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qwen.qwen3-coder-480b-a35b-v1:0 Cost Calculator - Bedrock Converse

Calculate the cost of using qwen.qwen3-coder-480b-a35b-v1:0 from Bedrock Converse for your AI applications

qwen.qwen3-coder-480b-a35b-v1:0 Cost Calculator

Mode: Chat

Max: 262,000 tokens

Max: 65,536 tokens

Cost Breakdown

Input Cost$0.00022000
Output Cost$0.001800
Total Cost$0.002020

Pricing Details

Input: $0.0000002200 per token
Output: $0.00000180 per token
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Model Specifications

Capabilities

✓Function Calling
✓Reasoning

Limits

Max Input Tokens262,000
Max Output Tokens65,536
Max Tokens65,536

About qwen.qwen3-coder-480b-a35b-v1:0

qwen.qwen3-coder-480b-a35b-v1:0 is a powerful chat AI model offered by Bedrock Converse. This comprehensive guide provides detailed pricing information, technical specifications, and capabilities to help you understand the costs and features of using qwen.qwen3-coder-480b-a35b-v1:0 in your development applications.

Pricing Information

Input Cost$0.22 per 1M tokens
Output Cost$1.80 per 1M tokens

Note: Use the interactive calculator above to estimate costs for your specific usage patterns.

Technical Specifications

Maximum Input Tokens262,000
Maximum Output Tokens65,536
Maximum Total Tokens65,536

Pro Tip

Use the maximum token limits shown above to understand the model's capacity. This model can handle up to 262,000 input tokens. The maximum output length is 65,536 tokens.

Model Capabilities

✓Function Calling - Execute custom functions and tools
✓Advanced Reasoning - Complex problem-solving capabilities
When should you use qwen.qwen3-coder-480b-a35b-v1:0?

qwen.qwen3-coder-480b-a35b-v1:0 is best suited for the following scenarios:

  • Code generation and completion
  • Bug fixing and refactoring
  • Developer tooling and IDE assistants
  • Long-context chat and document analysis
  • Agent workflows with large memory windows
  • Agentic systems with function or tool calling
  • Workflow automation and API orchestration
  • Complex problem-solving and multi-step reasoning tasks
  • Planning and strategic decision-making applications
When should you avoid qwen.qwen3-coder-480b-a35b-v1:0?
  • High-volume text generation where output cost dominates
  • Streaming or verbose response workloads
  • Applications requiring image, audio, or multimodal inputs
  • Non-technical content generation or general-purpose chat
  • Creative writing or marketing copy generation
How does qwen.qwen3-coder-480b-a35b-v1:0 compare to similar models?

Compared to other models in a similar category, this model is more cost-efficient on input tokens but relatively expensive on output tokens. It is better suited for retrieval-heavy or context-rich workflows than generation-heavy use cases.

Understanding qwen.qwen3-coder-480b-a35b-v1:0 pricing
  • qwen.qwen3-coder-480b-a35b-v1:0 is a coding and software development model provided by Bedrock Converse.
  • Input tokens are priced at $0.22 per 1M tokens.
  • Output tokens are priced at $1.80 per 1M tokens.
  • The model supports a maximum input capacity of 262,000 tokens, capable of working with large codebases and complex software projects.
  • Maximum output length is 65,536 tokens.
  • For this model, input tokens are less expensive than output tokens, so optimizing your prompts can help manage costs.
  • Supports function calling for executing code snippets, API calls, and development tools.
  • Features advanced reasoning capabilities for complex problem-solving tasks.
  • Bedrock Converse offers qwen.qwen3-coder-480b-a35b-v1:0 for coding and software development workloads — code generation, code completion, debugging, and software development tasks.

How to Use This Calculator

Step 1: Enter the number of input tokens you expect to use. Input tokens include your prompt, system messages, and any context you provide to the model.

Step 2: Specify the number of output tokens you anticipate. Output tokens are the text generated by the model in response to your input.

Step 3: Review the cost breakdown to see the total estimated cost for your usage. The calculator automatically updates as you adjust the token counts.

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