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GPT-5 vs Gemini 3.1 Pro: Complete Pricing & Performance Comparison (May 2026)

OpenAI's GPT-5 and Google's Gemini 3.1 Pro are the two most popular mid-tier flagship models in 2026. Both offer strong reasoning, large context windows, and competitive pricing — but they're priced differently and optimized for different workloads.

We compare every dimension that matters: input/output cost, context window, speed, quality, and real-world monthly spend across common workload sizes.

Head-to-Head: Pricing Comparison

Feature	GPT-5 (OpenAI)	Gemini 3.1 Pro (Google)
Input ($/1M tokens)	$1.25	$2.00
Output ($/1M tokens)	$10.00	$12.00
Context Window	272K tokens	1M tokens
Tier	Premium	Mid
Input cost vs competitor	37% cheaper	60% more expensive
Context vs competitor	3.7x smaller	3.7x larger

GPT-5 costs 37% less on input tokens and 17% less on output tokens. But Gemini 3.1 Pro offers 3.7x more context (1M vs 272K). The right choice depends on whether you prioritize cost or context length.

Monthly Cost Scenarios

Small App: 1K requests/day, 2K tokens avg

Medium App: 10K requests/day, 3K tokens avg

Scale App: 50K requests/day, 2K tokens avg

At every workload size, GPT-5 saves you exactly 37% on input costs compared to Gemini 3.1 Pro. Over a year at scale, that's $24,300 in savings.

When Gemini 3.1 Pro Wins: The Context Advantage

Gemini 3.1 Pro's 1M token context window is 3.7x larger than GPT-5's 272K. This matters for:

• Long document analysis: Processing entire codebases, legal contracts, or research papers in a single prompt
• Large codebases: Analyzing 50K+ line projects without chunking
• Multi-turn conversations: Maintaining 100+ message conversations without losing context
• RAG with large retrieval sets: Fitting more retrieved documents into the context window

If your workload involves processing very long inputs (50K+ tokens per request), Gemini 3.1 Pro's larger context may justify the higher price — especially if the alternative is splitting requests or implementing complex chunking logic.

When GPT-5 Wins: Cost Efficiency

For most production workloads, GPT-5's lower cost makes it the better choice:

• High-volume APIs: Chatbots, classification, summarization at scale
• Short-to-medium inputs: Most requests under 50K tokens don't need 1M context
• Cost-sensitive applications: When every dollar matters at scale
• Multi-model routing: Use GPT-5 as the default, upgrade to Gemini only when context exceeds 272K

Budget Alternatives to Both

Neither GPT-5 nor Gemini 3.1 Pro is the cheapest option. If cost is the primary concern, consider these alternatives:

Model	Input ($/1M)	Output ($/1M)	Context	vs GPT-5
GPT-5 mini	$0.25	$2.00	272K	80% cheaper
Gemini 2.0 Flash	$0.10	$0.40	1M	92% cheaper
DeepSeek V4 Pro	$0.44	$0.87	1M	65% cheaper
Mistral Large 3	$0.50	$1.50	128K	60% cheaper
GPT-oss 120B	$0.15	$0.60	128K	88% cheaper

GPT-5 mini offers near-GPT-5 quality at 80% lower cost. For most workloads, it's the better value. Use GPT-5 or Gemini 3.1 Pro only when you need the full flagship capability.