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 Pro | GPT-4o mini | |
|---|---|---|
| Input / 1M tokens | $2 | $0.15 |
| Output / 1M tokens | $12 | $0.6 |
| Context window | 1,000,000 | 128,000 |
| 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.