Comparison

OpenAI vs Google Gemini API pricing comparison

Gemini Flash and GPT mini tiers often win on high-volume workloads while premium models diverge on output pricing. Normalize both vendors on one baseline before choosing.

Related: providers/openai api pricing · providers/google gemini api pricing

Calculator

Step 1

Describe your workload

Start with a preset or dial in your own numbers.

Synced Jun 29, 2026

1K messages / mo

1K tokens avg

Step 2

Estimated monthly spend

$67.50

Rank #14 of 63 · save $67.44/mo vs #1

Input $22.50 Cached 0.34M tok Output $45.00
Total tokens1.00M
Messages1K
Avg tok/msg1K

High-quality reasoning agents and premium customer support flows.

Step 3

Compare all models

14 models priced for your workload

Best value

02
Gemini 2.0 Flash-Lite (batch)Google Gemini
Estimated$0.07
04
Gemini 2.5 Flash-Lite (batch)Google Gemini
Verified$0.07
05
Gemini 2.0 Flash (batch)Google Gemini
Estimated$0.08
09
Gemini 2.0 Flash (standard)Google Gemini
Estimated$0.11
11
Gemini 2.0 Flash-Lite (standard)Google Gemini
Verified$0.13
12
Gemini 2.5 Flash-Lite (standard)Google Gemini
Verified$0.14
20
GPT-5.4 nanoOpenAI
Verified$0.40
26
Gemini 2.5 Flash (standard)Google Gemini
Verified$0.76
33
GPT-5.4 miniOpenAI
Verified$1.46
34
Gemini 2.5 Pro (batch)Google Gemini
Verified$1.55
43
Gemini 2.5 Pro (standard)Google Gemini
Verified$3.06
50
GPT-5.4OpenAI
Verified$4.87
60
GPT-5.5OpenAI
Verified$11.25
63
GPT-5.5 ProOpenAI
Verified$67.50

How costs are calculated

Prices from ai-provider-pricing-validated.json, validated Jun 2026. Confirm on official provider pages before billing decisions.

Embed

Want this calculator on your site?

Copy the iframe snippet below and paste it into any page, doc, or WordPress Custom HTML block.

<iframe src="https://modelcostcomparison.com/embed/ai-api-pricing-calculator?ref=topic-openai-vs-gemini" width="100%" height="980" style="border:0;border-radius:12px;overflow:hidden" loading="lazy" referrerpolicy="strict-origin-when-cross-origin" title="AI API Pricing Calculator by Model Cost Comparison"></iframe>

Model Cost Comparison · Built by Lazige · Methodology

How we calculate cost

Monthly estimate = (input tokens × input $/MTok) + (cached tokens × cached $/MTok when published) + (output tokens × output $/MTok), scaled to your message volume. See the methodology for validation sources and update cadence.

Use cases

Common workload patterns teams model here

Illustrative scenarios — not customer testimonials. Each card shows how a typical team shape (support bot, RAG, code assistant, or agent) maps to the calculator presets.

“A support-bot preset with 50k messages/month surfaced three budget models in one pass — faster than copying rates from five pricing pages.”
B2B SaaS support botHigh volume · short replies · 55% cache
“Raising the cached-input slider made our RAG estimate realistic. We moved retrieval-heavy traffic to a cheaper model without changing reply quality.”
Document Q&A / RAGRetrieval-heavy · 65% cache
“PMs use the embed on internal docs to sanity-check model spend before vendor requests — everyone shares the same workload baseline.”
Platform / internal toolingMixed presets · stakeholder decks
“Before scaling an agent workflow, comparing monthly cost across every provider for the same token mix avoided over-provisioning on day one.”
Tool-calling agentAgent preset · multi-step I/O
“Finance teams grasp token mix faster with one screenshot from the ranking table — useful when justifying a move off a default premium model.”
Cost review / FinOpsBoard prep · usage doubles scenario
“Quarterly Bedrock vs Vertex vs direct API reviews start here — normalize the math before opening vendor spreadsheets.”
Cloud architecture reviewMulti-cloud comparison
“The code-assistant preset was a realistic starting point for a copilot MVP; we adjusted tokens after a pilot week and stayed within 10% of the estimate.”
IDE / code copilotCode preset · long context
“Gemini Flash placed top three for our exact cache ratio on a high-volume FAQ bot — easy to miss in a static pricing table.”
High-volume FAQSupport preset · high cache