Is Gemini 1.5 Pro better than GPT-4o?

It depends on the workload. Gemini 1.5 Pro and GPT-4o differ in pricing, context window, and capability mix — see the side-by-side table above for the breakdown.

What's the price difference between Gemini 1.5 Pro and GPT-4o?

Gemini 1.5 Pro: Custom per 1M input tokens. GPT-4o: Custom per 1M input tokens. Output token pricing typically runs 2-5x higher.

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Gemini 1.5 Pro vs GPT-4o

Gemini 1.5 Pro for long-document and video understanding, GPT-4o for multimodal breadth and ecosystem.

Pricing source: live OpenRouter model catalog (`/api/v1/models`).

Model A

Gemini 1.5 Pro

Google • Multimodal

Context Window1M

Knowledge CutoffUnknown

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Model B

GPT-4o

OpenAI • Multimodal

Context Window128K

Knowledge CutoffUnknown

Open model page

Editorial Verdict

GPT-4o wins on coding and ecosystem; Gemini 1.5 Pro wins on context length and document processing.

GPT-4o and Gemini 1.5 Pro are both strong multimodal models, but Gemini 1.5 Pro's 1M token context gives it a massive advantage for document-heavy workflows. GPT-4o leads on coding and has a more mature API ecosystem. For multimodal document processing at scale, Gemini 1.5 Pro wins. For coding and general production use, GPT-4o wins.

Gemini 1.5 Pro

1M token context for processing entire books, document sets, or codebases
Strong multimodal understanding with native video frame analysis
Lower cost per token than GPT-4o with competitive benchmark performance

Best for: Long-document multimodal processing and massive context applications

GPT-4o

Superior coding performance across diverse programming languages
Mature API with reliable function calling and structured output
Multimodal consistency across text, images, audio, and video

Best for: Coding applications and general multimodal production use

Pricing sourced from OpenRouter — updates as their catalog changes.

Pricing Comparison

All values pull from OpenRouter and update as their catalog changes.

Metric	Gemini 1.5 Pro	GPT-4o	Winner
Input (per 1M tokens)	Custom	Custom	N/A
Output (per 1M tokens)	Custom	Custom	N/A
Request fee	N/A	N/A	N/A
Image fee	N/A	N/A	N/A

Cost Estimator

Estimate monthly billing using real OpenRouter prices.

Monthly RequestsAvg Tokens / Request

Gemini 1.5 Pro Total

Variable

GPT-4o Total

Variable

Savings unavailable (pricing not published for one model).

Capability Signals

Scores are directional estimates from model metadata — not official benchmark results.

Gemini 1.5 ProMMLU Signal (Reasoning)GPT-4o

74%

69%

Gemini 1.5 ProHumanEval Signal (Coding)GPT-4o

74%

69%

Gemini 1.5 ProAgentic Tooling SignalGPT-4o

74%

69%

Gemini 1.5 ProMultimodal SignalGPT-4o

74%

69%

Capabilities Matrix

Feature highlights for architecture and production fit.

Gemini 1.5 Pro Strengths

Large context window for long documents and codebases.
Balanced general-purpose profile for chat, extraction, and automation tasks.

GPT-4o Strengths

Balanced general-purpose profile for chat, extraction, and automation tasks.

API Implementation

Quick start snippets for each provider style.

Google (Python)

from google import genai

client = genai.Client(api_key="YOUR_API_KEY")
response = client.models.generate_content(
  model="google/gemini-1.5-pro",
  contents="Hello"
)

print(response.text)

OpenAI (Python)

from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
  model="openai/gpt-4o",
  messages=[{"role": "user", "content": "Hello"}]
)

print(response.choices[0].message.content)

Choose Gemini 1.5 Pro when...

You process large documents or code repositories in a single prompt.
You want a balanced default for mixed chat and workflow automation workloads.
You can measure quality with your own benchmark and prompt set.

Choose GPT-4o when...

You want a balanced default for mixed chat and workflow automation workloads.
You can measure quality with your own benchmark and prompt set.

Frequently Asked Questions

Practical checks before selecting a production model.

Which model is better for coding?

Coding preference depends on your stack and tool-calling needs. Compare the coding signal row, test with your repository tasks, and validate latency in your target region.

Which model is cheaper at scale?

Input and output token pricing can diverge by workload profile. Use the estimator with your monthly request count and token mix to get a realistic cost difference.

Are these official benchmark numbers?

Pricing is live from OpenRouter. Benchmark rows are ModelsAtlas metadata-based signals and should be treated as directional guidance, not official leaderboard scores.

Related Comparisons

Explore adjacent model matchups.

GPT-4o vs Claude 3.7 SonnetGPT-4o for multimodal breadth, Claude 3.7 for coding-heavy workflows.GPT-4o vs Claude 3 OpusClaude 3 Opus for highest reasoning quality, GPT-4o for production ecosystem and speed.GPT-4o vs Gemini 2.0 FlashGemini 2.0 Flash for cost efficiency and 1M context, GPT-4o for mature tool ecosystem.Claude 3.7 Sonnet vs Claude 3 OpusClaude 3 Opus for maximum capability on complex tasks, Claude 3.7 Sonnet for best price-to-performance in Claude family.