GPT-4 Turbo vs Qwen-Max

Compare GPT-4 Turbo and Qwen-Max. Find out which one is better for your use case.

Model Comparison

FeatureGPT-4 TurboQwen-Max
ProviderOpenAIAlibaba Cloud
Model Typetexttext
Context Window128,000 tokens32,768 tokens
Input Cost$10.00 / 1M tokens$1.60 / 1M tokens
Output Cost$30.00 / 1M tokens$6.40 / 1M tokens

Strengths & Best Use Cases

GPT-4 Turbo

1. Strong reasoning for its generation

  • Next-gen version of GPT-4 designed to be cheaper and faster than the original.
  • Good for analytical tasks, structured writing, coding guidance, and multi-step reasoning.

2. Image input support

  • Accepts images and provides text-only outputs.
  • Useful for OCR, visual Q&A, document extraction, UI analysis, and design interpretation.

3. Stable performance

  • Predictable model behavior suitable for legacy systems still built on GPT-4.
  • Works reliably for established pipelines and enterprise workloads.

4. Large 128K context window

  • Handles long documents, multi-file inputs, or extended conversational sessions.
  • Allows complex prompt chaining and large instruction sets.

5. Broad endpoint compatibility

  • Works with Chat Completions, Responses API, Realtime API, Assistants, Batch, Fine-tuning, Embeddings, and more.
  • Supports streaming and function calling.

6. Good choice for cost-controlled GPT-4-class workloads

  • Although older, still useful for teams who want GPT-4-level reasoning without upgrading immediately.
  • A midpoint between legacy GPT-4 and modern GPT-4o/5.1 models.

7. Text-only output simplifies downstream use

  • Ensures deterministic outputs for applications that need reliable text generation.
  • Good for RAG, data pipelines, automation tools, and enterprise systems.

8. Recommended migration path

  • OpenAI now recommends using GPT-4o or GPT-5.1 for improved speed, cost, reasoning, and multimodal capability.
  • GPT-4 Turbo remains available for backward compatibility and stability.

Qwen-Max

1. Strong general-purpose reasoning

  • Great for coding, analysis, creation, and multi-step tasks.

2. Stable commercial-grade model

  • Predictable output quality and long-term stability.

3. Supports batch operations

  • Batch inference is 50% cheaper.

4. Good for production agents

  • Reliable instruction following and structured output.

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