GPT-5 vs Gemini 3.1 Pro — Pricing Comparison 2026

Requests per Day

Days per Month

OpenAI

GPT-5

$0.00

per month

Input cost

Output cost

Cost per request

Requests/month

Google

Gemini 3.1 Pro

$0.00

per month

Input cost

Output cost

Cost per request

Requests/month

Which Model for Which Use Case?

Code Generation & Debugging

Both handle coding well. GPT-5 is cheaper for standard coding tasks. Gemini 3.1 Pro's 1M context window is better for analyzing entire codebases or large refactoring tasks.

Cost-sensitive: GPT-5 (37% cheaper) | Large codebases: Gemini 3.1 Pro

Long-Document Processing

Gemini 3.1 Pro's 1M context window can process documents that GPT-5's 272K cannot fit. For legal documents, research papers, or large codebases, Gemini is the clear choice.

Best choice: Gemini 3.1 Pro (3.7x more context)

Chatbots & Customer Support

High-volume, shorter conversations where cost per request matters most. GPT-5's lower pricing makes it the winner for production chatbots at scale.

Best value: GPT-5 (22% cheaper)

RAG Pipelines & Search

RAG workloads have large inputs (retrieved context) and moderate outputs. GPT-5's lower input pricing wins for standard RAG. Gemini's larger context helps when retrieved chunks are very large.

Standard RAG: GPT-5 | Large context RAG: Gemini 3.1 Pro

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Frequently Asked Questions

Is GPT-5 cheaper than Gemini 3.1 Pro?

Yes. GPT-5 costs $1.25/M input and $10/M output. Gemini 3.1 Pro costs $2/M input and $12/M output. For a typical workload of 1M input + 500K output tokens/month, GPT-5 costs $6.25 vs Gemini 3.1 Pro's $8.00 — saving you $1.75/month (22%).

Which has a larger context window?

Gemini 3.1 Pro has a 1M token context window, while GPT-5 has 272K. Gemini 3.1 Pro offers 3.7x more context capacity, which is critical for processing very long documents, maintaining extended conversations, or handling large codebases in a single request.

Which is better for long-context tasks?

Gemini 3.1 Pro is the clear winner for long-context tasks with its 1M token context window (vs GPT-5's 272K). If your workload involves processing documents over 200K tokens, analyzing large codebases, or maintaining very long conversations, Gemini 3.1 Pro can handle it in a single request.

Can I switch between GPT-5 and Gemini 3.1 Pro easily?

Yes. Both use standard REST APIs with similar request/response formats. If you're using an abstraction layer (like LangChain, LiteLLM, or the OpenAI SDK with Google's endpoint), switching is straightforward. The main differences are in API structure (OpenAI vs Google AI Studio) and authentication methods.