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

Gemini 2.0 Flash Thinking vs OpenAI: GPT-4o-mini (batch) benchmark

Name: Gemini 2.0 Flash Thinking vs OpenAI: GPT-4o-mini (batch) AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 2.0 Flash Thinking (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 2.0 Flash Thinking has the larger context window (1M 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 2.0 Flash Thinking

Google experimental reasoning model that visualizes thoughts in real-time.

Elo 1,490$0.4/1M 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

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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 2.0 Flash Thinking (8)vsOpenAI: GPT-4o-mini (batch) (2)

Gemini 2.0 Flash Thinking: 8W (80%)Overall: Gemini 2.0 Flash ThinkingOpenAI: GPT-4o-mini (batch): 2W (20%)

← Gemini 2.0 Flash ThinkingOpenAI: GPT-4o-mini (batch) →

Gemini 2.0 Flash Thinking (5/5)

Intelligence & Reasoning

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

Preference Elo

1,490vs—

Coding Elo

1,515vs—

SWE-bench

66.8%vs—

LiveBench

67%vs—

GPQA Diamond

81%vs—

Gemini 2.0 Flash Thinking: 5WOpenAI: GPT-4o-mini (batch): 0W

Gemini 2.0 Flash Thinking (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

115 tok/svs—

Time to first token

220 msvs—

Gemini 2.0 Flash Thinking: 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

$0.4/1Mvs$0.3/1M

Input price

$0.1/1Mvs$0.08/1M

Context window

1Mvs128k

Gemini 2.0 Flash Thinking: 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 2.0 Flash Thinking 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

OpenAI: GPT-4o-mini (batch)

$0.10/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,490

EloHigh is better

—

Code Elo

High is better

1,515

Code EloHigh is better

—

LiveBench

High is better

67%

LiveBenchHigh is better

—

SWE-bench

High is better

66.8%

SWE-benchHigh is better

—

GPQA

High is better

81%

GPQAHigh is better

—

TTFT

Low is better

220 ms

TTFTLow is better

—

Speed

High is better

115 tok/s

SpeedHigh is better

—

In $

Low is better

$0.1/1M

In $Low is better

$0.08/1M+33%

Out $

Low is better

$0.4/1M

Out $Low is better

$0.3/1M+33%

Context

High is better

+719%1M

ContextHigh is better

128k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 25% 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 2.0 Flash Thinking$9.50 / mo

In: $3.50Out: $6.00

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

In: $2.63Out: $4.50

Estimated Cost Delta

OpenAI: GPT-4o-mini (batch) is estimated to save $2.38/month ($29/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 2.0 Flash Thinking:Pick Gemini 2.0 Flash Thinking 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 2.0 Flash Thinking

Larger window (1M).

Screenshots / visionGemini 2.0 Flash Thinking

Gemini 2.0 Flash Thinking 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 2.0 Flash Thinking	Larger window (1M).
Screenshots / vision	Gemini 2.0 Flash Thinking	Gemini 2.0 Flash Thinking is the side marked multimodal in the catalog.

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CompareLLM Matrix

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LIVE

Google

Gemini 2.0 Flash Thinking

Elo 1,490

LiveBench67%

SWE-bench66.8%

Speed115 tok/s

Output cost$0.4/1M

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 2.0 Flash Thinking 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 2.0 Flash Thinking 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 $0.4/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 2.0 Flash Thinking or OpenAI: GPT-4o-mini (batch)?: We do not have TTFT for both models.
Which has the larger context window?: Gemini 2.0 Flash Thinking accepts 1M 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 2.0 Flash Thinking 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 2.0 Flash Thinking matchups →|All OpenAI: GPT-4o-mini (batch) matchups →

Gemini 2.0 Flash Thinking vs Gemini 2.5 Flash (next gemini-flash)Gemini 3 Flash vs Gemini 2.0 Flash Thinking Gemini 3.6 Flash vs Gemini 2.0 Flash Thinking Gemini 3.7 Flash vs Gemini 2.0 Flash Thinking GPT-4.5 Orion vs Gemini 2.0 Flash Thinking GPT-5.6 Terra vs Gemini 2.0 Flash Thinking

Gemini 2.0 Flash Thinking 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 2.0 Flash Thinking (8)vsOpenAI: GPT-4o-mini (batch) (2)

Gemini 2.0 Flash Thinking: 8W (80%)Overall: Gemini 2.0 Flash ThinkingOpenAI: GPT-4o-mini (batch): 2W (20%)

← Gemini 2.0 Flash ThinkingOpenAI: GPT-4o-mini (batch) →

Gemini 2.0 Flash Thinking (5/5)

Intelligence & Reasoning

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

Preference Elo

1,490vs—

Coding Elo

1,515vs—

SWE-bench

66.8%vs—

LiveBench

67%vs—

GPQA Diamond

81%vs—

Gemini 2.0 Flash Thinking: 5WOpenAI: GPT-4o-mini (batch): 0W

Gemini 2.0 Flash Thinking (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

115 tok/svs—

Time to first token

220 msvs—

Gemini 2.0 Flash Thinking: 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

$0.4/1Mvs$0.3/1M

Input price

$0.1/1Mvs$0.08/1M

Context window

1Mvs128k

Gemini 2.0 Flash Thinking: 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 2.0 Flash Thinking

Larger window (1M).

Screenshots / visionGemini 2.0 Flash Thinking

Gemini 2.0 Flash Thinking 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 2.0 Flash Thinking	Larger window (1M).
Screenshots / vision	Gemini 2.0 Flash Thinking	Gemini 2.0 Flash Thinking is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Gemini 2.0 Flash Thinking 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 2.0 Flash Thinking 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 $0.4/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 2.0 Flash Thinking or OpenAI: GPT-4o-mini (batch)?

We do not have TTFT for both models.

Which has the larger context window?

Gemini 2.0 Flash Thinking accepts 1M 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 2.0 Flash Thinking and OpenAI: GPT-4o-mini (batch) Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.