via InferenceSaver Gateway
200K
32K
$5.00
$25.00
Opus 4.7 is a flagship large language model available through InferenceSaver that delivers exceptional performance across a wide range of natural language processing tasks. This model combines advanced architecture with extensive training to provide reliable, high-quality outputs for both simple and complex queries.
Built with modern neural network architecture, Opus 4.7 excels at understanding context, generating coherent responses, and maintaining consistency across long conversations. It has been trained on diverse datasets to ensure broad knowledge coverage across technology, science, creative writing, and more.
The model supports a 200K context window and can generate up to 32K output tokens per response. Whether you're building chatbots, content generation systems, code assistants, or analytical tools, Opus 4.7 provides the intelligence and reliability you need via InferenceSaver's optimized gateway.
Exceptional comprehension of context, nuance, and intent across diverse topics and domains.
Large context window enables processing of lengthy documents and maintaining extended conversations across 200K tokens.
State-of-the-art reasoning and generation quality for the most demanding production workloads.
Easy to integrate with existing systems through REST API, with support for streaming responses and function calling.
| Feature | Supported |
|---|---|
Vision Support Process and analyze images, charts, and visual content | No |
Function Calling Execute custom functions and integrate with external tools | No |
Streaming Real-time token-by-token response generation | Yes |
Structured Output Generate responses conforming to JSON schemas | No |
| Property | Value | Description |
|---|---|---|
| Context Window | 200K | Maximum input tokens the model can process at once |
| Max Output Tokens | 32K | Maximum tokens the model can generate in a single response |
Token Usage Note
Tokens can be words or parts of words. On average, 1 token is approximately 4 characters or 0.75 words in English. The actual token count depends on the specific text and language.
For optimal results, provide clear and specific instructions. Include relevant context and examples when possible. Break down complex tasks into smaller, manageable steps for better accuracy.
Implement appropriate rate limiting and error handling in your application. Consider implementing retry logic with exponential backoff for production deployments.
Monitor your token usage and optimize prompts to reduce unnecessary tokens. Use streaming for better user experience without increasing costs. Prompt caching is available and can significantly reduce costs for repeated contexts.