💰 Unit Cost Winner Gemini 2.5 Pro $1.25/1M in · $10.00/1M out
⚡ Latency & Speed Winner GPT-5 340ms TTFT · 105 tok/s
💻 Coding (SWE) Winner GPT-5 74.9% SWE-bench · 95.8% HumanEval
🧠 STEM Reasoning Winner GPT-5 85.4% GPQA · 98% MATH-500

Which model is better: Gemini 2.5 Pro or GPT-5?

Gemini 2.5 Pro is 50.0% cheaper for input tokens ($1.25 vs. $2.50 per 1M tokens) and $10.00 vs. $10.00 for output tokens (2.0x cost advantage). In terms of operational performance, GPT-5 delivers lower response latency at 340ms TTFT (360ms faster than Gemini 2.5 Pro) with 105 vs. 80 tok/s throughput. GPT-5 leads coding evaluations at 74.9% SWE-bench Verified vs. 63.8% for Gemini 2.5 Pro and 85.4% vs. 74.0% 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 2.5 Pro GPT-5 Net Delta / Advantage
Input Cost / 1M Tokens $1.25 $2.50 Gemini 2.5 Pro is 2.0x cheaper
Output Cost / 1M Tokens $10.00 $10.00 Identical Output Pricing
Prompt Cache Read / 1M $0.313 $0.625 Gemini 2.5 Pro is 2.0x cheaper
Prompt Cache Write / 1M $1.25 $2.50 Gemini 2.5 Pro is 2.0x cheaper
Context Window (Max Output) 1,024k (66k out) 410k (128k out) Gemini 2.5 Pro (2.5x larger context)
Avg TTFT Response Latency 700ms 340ms GPT-5 (360ms faster)
Generation Speed (Throughput) 80 tok/s 105 tok/s GPT-5 (+25 tok/s faster)
SWE-bench Verified (Coding) 63.8% 74.9% GPT-5 (+11.1% lead)
GPQA Diamond (PhD STEM) 74% 85.4% GPT-5 (+11.4% lead)
MMLU-Pro (Multi-Discipline) 85.2% 90.8% GPT-5 (+5.6% lead)
MATH-500 (Quantitative Logic) 95% 98% GPT-5 (+3.0% lead)
Inference Value Score (IVR) 56.8 / 100 59.2 / 100 GPT-5 (+2.4 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 2.5 Pro $69.53

$1.25/1M in · $10.00/1M out

Prompt Cache: $0.313/1M read · $1.25/1M write

GPT-5 $89.06

$2.50/1M in · $10.00/1M out

Prompt Cache: $0.625/1M read · $2.50/1M write

Projected Monthly Cost Reduction
$19.53 / mo
(21.9% lower cost with Gemini 2.5 Pro)
Open Full Matchup Analysis →
A

When to Choose Gemini 2.5 Pro

Choose Gemini 2.5 Pro (Google) for long-context document and video analysis, large codebase ingestion, and multimodal reasoning — offering a 1,024k context window, 700ms TTFT, and $1.25/1M input pricing ($0.312/1M cached reads).

B

When to Choose GPT-5

Choose GPT-5 (OpenAI) for complex enterprise automation, full-stack software engineering, multimodal agent orchestration, and scientific synthesis — offering a 409k context window, 340ms TTFT, and $2.50/1M input pricing ($0.625/1M cached reads).

Related Questions & Decision Factors

Which is cheaper: Gemini 2.5 Pro or GPT-5?

Gemini 2.5 Pro is cheaper at $1.25/1M input tokens ($10.00/1M output) compared to $2.50/1M input ($10.00/1M output) for GPT-5.

Which model has lower TTFT latency and higher tok/s throughput: Gemini 2.5 Pro or GPT-5?

GPT-5 delivers faster first-token latency at 340ms TTFT (105 tok/s throughput) compared to 700ms TTFT (80 tok/s) for Gemini 2.5 Pro.

How do Gemini 2.5 Pro and GPT-5 compare on SWE-bench coding and GPQA Diamond reasoning?

On SWE-bench Verified software engineering, Gemini 2.5 Pro scores 63.8% versus 74.9% for GPT-5. On GPQA Diamond graduate-level STEM reasoning, Gemini 2.5 Pro scores 74% versus 85.4% for GPT-5 (and 95% vs 98% on MATH-500).

When should you choose Gemini 2.5 Pro?

Choose Gemini 2.5 Pro (Google) for long-context document and video analysis, large codebase ingestion, and multimodal reasoning — offering a 1,024k context window, 700ms TTFT, and $1.25/1M input pricing ($0.312/1M cached reads).

When should you choose GPT-5?

Choose GPT-5 (OpenAI) for complex enterprise automation, full-stack software engineering, multimodal agent orchestration, and scientific synthesis — offering a 409k context window, 340ms TTFT, and $2.50/1M input pricing ($0.625/1M cached reads).

Explore High-Priority Related Model Matchups