GPT-5 Nano vs Gemini 3.1 Pro
Compare pricing, context windows, and strengths for GPT-5 Nano by OpenAI and Gemini 3.1 Pro by Google - and see how to put either to work in Appaca.
GPT-5 Nano
The fastest and cheapest GPT-5 variant, ideal for summarization, classification, and lightweight tasks requiring high speed and low cost.
View GPT-5 NanoGemini 3.1 Pro
Google's most advanced reasoning Gemini model, built for complex multimodal problem-solving, software engineering, and long-horizon agentic workflows.
View Gemini 3.1 ProGPT-5 Nano vs Gemini 3.1 Pro at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | GPT-5 Nano | Gemini 3.1 Pro |
|---|---|---|
| Provider | OpenAI | |
| Model type | Text | Text |
| Context window | 400K tokens | 1.05M tokens |
| Input price | $0.05 / 1M tokens | $4 / 1M tokens |
| Output price | $0.4 / 1M tokens | $18 / 1M tokens |
| Status | Current | Current |
How GPT-5 Nano and Gemini 3.1 Pro differ
What the numbers mean in practice when choosing between GPT-5 Nano and Gemini 3.1 Pro.
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GPT-5 Nano is 99% cheaper on input tokens ($0.05 vs $4 per million), which adds up quickly in document-heavy workloads.
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GPT-5 Nano is 98% cheaper on output tokens ($0.4 vs $18 per million) - the bigger factor for tools that generate long documents.
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Gemini 3.1 Pro's 1.05M tokens context window is roughly 2.6x larger than GPT-5 Nano's 400K tokens, so it can work across bigger codebases, contracts, or archives in one pass.
Strengths side by side
Where each model shines, according to benchmarks and provider positioning.
GPT-5 Nano
1. Extremely fast performance
- Fastest model in the GPT-5 family.
- Great for real-time workflows, rapid responses, and high-throughput systems.
2. Most cost-efficient GPT-5 model
- Lowest input and output token costs.
- Suitable for large-scale or budget-sensitive applications.
3. Ideal for lightweight, well-scoped tasks
- Excels at summarization, classification, text extraction, and simple logic tasks.
- Best used when tasks are narrow and well-defined.
4. Multimodal input
- Accepts text + image as input.
- Outputs text only.
5. Broad tool support
- Supports Web Search, File Search, Image Generation (as a tool), Code Interpreter, and MCP.
- (Does not support Computer Use.)
Gemini 3.1 Pro
1. Google's most advanced reasoning Gemini model
- Designed to solve complex problems across multimodal inputs, including text, audio, images, video, PDFs, and full code repositories.
- Google highlights improved software engineering behavior, better agentic performance, and stronger usability in domains like finance and spreadsheets.
2. Large multimodal context with substantial output room
- Supports a 1,048,576 token input context window for large repositories, long documents, and multi-source workflows.
- Allows up to 65,536 output tokens for longer answers, plans, and code generations.
3. More efficient thinking with expanded controls
- Improves token efficiency and reasoning performance across use cases.
- Adds the
MEDIUMthinking_leveloption to better balance cost, speed, and quality.
4. Strong support for production agents
- Supports grounding with Google Search, code execution, function calling, structured outputs, context caching, RAG, and chat completions.
- Also offers a custom-tools endpoint tuned for agentic workflows that mix bash-like tools with custom code tools.
Use GPT-5 Nano or Gemini 3.1 Pro - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by GPT-5 Nano or Gemini 3.1 Pro - connected to your real data and ready for your whole team. No code, no deployment.
Describe it, and it's built
Tell the Appaca agent the internal tool you need and it builds a working app powered by GPT-5 Nano or Gemini 3.1 Pro. No code, no API keys, no deployment.
Switch models without rebuilding
Start on GPT-5 Nano, test the same tool on Gemini 3.1 Pro, and keep whichever performs better - the rest of your app stays exactly as it is.
Automated for the whole team
Schedule tools to run on autopilot - daily digests, weekly reports, real-time triggers - and share them with your whole team from one workspace.
Describe it, and it's built
Tell the Appaca agent what your team needs and it builds a working app powered by GPT-5 Nano or Gemini 3.1 Pro - connected to the tools you already use.
Related comparisons
See how GPT-5 Nano and Gemini 3.1 Pro stack up against other models in the directory.
FAQs
GPT-5 Nano is generally cheaper: $0.05 input / $0.4 output per million tokens, versus $4 / $18 for Gemini 3.1 Pro. Actual cost depends on how many tokens your workload reads and writes.
Gemini 3.1 Pro has the larger context window at 1.05M tokens, compared to 400K tokens for GPT-5 Nano. A larger window means the model can consider more text at once - useful for long contracts, codebases, or months of records.
It depends on the job. Compare the pricing, context window, and strengths above against your workload - and remember the choice isn't permanent. In Appaca you can build a tool on GPT-5 Nano, test the same tool on Gemini 3.1 Pro, and switch at any time without rebuilding anything.
Yes. Appaca is a no-code AI workspace: describe the internal tool your team needs and the Appaca agent builds it as a working app powered by GPT-5 Nano, Gemini 3.1 Pro, or any other model in the directory - with a built-in database, team access, and integrations. No API keys to wire up and nothing to deploy.
Build AI tools with GPT-5 Nano or Gemini 3.1 Pro
Describe the tool your team needs and get a working app powered by the model you choose - with a built-in database, team access, and integrations. No code, no deployment.