Grok 4 vs Qwen3-Flash

Compare Grok 4 and Qwen3-Flash. Find out which one is better for your use case.

Model Comparison

FeatureGrok 4Qwen3-Flash
ProviderxAIAlibaba Cloud
Model Typetexttext
Context Window256,000 tokens1,000,000 tokens
Input Cost$3.00 / 1M tokens$0.02 / 1M tokens
Output Cost$15.00 / 1M tokens$0.22 / 1M tokens

Strengths & Best Use Cases

Grok 4

1. Flagship-level reasoning and math performance

  • Designed for world-class reasoning depth, precision, and multi-step logical chains.
  • Excels at STEM, mathematics, symbolic operations, proofs, and analytical workloads.

2. Powerful multimodal understanding

  • Supports text, images, and other modalities.
  • Handles cross-modal reasoning tasks requiring context synthesis.

3. Extreme capability across diverse tasks

  • Positioned as a top-tier 'jack of all trades' model.
  • Strong in natural language, coding, knowledge retrieval, and structured generation.

4. Large 256K context window

  • Enables analysis of long documents, entire codebases, multi-document packs, and extensive agent sessions.
  • Supports workloads that require persistent reasoning across large inputs.

5. Advanced developer tooling support

  • Function calling for tool-augmented workflows.
  • Structured outputs for predictable, schema-controlled generation.
  • Integrates smoothly with agents and complex automation pipelines.

6. Efficient caching for cost reduction

  • Cached input tokens discounted to $0.75 / 1M tokens.
  • Encourages RAG, retrieval pipelines, and multi-step conversational workflows.

7. Production-ready performance

  • Stable rate limits: 480 requests per minute.
  • High token throughput: 2,000,000 tokens per minute.
  • Available across multiple xAI regional clusters.

8. Optional Live Search augmentation

  • Add-on: $25 per 1K sources.
  • Enhances factual accuracy and real-time information retrieval.

Qwen3-Flash

1. Enhanced Flash-generation performance

  • Better factual accuracy and reasoning.

2. Very inexpensive

  • Perfect for high-volume automation and micro-agents.

3. Hybrid thinking mode

  • Not typical for small models.

4. Large context capacity

  • Up to 1M tokens.

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