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By tokenmath Research Desk · Pricing verified 2026-07-06

Gemini 3.1 Pro vs GPT-4o mini: pricing & cost comparison

On input tokens, GPT-4o mini is the cheaper of the two — 93% less per million ($2 vs $0.15). On output, GPT-4o mini is 95% cheaper ($12 vs $0.6) — and since output is usually the dominant cost driver, that gap matters more than it looks.

Side by side

Gemini 3.1 ProGPT-4o mini
Input / 1M tokens$2$0.15
Output / 1M tokens$12$0.6
Context window1,000,000128,000
Token-count accuracy±3%exact
Cost — 10,000 input + 2,000 output tokens$0.044$0.0027

What a real request costs

Take a representative turn — 10,000 input + 2,000 output tokens. Gemini 3.1 Pro comes to $0.044, GPT-4o mini to $0.0027. Across 100,000 requests that's a $4130 swing in favour of GPT-4o mini. To run the numbers on your actual prompt, paste it into the calculator and toggle Compare across all models.

Different leagues

These two sit in different price tiers — Gemini 3.1 Pro runs roughly 16× the per-request cost of GPT-4o mini on the worked example — so they rarely compete for the same job. Reach for GPT-4o mini on high-volume, latency-sensitive work (classification, extraction, routing) and Gemini 3.1 Pro only where the harder reasoning earns its price. They're different vendors, so expect a different API and tokenizer: the OpenAI side counts exactly; the other lands within a few percent — budget a small calibration buffer when you switch.

See the full breakdown on the dedicated pages for Gemini 3.1 Pro and GPT-4o mini.

FAQ

Is Gemini 3.1 Pro or GPT-4o mini cheaper?
For a typical request (10,000 input + 2,000 output tokens), GPT-4o mini is cheaper — about 94% less, or roughly $4130 saved per 100,000 requests. Gemini 3.1 Pro runs $2/$12 per 1M input/output tokens; GPT-4o mini runs $0.15/$0.6.
Which has the larger context window?
Gemini 3.1 Pro, at 1,000,000 tokens versus 128,000.
How accurate are these token counts?
Gemini 3.1 Pro: Approximated with o200k_base; drift typically ~3% on English and code. GPT-4o mini: Exact tokenization via the canonical OpenAI vocab (o200k_base). The dollar math itself is exact once the token count is known.

Both prices are computed from tokenmath's verified pricing table. Rates sourced from ai.google.dev and openai.com, verified 2026-07-06. Vendor pricing changes often — confirm before you commit.

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