LiveBench

71.8%vs—

GPQA Diamond

84.4%vs—

Gemini 3.6 Pro: 5WOpenAI: GPT-4o-mini (batch): 0W

Gemini 3.6 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

102 tok/svs—

Time to first token

205 msvs—

Gemini 3.6 Pro: 2WOpenAI: GPT-4o-mini (batch): 0W

OpenAI: GPT-4o-mini (batch) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$0.3/1M

Input price

$1.25/1Mvs$0.075/1M

Context window

2Mvs128k

Gemini 3.6 Pro: 1WOpenAI: GPT-4o-mini (batch): 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 AdvantageGemini 3.6 Pro 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

OpenAI: GPT-4o-mini (batch)

$4.70/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,570

EloHigh is better

—

Code Elo

High is better

1,566

Code EloHigh is better

—

LiveBench

High is better

71.8%

LiveBenchHigh is better

—

SWE-bench

High is better

74.6%

SWE-benchHigh is better

—

GPQA

High is better

84.4%

GPQAHigh is better

—

TTFT

Low is better

205 ms

TTFTLow is better

—

Speed

High is better

102 tok/s

SpeedHigh is better

—

In $

Low is better

$1.25 / 1M

In $Low is better

$0.08 / 1M+1567%

Out $

Low is better

$5.00 / 1M

Out $Low is better

$0.30 / 1M+1567%

Context

High is better

+1463%2M

ContextHigh is better

128k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

Simulate monthly production API costs in USD (US Dollar).

Save up to 94% with OpenAI: GPT-4o-mini (batch)

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Gemini 3.6 Pro$118.75 / mo

In: $43.75Out: $75

OpenAI: GPT-4o-mini (batch)$7.13 / mo

In: $2.63Out: $4.5

Estimated Cost Delta

OpenAI: GPT-4o-mini (batch) is estimated to save $111.63/month ($1,340/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI: GPT-4o-mini (batch):Pick OpenAI: GPT-4o-mini (batch) when you are optimizing output cost.
2
Gemini 3.6 Pro:Pick Gemini 3.6 Pro for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI: GPT-4o-mini (batch)

Lower output list price ($0.3/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGemini 3.6 Pro

Larger window (2M).

Screenshots / visionGemini 3.6 Pro

Gemini 3.6 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	OpenAI: GPT-4o-mini (batch)	Lower output list price ($0.3/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Gemini 3.6 Pro	Larger window (2M).
Screenshots / vision	Gemini 3.6 Pro	Gemini 3.6 Pro is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

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Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Top Anthropic vs top Google. Usually Opus 5 vs 3.6 Pro. Two different invoices.

compareJul 16, 2026

Gemini vs GPT: long context versus the OpenAI flagship invoice

Hub remaps to top Google vs top OpenAI Elo. Usually 3.6 Pro vs Sol. Window vs price is the plot.

launchJul 15, 2026

Gemini 3.6 Pro: Google’s current Pro-class long-context row

2M context, Pro-class Elo and coding. Upgrade path from Flash is this page, not a vibe check.

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LIVE

Google

Gemini 3.6 Pro

Elo 1,570

LiveBench71.8%

SWE-bench74.6%

Speed102 tok/s

Output cost$5/1M

OpenAI

OpenAI: GPT-4o-mini (batch)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.3/1M

Frequently Asked Questions

Which is better overall, Gemini 3.6 Pro or OpenAI: GPT-4o-mini (batch)?: 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 3.6 Pro or OpenAI: GPT-4o-mini (batch)?: 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: GPT-4o-mini (batch) output tokens are $0.3/1M versus $5/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 3.6 Pro or OpenAI: GPT-4o-mini (batch)?: We do not have TTFT for both models.
Which has the larger context window?: Gemini 3.6 Pro accepts 2M tokens versus 128k.
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 3.6 Pro and OpenAI: GPT-4o-mini (batch) Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Gemini 3.6 Pro matchups →|All OpenAI: GPT-4o-mini (batch) matchups →

Gemini 3.6 Pro vs Gemini 3 Pro (previous gemini-pro)GPT-5 vs Gemini 3.6 Pro GPT-5.6 Terra vs Gemini 3.6 Pro GPT-4.5 Orion vs Gemini 3.6 Pro GPT-5.6 Sol vs Gemini 3.6 Pro Gemini 3.7 Flash vs Gemini 3.6 Pro

Gemini 3.6 Pro vs OpenAI: GPT-4o-mini (batch) benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 3.6 Pro (8)vsOpenAI: GPT-4o-mini (batch) (2)

Gemini 3.6 Pro: 8W (80%)Overall: Gemini 3.6 ProOpenAI: GPT-4o-mini (batch): 2W (20%)

← Gemini 3.6 ProOpenAI: GPT-4o-mini (batch) →

Gemini 3.6 Pro (5/5)

Intelligence & Reasoning

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

Preference Elo

1,570vs—

Coding Elo

1,566vs—

SWE-bench

74.6%vs—

LiveBench

71.8%vs—

GPQA Diamond

84.4%vs—

Gemini 3.6 Pro: 5WOpenAI: GPT-4o-mini (batch): 0W

Gemini 3.6 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

102 tok/svs—

Time to first token

205 msvs—

Gemini 3.6 Pro: 2WOpenAI: GPT-4o-mini (batch): 0W

OpenAI: GPT-4o-mini (batch) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$5/1Mvs$0.3/1M

Input price

$1.25/1Mvs$0.075/1M

Context window

2Mvs128k

Gemini 3.6 Pro: 1WOpenAI: GPT-4o-mini (batch): 2W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI: GPT-4o-mini (batch)

Lower output list price ($0.3/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGGemini 3.6 Pro

Larger window (2M).

Screenshots / visionGemini 3.6 Pro

Gemini 3.6 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	OpenAI: GPT-4o-mini (batch)	Lower output list price ($0.3/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Gemini 3.6 Pro	Larger window (2M).
Screenshots / vision	Gemini 3.6 Pro	Gemini 3.6 Pro is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Gemini 3.6 Pro or OpenAI: GPT-4o-mini (batch)?

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 3.6 Pro or OpenAI: GPT-4o-mini (batch)?

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: GPT-4o-mini (batch) output tokens are $0.3/1M versus $5/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 3.6 Pro or OpenAI: GPT-4o-mini (batch)?

We do not have TTFT for both models.

Which has the larger context window?

Gemini 3.6 Pro accepts 2M tokens versus 128k.

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 3.6 Pro and OpenAI: GPT-4o-mini (batch) Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Gemini 3.6 Pro Reasoning Scaler & Token Billing Simulator

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

No shared data

No shared data

OpenAI: GPT-4o-mini (batch)

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Gemini Flash vs Pro: when Flash is the whole product

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Gemini vs GPT: long context versus the OpenAI flagship invoice

Gemini 3.6 Pro: Google’s current Pro-class long-context row

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

Gemini 3.6 Pro

OpenAI: GPT-4o-mini (batch)

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Gemini 3.6 Pro Reasoning Scaler & Token Billing Simulator

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

No shared data

No shared data

OpenAI: GPT-4o-mini (batch)

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Gemini Flash vs Pro: when Flash is the whole product

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Gemini vs GPT: long context versus the OpenAI flagship invoice

Gemini 3.6 Pro: Google’s current Pro-class long-context row

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Gemini 3.6 Pro

OpenAI: GPT-4o-mini (batch)

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons