💰 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 OpenAI o3 71.7% SWE-bench · 95.2% HumanEval
🧠 STEM Reasoning Winner OpenAI o3 87.7% GPQA · 98.6% MATH-500

Which model is better: Gemini 2.0 Flash or OpenAI o3?

Gemini 2.0 Flash is 95.0% cheaper for input tokens ($0.10 vs. $2.00 per 1M tokens) and $0.40 vs. $8.00 for output tokens (20.0x cost advantage). In terms of operational performance, Gemini 2.0 Flash delivers lower response latency at 280ms TTFT (870ms faster than OpenAI o3) with 185 vs. 82 tok/s throughput. OpenAI o3 leads coding evaluations at 71.7% SWE-bench Verified vs. 51.8% for Gemini 2.0 Flash and 87.7% vs. 63.6% 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 OpenAI o3 Net Delta / Advantage
Input Cost / 1M Tokens $0.10 $2.00 Gemini 2.0 Flash is 20.0x cheaper
Output Cost / 1M Tokens $0.40 $8.00 Gemini 2.0 Flash (95.0% lower)
Prompt Cache Read / 1M $0.025 $0.500 Gemini 2.0 Flash is 20.0x cheaper
Prompt Cache Write / 1M $0.10 $2.00 Gemini 2.0 Flash is 20.0x cheaper
Context Window (Max Output) 1,073k (8k out) 205k (100k out) Gemini 2.0 Flash (5.2x larger context)
Avg TTFT Response Latency 280ms 1150ms Gemini 2.0 Flash (870ms faster)
Generation Speed (Throughput) 185 tok/s 82 tok/s Gemini 2.0 Flash (+103 tok/s faster)
SWE-bench Verified (Coding) 51.8% 71.7% OpenAI o3 (+19.9% lead)
GPQA Diamond (PhD STEM) 63.6% 87.7% OpenAI o3 (+24.1% lead)
MMLU-Pro (Multi-Discipline) 78.2% 89.8% OpenAI o3 (+11.6% lead)
MATH-500 (Quantitative Logic) 91.1% 98.6% OpenAI o3 (+7.5% lead)
Inference Value Score (IVR) 77.7 / 100 60.6 / 100 Gemini 2.0 Flash (+17.1 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

OpenAI o3 $71.25

$2.00/1M in · $8.00/1M out

Prompt Cache: $0.500/1M read · $2.00/1M write

Projected Monthly Cost Reduction
$67.69 / mo
(95.0% 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 OpenAI o3

Choose OpenAI o3 (OpenAI) for algorithmic synthesis, mathematical verification, PhD-level scientific research, and multi-step tool planning — offering a 204k context window, 1150ms TTFT, and $2.00/1M input pricing ($0.500/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: Gemini 2.0 Flash or OpenAI o3?

Gemini 2.0 Flash is cheaper at $0.10/1M input tokens ($0.40/1M output) compared to $2.00/1M input ($8.00/1M output) for OpenAI o3.

Which model has lower TTFT latency and higher tok/s throughput: Gemini 2.0 Flash or OpenAI o3?

Gemini 2.0 Flash delivers faster first-token latency at 280ms TTFT (185 tok/s throughput) compared to 1150ms TTFT (82 tok/s) for OpenAI o3.

How do Gemini 2.0 Flash and OpenAI o3 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Gemini 2.0 Flash scores 51.8% versus 71.7% for OpenAI o3. On GPQA Diamond graduate-level STEM reasoning, Gemini 2.0 Flash scores 63.6% versus 87.7% for OpenAI o3 (and 91.1% vs 98.6% 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 OpenAI o3?

Choose OpenAI o3 (OpenAI) for algorithmic synthesis, mathematical verification, PhD-level scientific research, and multi-step tool planning — offering a 204k context window, 1150ms TTFT, and $2.00/1M input pricing ($0.500/1M cached reads).

Explore High-Priority Related Model Matchups