Vertex AI (Gemini) vs OpenAI API: Pricing, Pros, and Cons Compared

Choosing between Google’s Gemini models (via Vertex AI) and OpenAI’s GPT family is one of the most common decisions developers face when building AI-powered applications. Both platforms offer capable models, but they differ significantly in pricing structure, context windows, and ecosystem benefits.

This post breaks down the current pricing, highlights the pros and cons of each platform, and offers guidance on when to choose one over the other.

Pricing Comparison (January 2026)

Pricing is per 1 million tokens. All models listed are production-ready unless noted.

Google Gemini (Vertex AI)

Model	Input	Output	Context Window
Gemini 2.5 Pro	$1.25	$10.00	1M tokens
Gemini 2.5 Flash	$0.075	$0.60	1M tokens
Gemini 2.5 Flash-Lite	$0.10	$0.40	1M tokens
Gemini 3 Pro (preview)	$2.00	$12.00	1M tokens

OpenAI

Model	Input	Output	Context Window
GPT-4o	$2.50	$10.00	128K tokens
GPT-4o mini	$0.15	$0.60	128K tokens
GPT-4.1	$2.00	$8.00	1M tokens

At the flagship level, Gemini 2.5 Pro undercuts GPT-4o on input costs ($1.25 vs $2.50) while matching on output ($10). The budget tiers are comparable—GPT-4o mini and Gemini Flash both land around $0.15/$0.60.

The biggest pricing difference is context length. Most Gemini models support 1 million tokens natively, while GPT-4o caps at 128K. GPT-4.1 matches the 1M context but is a newer, less battle-tested model.

Context Length Pricing Gotcha

One aspect of Gemini pricing catches developers off guard: context-tiered pricing. For most Gemini models, prices remain standard up to 200,000 tokens. Beyond that threshold, prices typically double.

For example, Gemini 2.5 Pro charges $1.25 per million input tokens under 200K context, but jumps to $2.50 for longer contexts.

OpenAI uses flat-rate pricing regardless of context length, which makes cost estimation simpler.

Vertex AI (Gemini) Pros and Cons

Pros

Larger context windows. 1 million tokens is standard across most Gemini models. This matters for applications processing large documents, codebases, or conversation histories.

Generous free tier. Google offers 1,000+ daily requests and 250K tokens per minute for development and testing. OpenAI has no free tier.

Cost optimization features. Batch processing cuts costs by 50% for async workloads. Context caching can reduce costs by up to 75% for applications with repeated prompts.

GCP integration. Native IAM controls, regional deployment options, and consolidated billing if you’re already in the Google Cloud ecosystem.

Lower input costs. Gemini 2.5 Pro’s input pricing is half that of GPT-4o.

Strong multimodal support. Image and video understanding are built in across the model family.

Cons

Context-tiered pricing. Costs double when exceeding 200K tokens, complicating budgeting for long-context applications.

Vertex overhead. Additional charges may apply for tuning, pipelines, and evaluation tools.

Preview pricing volatility. New models often see 20-50% price changes when moving from preview to stable.

Ecosystem lock-in. You get the best value when committed to GCP.

OpenAI API Pros and Cons

Pros

Mature ecosystem. Extensive documentation, well-maintained client libraries, and a large developer community.

Consistent pricing. Flat rates regardless of context length make cost estimation straightforward.

Fine-tuning options. Well-established workflows for custom model training.

Strong function calling. Industry-leading structured output and tool use capabilities.

Web search integration. Built-in browsing for models that support it.

Cons

Higher input costs. GPT-4o input pricing is 2x Gemini 2.5 Pro.

Smaller context windows. 128K tokens for most models vs Gemini’s 1M default.

No free tier. Pay-as-you-go from the first request.

Hidden reasoning costs. For reasoning models, internal “thinking” tokens are billed as output even though they’re not visible in the response.

Cost Optimization Features

Feature	Gemini	OpenAI
Batch discounts	50% off	Limited
Context caching	Up to 75% off	Prompt caching available
Free tier	Yes (generous)	No
Enterprise plans	$30/user/month	Custom pricing

Gemini’s batch mode is particularly valuable for non-real-time workloads. Gemini 2.5 Pro drops to $0.625/$5.00 per million tokens when processed asynchronously.

When to Choose Each

Choose Gemini / Vertex AI if:

• You need long context (>128K tokens)
• You’re already invested in GCP
• Cost optimization is a priority (batch processing, caching)
• You want a free tier for development and prototyping
• Multimodal (image/video) processing is core to your use case

Choose OpenAI if:

• You need a mature, well-documented ecosystem
• Predictable, flat-rate pricing is important
• You rely heavily on function calling and structured outputs
• You want built-in web search capabilities
• Your team is already familiar with the OpenAI SDK

Key Takeaways

• Input costs favor Gemini. Gemini 2.5 Pro is 50% cheaper on input than GPT-4o.
• Output costs are similar. Both platforms charge around $10 per million output tokens at the flagship level.
• Context windows favor Gemini. 1M tokens vs 128K for most OpenAI models.
• Free tier only exists on Gemini. Significant for prototyping and development.
• Watch for context-tiered pricing. Gemini costs double beyond 200K tokens.
• Ecosystem matters. GCP users benefit from Vertex AI integration; teams with OpenAI experience may prefer staying in that ecosystem.

The “right” choice depends on your specific requirements. For long-context applications with cost sensitivity, Gemini has clear advantages. For teams that value ecosystem maturity and predictable pricing, OpenAI remains a strong option.

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Pricing data current as of January 2026. Both platforms update pricing regularly—check official documentation before making production decisions.