💰 Unit Cost Winner Gemini 2.0 Flash $0.10/1M in · $0.40/1M out
⚡ Latency & Speed Winner Gemini 2.0 Flash 280ms TTFT · 185 tok/s
💻 Coding (SWE) Winner Gemini 2.0 Flash 51.8% SWE-bench · 90.7% HumanEval
🧠 STEM Reasoning Winner Gemini 2.0 Flash 63.6% GPQA · 91.1% MATH-500

Which model is better: Gemini 2.0 Flash or GPT-4o?

Gemini 2.0 Flash is 96.0% cheaper for input tokens ($0.10 vs. $2.50 per 1M tokens) and $0.40 vs. $10.00 for output tokens (25.0x cost advantage). In terms of operational performance, Both models demonstrate comparable response latency around 280ms TTFT (185 vs. 100 tok/s). Gemini 2.0 Flash leads coding evaluations at 51.8% SWE-bench Verified vs. 48.0% for GPT-4o and 63.6% vs. 53.6% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.

Verified Performance & Unit Economics Delta (12 Dimensions)

Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.

Metric / Dimension Gemini 2.0 Flash GPT-4o Net Delta / Advantage
Input Cost / 1M Tokens $0.10 $2.50 Gemini 2.0 Flash is 25.0x cheaper
Output Cost / 1M Tokens $0.40 $10.00 Gemini 2.0 Flash (96.0% lower)
Prompt Cache Read / 1M $0.025 $1.250 Gemini 2.0 Flash is 50.0x cheaper
Prompt Cache Write / 1M $0.10 $2.50 Gemini 2.0 Flash is 25.0x cheaper
Context Window (Max Output) 1,073k (8k out) 131k (16k out) Gemini 2.0 Flash (8.2x larger context)
Avg TTFT Response Latency 280ms 280ms Identical Latency
Generation Speed (Throughput) 185 tok/s 100 tok/s Gemini 2.0 Flash (+85 tok/s faster)
SWE-bench Verified (Coding) 51.8% 48% Gemini 2.0 Flash (+3.8% lead)
GPQA Diamond (PhD STEM) 63.6% 53.6% Gemini 2.0 Flash (+10.0% lead)
MMLU-Pro (Multi-Discipline) 78.2% 75.9% Gemini 2.0 Flash (+2.3% lead)
MATH-500 (Quantitative Logic) 91.1% 76.6% Gemini 2.0 Flash (+14.5% lead)
Inference Value Score (IVR) 77.7 / 100 47.6 / 100 Gemini 2.0 Flash (+30.1 pts)

Interactive Monthly Token Economics & ROI Forecaster

Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.

25.0M Tokens
500K 50M 250M 500M+
5.0M Tokens
100K 25M 75M 150M+
50% Cached
0% (Uncached) 25% 50% (Typical Agent) 95% Max

Estimated Monthly Spend

Gemini 2.0 Flash $3.56

$0.10/1M in · $0.40/1M out

Prompt Cache: $0.025/1M read · $0.10/1M write

GPT-4o $96.88

$2.50/1M in · $10.00/1M out

Prompt Cache: $1.250/1M read · $2.50/1M write

Projected Monthly Cost Reduction
$93.31 / mo
(96.3% lower cost with Gemini 2.0 Flash)
Open Full Matchup Analysis →
A

When to Choose Gemini 2.0 Flash

Choose Gemini 2.0 Flash (Google DeepMind) for legacy high-throughput multimodal pipelines migrating to Gemini 3.8 Flash — offering a 1,073k context window, 280ms TTFT, and $0.10/1M input pricing ($0.025/1M cached reads).

B

When to Choose GPT-4o

Choose GPT-4o (OpenAI) for general enterprise assistants, multimodal customer support, and structured JSON output pipelines — offering a 131k context window, 280ms TTFT, and $2.50/1M input pricing ($1.250/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: Gemini 2.0 Flash or GPT-4o?

Gemini 2.0 Flash is cheaper at $0.10/1M input tokens ($0.40/1M output) compared to $2.50/1M input ($10.00/1M output) for GPT-4o.

Which model has lower TTFT latency and higher tok/s throughput: Gemini 2.0 Flash or GPT-4o?

Both Gemini 2.0 Flash and GPT-4o deliver comparable response times around 280ms TTFT.

How do Gemini 2.0 Flash and GPT-4o compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Gemini 2.0 Flash scores 51.8% versus 48% for GPT-4o. On GPQA Diamond graduate-level STEM reasoning, Gemini 2.0 Flash scores 63.6% versus 53.6% for GPT-4o (and 91.1% vs 76.6% on MATH-500).

When should you choose Gemini 2.0 Flash?

Choose Gemini 2.0 Flash (Google DeepMind) for legacy high-throughput multimodal pipelines migrating to Gemini 3.8 Flash — offering a 1,073k context window, 280ms TTFT, and $0.10/1M input pricing ($0.025/1M cached reads).

When should you choose GPT-4o?

Choose GPT-4o (OpenAI) for general enterprise assistants, multimodal customer support, and structured JSON output pipelines — offering a 131k context window, 280ms TTFT, and $2.50/1M input pricing ($1.250/1M cached reads).

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