Cost by workload size
Price comparison
| Feature |  GPT-4o |  Gemini 2.0 Flash |
|---|---|---|
| Provider | OpenAI | |
| Input / M tokens | $2.50 | $0.10 |
| Output / M tokens | $10 | $0.40 |
| Cached input read | $1.25 | — |
| Context window | 128K | 1M |
| Batch discount | −50% | — |
| Vision | Yes | Yes |
| Audio | Yes | Yes |
| Reasoning tokens | — | — |
What your workload actually costs
Prices per token hide the real picture. Here are three common workloads computed with AITokenCalculator's own engine (medium reasoning effort, no batch, no cache):
| Workload | GPT-4o | Gemini 2.0 Flash | Cheaper |
|---|---|---|---|
| 1,000 in / 300 out | $0.0055 | $0.00022 | Gemini 2.0 Flash |
| 10,000 in / 2,000 out | $0.045 | $0.0018 | Gemini 2.0 Flash |
| 100,000 in / 20,000 out | $0.45 | $0.018 | Gemini 2.0 Flash |
| Monthly @ 1,000 req/day (standard) | $1,369.69 | $54.79 | Gemini 2.0 Flash |
Analysis
On input price, Gemini 2.0 Flash wins at $0.10/M — roughly 25.0× cheaper than its rival. Output tells a sharper story: Gemini 2.0 Flash charges $0.40/M , about 25.0× less than GPT-4o — and output is where chatty and agentic workloads bleed money.
Context capacity differs too: Gemini 2.0 Flash fits 1M tokens (~750,000 words) versus 128K. If you feed whole documents or codebases, that gap decides feasibility before price even matters.
Speed-vs-depth trade. Gemini Flash is dramatically cheaper and faster for high-volume pipelines (classification, extraction, chat); GPT-4o holds an edge on nuanced reasoning and complex instruction following. Many teams run Flash for 90% of traffic and escalate hard cases.
Numbers shift as providers reprice — check the full AI model pricing table, then run your own prompt through the calculator preloaded with GPT-4o or with Gemini 2.0 Flash.