GPT-4o vs Gemini 1.5 Pro — API Cost Calculator

Predict your real monthly bill. Toggle batch API and prompt caching to see how discounts and cache hits change the math for your exact workload. Pricing verified against official provider pages — May 2026.

Cost Calculator

⬛ GPT-4o OpenAI
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per month
🔵 Gemini 1.5 Pro Google
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per month

Pricing snapshot (as of May 2026)

The table below shows per-1M-token rates sourced from the official OpenAI and Google pricing pages, last verified on 21 May 2026. All figures are in USD.

Rate type GPT-4o Gemini 1.5 Pro
Input (standard) $2.50 $1.25
Output (standard) $10.00 $5.00
Input (batch) $1.2500 —
Output (batch) $5.0000 —
Cache read $1.2500 —
Context window 128K 2097K

Sources: https://platform.openai.com/docs/pricing · https://ai.google.dev/gemini-api/docs/pricing

When GPT-4o is the better pick

GPT-4o is the stronger choice when your application relies on the full OpenAI ecosystem: structured JSON schema outputs, native vision and audio input, fine-tuning support, and the Assistants API all tie into the GPT-4o family. If you deploy on Azure OpenAI Service for EU or US data-residency compliance, or depend on broad third-party SDK coverage, the OpenAI infrastructure around GPT-4o is hard to match. Its automatic prompt caching and straightforward pricing with no separate write fee also reduce operational complexity for teams that prefer a simpler billing model.

When Gemini 1.5 Pro is the better pick

Gemini 1.5 Pro is Google's flagship long-context model with a 2M-token context window — the largest of any model covered on this site — and native multimodal capabilities spanning text, images, audio, and video. It is the right pick when you need to analyse hours of video, process entire codebases, or reason across multiple lengthy documents simultaneously without pagination. At $1.25/$5.00 per million tokens it sits in the mid-price tier, offering substantially more context per dollar than GPT-4o or Claude 3.5 Sonnet for input-heavy workloads where the quality of long-context retrieval is paramount.

Real-world example: 1M requests/month at 2K input + 500 output tokens

Assume a production workload of 1 million API calls per month, each consuming 2,000 input tokens and generating 500 output tokens. This is a realistic profile for a mid-size SaaS product with active users across time zones — a customer-support bot, a document-analysis pipeline, or an AI-assisted search feature.

Scenario A — Standard pricing, no optimisations:

At this volume and token mix, Gemini 1.5 Pro is 50% cheaper than the alternative on standard rates — a difference of $5,000.00/month. Over a full year that compounds to $60,000.00 in savings, which is meaningful even before factoring in batch or caching optimisations.

Scenario B — Batch API enabled (50% off, where supported):

  • GPT-4o batch: $5,000.00/month (saving $5,000.00 vs. standard)
  • Gemini 1.5 Pro: no batch API — standard rate applies ($5,000.00/month)

The batch API is well-suited for nightly analytics pipelines, content moderation queues, data-labelling jobs, and any workload that can tolerate asynchronous processing with up to 24-hour turnaround. It is incompatible with real-time interactive use cases such as customer-facing chat or streaming completions.

Use the interactive calculator above to model your specific token mix, request volume, and caching strategy. Real production costs typically run 10–30% above median estimates due to prompt variability, retry logic, and usage spikes.

Migration considerations

Switching between GPT-4o and Gemini 1.5 Pro is not always a drop-in model swap. Differences in API shape, prompt conventions, tokeniser behaviour, and context-window limits can require non-trivial engineering work. Here is what to audit before migrating production traffic.

Frequently asked questions

Which is cheaper at 1M requests/month — GPT-4o or Gemini 1.5 Pro? +

At 1M requests/month with 2,000 input tokens and 500 output tokens per request, Gemini 1.5 Pro costs $5,000.00 versus GPT-4o at $10,000.00 — a difference of $5,000.00 per month (50%). Enabling the batch API (where available) cuts those figures by 50% for workloads that tolerate up to 24-hour turnaround.

Does GPT-4o or Gemini 1.5 Pro support batch API pricing? +

GPT-4o supports batch API pricing at 50% off (input: $1.25/1M, output: $5.00/1M) in exchange for up to 24-hour latency. Gemini 1.5 Pro does not currently offer an equivalent batch discount, so all Gemini 1.5 Pro requests are billed at standard rates regardless of scheduling.

How does prompt caching compare between GPT-4o and Gemini 1.5 Pro? +

GPT-4o supports prompt caching with cache reads at $1.25/1M (no separate write fee — caching is applied automatically). Gemini 1.5 Pro does not currently offer explicit prompt caching. Prompt caching delivers the largest savings when you have a large, stable system prompt reused across thousands of requests per day — a 50,000-token knowledge-base system prompt reused 10,000 times can cut input costs by 80–90%.

Which model has lower latency — GPT-4o or Gemini 1.5 Pro? +

Latency depends on region, time of day, request size, and infrastructure routing — not just model architecture. In general, smaller models (the lower-priced model in this pair) tend to return the first token faster because they require fewer compute cycles per forward pass. For latency-critical production workloads, benchmark with your own representative prompt and output length distribution using p50/p95/p99 metrics rather than synthetic averages. Provider infrastructure also varies: OpenAI has more global edge regions via Azure, while Google Vertex AI and Anthropic offer fewer but growing geographic options.

Can I trust this calculator for production budgeting? +

This calculator uses pricing verified against the official provider pricing pages as of May 2026. It is suitable for planning and estimating monthly spend. For production budgets, always cross-check against your provider dashboard, account for any committed-use discounts or enterprise pricing you have negotiated, and add a 10–20% buffer for unexpected usage spikes. Token counts in the calculator are per-request estimates — actual production variance (longer user queries, retry logic, error recovery) can push real costs 15–30% above median estimates.