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Gemini Deep Research vs GPT-5.2

Gemini Deep Research (2024) and GPT-5.2 (2025) are frontier reasoning models from Google DeepMind and OpenAI. Gemini Deep Research ships a 128K-token context window, while GPT-5.2 ships a 256K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.

GPT-5.2 is safer overall; choose Gemini Deep Research when provider fit matters.

Specs

Released2024-12-112025-12-11
Context window128K256K
Parameters
Architecturedecoder onlydecoder only
LicenseProprietaryProprietary
Knowledge cutoff--

Pricing and availability

Gemini Deep ResearchGPT-5.2
Input price-$1.75/1M tokens
Output price-$14/1M tokens
Providers

Capabilities

Gemini Deep ResearchGPT-5.2
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: GPT-5.2, multimodal input: GPT-5.2, reasoning mode: GPT-5.2, and code execution: GPT-5.2. Both models share function calling, tool use, and 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.

Pricing coverage is uneven: Gemini Deep Research has no token price sourced yet and GPT-5.2 has $1.75/1M input tokens. Provider availability is 1 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose Gemini Deep Research when provider fit are central to the workload. Choose GPT-5.2 when coding workflow support, larger context windows, and broader provider choice 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. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Which has a larger context window, Gemini Deep Research or GPT-5.2?

GPT-5.2 supports 256K tokens, while Gemini Deep Research supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.

Is Gemini Deep Research or GPT-5.2 open source?

Gemini Deep Research is listed under Proprietary. GPT-5.2 is listed under Proprietary. 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 Deep Research or GPT-5.2?

GPT-5.2 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. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Which is better for multimodal input, Gemini Deep Research or GPT-5.2?

GPT-5.2 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.

Which is better for reasoning mode, Gemini Deep Research or GPT-5.2?

GPT-5.2 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run Gemini Deep Research and GPT-5.2?

Gemini Deep Research is available on Google AI Studio. GPT-5.2 is available on Replicate API and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.

Continue comparing

Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.