Gemini 3.1 Pro vs QVQ-Max
Compare pricing, context windows, and strengths for Gemini 3.1 Pro by Google and QVQ-Max by Alibaba Cloud - and see how to put either to work in Appaca.
Gemini 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 ProQVQ-Max
High-end visual reasoning model with strong math, coding, and diagram understanding.
View QVQ-MaxGemini 3.1 Pro vs QVQ-Max at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | Gemini 3.1 Pro | QVQ-Max |
|---|---|---|
| Provider | Alibaba Cloud | |
| Model type | Text | Vision |
| Context window | 1.05M tokens | 131.1K tokens |
| Input price | $4 / 1M tokens | $1.147 / 1M tokens |
| Output price | $18 / 1M tokens | $4.588 / 1M tokens |
| Status | Current | Current |
How Gemini 3.1 Pro and QVQ-Max differ
What the numbers mean in practice when choosing between Gemini 3.1 Pro and QVQ-Max.
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QVQ-Max is 71% cheaper on input tokens ($1.147 vs $4 per million), which adds up quickly in document-heavy workloads.
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QVQ-Max is 75% cheaper on output tokens ($4.588 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 8x larger than QVQ-Max's 131.1K tokens, so it can work across bigger codebases, contracts, or archives in one pass.
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These are different kinds of model: Gemini 3.1 Pro is a text model while QVQ-Max is a vision model, so they often complement each other in a workflow rather than compete.
Strengths side by side
Where each model shines, according to benchmarks and provider positioning.
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.
QVQ-Max
1. Strongest visual reasoning in Qwen lineup
- Handles charts, diagrams, puzzles.
2. Great for math + vision hybrids
- Geometry, visual logic testing.
3. High-quality instruction following
- Consistent formatting and detailed responses.
Use Gemini 3.1 Pro or QVQ-Max - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by Gemini 3.1 Pro or QVQ-Max - 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 Gemini 3.1 Pro or QVQ-Max. No code, no API keys, no deployment.
Switch models without rebuilding
Start on Gemini 3.1 Pro, test the same tool on QVQ-Max, 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 Gemini 3.1 Pro or QVQ-Max - connected to the tools you already use.
Related comparisons
See how Gemini 3.1 Pro and QVQ-Max stack up against other models in the directory.
FAQs
QVQ-Max is generally cheaper: $1.147 input / $4.588 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 131.1K tokens for QVQ-Max. 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 Gemini 3.1 Pro, test the same tool on QVQ-Max, 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 Gemini 3.1 Pro, QVQ-Max, 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 Gemini 3.1 Pro or QVQ-Max
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.