Qwen-Turbo vs Qwen-Long
Compare pricing, context windows, and strengths for Qwen-Turbo by Alibaba Cloud and Qwen-Long by Alibaba Cloud - and see how to put either to work in Appaca.
Qwen-Turbo
Fast, low-cost model for general tasks; being phased out in favor of Flash.
View Qwen-TurboQwen-Long
Long-context model with 10M tokens for huge document analysis and summarization.
View Qwen-LongQwen-Turbo vs Qwen-Long at a glance
Specs and pricing side by side, from the Appaca AI models directory.
| Spec | Qwen-Turbo | Qwen-Long |
|---|---|---|
| Provider | Alibaba Cloud | Alibaba Cloud |
| Model type | Text | Text |
| Context window | 1M tokens | 10M tokens |
| Input price | $0.044 / 1M tokens | $0.072 / 1M tokens |
| Output price | $0.431 / 1M tokens | $0.287 / 1M tokens |
| Status | Current | Current |
How Qwen-Turbo and Qwen-Long differ
What the numbers mean in practice when choosing between Qwen-Turbo and Qwen-Long.
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Qwen-Turbo is 39% cheaper on input tokens ($0.044 vs $0.072 per million), which adds up quickly in document-heavy workloads.
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Qwen-Long is 33% cheaper on output tokens ($0.287 vs $0.431 per million) - the bigger factor for tools that generate long documents.
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Qwen-Long's 10M tokens context window is roughly 10x larger than Qwen-Turbo's 1M 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.
Qwen-Turbo
1. Fast and affordable
- Good for standard LLM workloads.
2. Supports thinking mode
- Allows moderate reasoning.
3. Being replaced by Qwen-Flash
- Flash has better pricing and performance.
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 Qwen-Turbo or Qwen-Long - or both
Appaca is the AI workspace for operators. Build internal tools and AI co-workers powered by Qwen-Turbo 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 Qwen-Turbo or Qwen-Long. No code, no API keys, no deployment.
Switch models without rebuilding
Start on Qwen-Turbo, 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 Qwen-Turbo or Qwen-Long - connected to the tools you already use.
Related comparisons
See how Qwen-Turbo 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 $0.044 / $0.431 for Qwen-Turbo. Actual cost depends on how many tokens your workload reads and writes.
Qwen-Long has the larger context window at 10M tokens, compared to 1M tokens for Qwen-Turbo. 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 Qwen-Turbo, 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 Qwen-Turbo, 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 Qwen-Turbo 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.