Gemini 3.1 Pro vs Qwen-Long
Compare pricing, context windows, and strengths for Gemini 3.1 Pro by Google and Qwen-Long 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 ProQwen-Long
Long-context model with 10M tokens for huge document analysis and summarization.
View Qwen-LongGemini 3.1 Pro vs Qwen-Long at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | Gemini 3.1 Pro | Qwen-Long |
|---|---|---|
| Provider | Alibaba Cloud | |
| Model type | Text | Text |
| Context window | 1.05M tokens | 10M tokens |
| Input price | $4 / 1M tokens | $0.072 / 1M tokens |
| Output price | $18 / 1M tokens | $0.287 / 1M tokens |
| Status | Current | Current |
How Gemini 3.1 Pro and Qwen-Long differ
What the numbers mean in practice when choosing between Gemini 3.1 Pro and Qwen-Long.
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Qwen-Long is 98% cheaper on input tokens ($0.072 vs $4 per million), which adds up quickly in document-heavy workloads.
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Qwen-Long is 98% cheaper on output tokens ($0.287 vs $18 per million) - the bigger factor for tools that generate long documents.
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Qwen-Long's 10M tokens context window is roughly 9.5x larger than Gemini 3.1 Pro's 1.05M 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.
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.
Qwen-Long
1. Extremely long context window
- Up to 10 million tokens.
2. Ideal for document-heavy workflows
- Legal, financial, RAG, compliance, research.
3. Low-cost for large-scale ingestion
- Optimized pricing for big inputs.
Use Gemini 3.1 Pro or Qwen-Long - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by Gemini 3.1 Pro or Qwen-Long - 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 Qwen-Long. No code, no API keys, no deployment.
Switch models without rebuilding
Start on Gemini 3.1 Pro, test the same tool on Qwen-Long, 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 Qwen-Long - connected to the tools you already use.
Related comparisons
See how Gemini 3.1 Pro and Qwen-Long stack up against other models in the directory.
FAQs
Qwen-Long is generally cheaper: $0.072 input / $0.287 output per million tokens, versus $4 / $18 for Gemini 3.1 Pro. Actual cost depends on how many tokens your workload reads and writes.
Qwen-Long has the larger context window at 10M tokens, compared to 1.05M tokens for Gemini 3.1 Pro. 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 Qwen-Long, 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, Qwen-Long, 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 Qwen-Long
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.