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Gemini 2.5 Pro vs GPT-4o Search Preview

Gemini 2.5 Pro (2025) and GPT-4o Search Preview (2025) are compact production models from Google DeepMind and OpenAI. Gemini 2.5 Pro ships a 1M-token context window, while GPT-4o Search Preview ships a 128K-token context window. On pricing, Gemini 2.5 Pro costs $1.25/1M input tokens versus $2.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.

Gemini 2.5 Pro is ~100% cheaper at $1.25/1M; pay for GPT-4o Search Preview only for provider fit.

Specs

Specification
Released2025-06-172025-02-26
Context window1M128K
Parameters
Architecturedecoder onlydecoder only
LicenseProprietaryUnknown
Knowledge cutoff2025-01-

Pricing and availability

Pricing attributeGemini 2.5 ProGPT-4o Search Preview
Input price$1.25/1M tokens$2.5/1M tokens
Output price$10/1M tokens$10/1M tokens
Providers

Capabilities

CapabilityGemini 2.5 ProGPT-4o Search Preview
VisionYesNo
MultimodalYesNo
ReasoningNoNo
Function callingYesNo
Tool useYesNo
Structured outputsYesYes
Code executionYesNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: Gemini 2.5 Pro, multimodal input: Gemini 2.5 Pro, function calling: Gemini 2.5 Pro, tool use: Gemini 2.5 Pro, and code execution: Gemini 2.5 Pro. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, Gemini 2.5 Pro lists $1.25/1M input and $10/1M output tokens, while GPT-4o Search Preview lists $2.5/1M input and $10/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Gemini 2.5 Pro lower by about $0.88 per million blended tokens. Availability is 3 providers versus 1, so concentration risk also matters.

Choose Gemini 2.5 Pro when coding workflow support, larger context windows, and lower input-token cost are central to the workload. Choose GPT-4o Search Preview when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions.

FAQ

Which has a larger context window, Gemini 2.5 Pro or GPT-4o Search Preview?

Gemini 2.5 Pro supports 1M tokens, while GPT-4o Search Preview supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Which is cheaper, Gemini 2.5 Pro or GPT-4o Search Preview?

Gemini 2.5 Pro is cheaper on tracked token pricing. Gemini 2.5 Pro costs $1.25/1M input and $10/1M output tokens. GPT-4o Search Preview costs $2.5/1M input and $10/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is Gemini 2.5 Pro or GPT-4o Search Preview open source?

Gemini 2.5 Pro is listed under Proprietary. GPT-4o Search Preview is listed under Unknown. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.

Which is better for vision, Gemini 2.5 Pro or GPT-4o Search Preview?

Gemini 2.5 Pro has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for multimodal input, Gemini 2.5 Pro or GPT-4o Search Preview?

Gemini 2.5 Pro has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Gemini 2.5 Pro and GPT-4o Search Preview?

Gemini 2.5 Pro is available on Google AI Studio, GCP Vertex AI, and OpenRouter. GPT-4o Search Preview is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.