Try Bifrost Enterprise free for 14 days.
Request access[ MODEL COMPARISON ]
Compare openai.gpt-5.4 with other models
Select another model to compare pricing, limits, and capabilities with openai.gpt-5.4.
VS
Models
openai.gpt-5.4bedrock_mantle
-
-
Context Length
272K
-
Max Output
128K
-
Input Cost
$2.75/M
-
Output Cost
$16.50/M
-
Mode
Responses
-
Max Input Tokens
272K
-
Max Tokens
128K
-
Supported Endpoints
/v1/responses
-
Provider
Bedrock Mantle
-
Tool Choice
Yes
-
Response Schema
Yes
-
Prompt Caching
Yes
-
Scale with the Fastest LLM Gateway
Built for enterprise-grade reliability, governance, and scale. Deploy in seconds.
Comparison Insights
Comprehensive analysis based on the latest model metadata from the comparison table above.
What should I know about openai.gpt-5.4?
Overview
- openai.gpt-5.4 is a responses model provided by Bedrock Mantle.
- With a context window of 272K tokens, this model can handle substantial inputs such as detailed documents or extended conversation histories.
Pricing
- Input processing costs $2.75 per million tokens.
- Output generation costs $16.50 per million tokens.
Output Capabilities
- The model can generate up to 128K tokens in a single response.
Availability
- Available through the following endpoints: /v1/responses.
What capabilities does openai.gpt-5.4 support?
- Supports function calling, enabling integration with external tools and APIs for extended functionality.
- Includes vision capabilities to process and analyze images alongside text inputs.
- Features advanced reasoning capabilities for complex problem-solving and multi-step logical tasks.
- Allows explicit tool selection, giving developers fine-grained control over function execution.
- Supports structured response schemas for consistent, predictable output formatting.
- Implements prompt caching to reduce costs and latency for repeated or similar queries.
openai.gpt-5.4 Pricing Overview
At $2.75 per 1M input tokens and $16.50 per 1M output tokens, openai.gpt-5.4 ranks 25 out of 50 responses models by input cost. It is more affordable compared to the median of $4.13 for responses models, and is cheaper than 50% of models in this category.