LLM Price Watch

Gemini 3 Flash vs GPT-4o mini: which budget model wins on price?

GPT-4o mini is roughly 4x cheaper than Gemini 3 Flash on both input ($0.15 vs $0.50 per million tokens) and output ($0.60 vs $3.00). At 150M input/40M output tokens a month, that's $46.50 for GPT-4o mini versus $195 for Gemini 3 Flash. The gap that closes it: Gemini 3 Flash's 1M-token context window is nearly 8x GPT-4o mini's 128K.

Verified · pricing checked against Google and OpenAI's own API documentation
ModelInput /1MOutput /1MContext
Gemini 3 Flash$0.50$3.001M tokens
GPT-4o mini$0.15$0.60128K tokens

The price-vs-context trade-off

This is a cleaner trade-off than most model comparisons: GPT-4o mini wins decisively on price, Gemini 3 Flash wins decisively on context window, and neither pulls ahead on both. There's no pricing trick or hidden tier here — the 4x cost difference and the 8x context difference are both real and both consistent across volume.

Worked example

At a 150M input/40M output token monthly workload — a realistic high-volume budget-tier task like classification or short-form generation — GPT-4o mini costs $46.50/month against Gemini 3 Flash's $195/month. At 10x that volume (1.5B input/400M output), the gap becomes $465 versus $1,950/month — the absolute dollar difference scales linearly, so it's worth deciding early which side of this trade-off actually matters for your workload.

How we'd actually decide

Default to GPT-4o mini for high-volume, well-defined tasks — classification, extraction, short-form generation — where 128K tokens is plenty and the 4x price gap adds up fast at scale. Reach for Gemini 3 Flash specifically when a task needs to hold long documents, extensive chat history, or large codebases in a single context window; paying 4x for a 128K-vs-1M gap you don't actually need is the most common budget-tier mistake in this comparison.

Pricing verified 15 July 2026, non-cached list pricing. Use the calculator with your own volume for an exact estimate.

Frequently asked questions

Is Gemini 3 Flash or GPT-4o mini cheaper?

GPT-4o mini is cheaper on both input ($0.15 vs $0.50 per million tokens) and output ($0.60 vs $3.00). At 150M input/40M output monthly, that's $46.50/month for GPT-4o mini versus $195/month for Gemini 3 Flash — roughly 4x the cost.

Why would I pay more for Gemini 3 Flash if GPT-4o mini is cheaper?

Context window is the main reason — Gemini 3 Flash offers a 1M-token window versus GPT-4o mini's 128K, nearly 8x larger. For tasks needing to hold long documents, extensive conversation history, or large codebases in a single call, that headroom can matter more than the per-token price gap.

Are these two models comparable in capability, or is this an unfair comparison?

Both sit in the budget tier from their respective providers, positioned for high-volume, well-defined tasks like classification, extraction, and routing rather than complex reasoning. They're a fair comparison on that basis, even though the price and context window differ meaningfully.

Which should I default to for a new project?

Default to GPT-4o mini unless you specifically know you need the larger context window — the 4x price difference is real money at any meaningful volume, and most budget-tier tasks (classification, short-form generation, simple extraction) don't need 1M tokens of context to complete.