GPT-5.5 vs Gemini 3.1 Pro
Compare pricing, context windows, and strengths for GPT-5.5 by OpenAI and Gemini 3.1 Pro by Google - and see how to put either to work in Appaca.
GPT-5.5
OpenAI's smartest and most capable model yet for agentic coding, knowledge work, and computer use, delivering a new class of intelligence at GPT-5.4 latency.
View GPT-5.5Gemini 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-5.5 vs Gemini 3.1 Pro at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | GPT-5.5 | Gemini 3.1 Pro |
|---|---|---|
| Provider | OpenAI | |
| Model type | Text | Text |
| Context window | 1M tokens | 1.05M tokens |
| Input price | $5 / 1M tokens | $4 / 1M tokens |
| Output price | $30 / 1M tokens | $18 / 1M tokens |
| Status | Current | Current |
How GPT-5.5 and Gemini 3.1 Pro differ
What the numbers mean in practice when choosing between GPT-5.5 and Gemini 3.1 Pro.
Our take
GPT-5.5 is the stronger agentic worker - it tops coding and computer-use benchmarks and uses fewer tokens per task. Gemini 3.1 Pro counters with a slightly larger context window and ~20% lower input pricing, making it attractive for context-heavy retrieval and analysis workloads. Teams building code-adjacent tools tend to pick GPT-5.5; teams feeding huge document sets often start with Gemini.
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Gemini 3.1 Pro input tokens cost $4 per million versus $5 for GPT-5.5, which adds up in high-volume document pipelines.
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GPT-5.5 delivers higher intelligence at GPT-5.4 latency, so interactive tools feel just as fast despite the bigger model.
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Both support ~1M-token contexts, so the practical difference comes down to task type rather than capacity.
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Gemini 3.1 Pro is 20% cheaper on input tokens ($4 vs $5 per million), which adds up quickly in document-heavy workloads.
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Gemini 3.1 Pro is 40% cheaper on output tokens ($18 vs $30 per million) - the bigger factor for tools that generate long documents.
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Context windows are close: GPT-5.5 handles 1M tokens and Gemini 3.1 Pro handles 1.05M tokens.
Strengths side by side
Where each model shines, according to benchmarks and provider positioning.
GPT-5.5
1. Strongest Agentic Coding Model
- State-of-the-art on Terminal-Bench 2.0 (82.7%), Expert-SWE (73.1%), and SWE-Bench Pro (58.6%), outperforming GPT-5.4 on complex coding tasks.
- Holds context across large systems, reasons through ambiguous failures, and carries changes through surrounding codebases with fewer tokens.
2. Higher Intelligence at GPT-5.4 Latency
- Co-designed, trained, and served on NVIDIA GB200/GB300 NVL72 systems to match GPT-5.4 per-token latency while performing at a significantly higher level.
- Uses fewer tokens to complete the same tasks, making it more efficient as well as more capable.
3. Powerful for Knowledge Work & Computer Use
- Scores 84.9% on GDPval (44 occupations) and 78.7% on OSWorld-Verified for autonomous computer operation.
- Excels at generating documents, spreadsheets, and reports; naturally moves across finding information, using tools, and checking output.
4. Scientific Research Co-Scientist
- Leading performance on GeneBench, BixBench, and FrontierMath; helped discover a new proof about Ramsey numbers verified in Lean.
- Strong enough to meaningfully accelerate progress at the frontiers of biomedical and mathematical research.
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-5.5 or Gemini 3.1 Pro - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by GPT-5.5 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-5.5 or Gemini 3.1 Pro. No code, no API keys, no deployment.
Switch models without rebuilding
Start on GPT-5.5, 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-5.5 or Gemini 3.1 Pro - connected to the tools you already use.
Related comparisons
See how GPT-5.5 and Gemini 3.1 Pro stack up against other models in the directory.
FAQs
Gemini 3.1 Pro is generally cheaper: $4 input / $18 output per million tokens, versus $5 / $30 for GPT-5.5. 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 1M tokens for GPT-5.5. A larger window means the model can consider more text at once - useful for long contracts, codebases, or months of records.
GPT-5.5 is the stronger agentic worker - it tops coding and computer-use benchmarks and uses fewer tokens per task. Gemini 3.1 Pro counters with a slightly larger context window and ~20% lower input pricing, making it attractive for context-heavy retrieval and analysis workloads. Teams building code-adjacent tools tend to pick GPT-5.5; teams feeding huge document sets often start with Gemini.
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-5.5, 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-5.5 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.