GPT-6.1 Sol

OpenAI — GPT-6.1 Sol

Model Details

OpenAI GPT-6.1 Sol brings advanced capabilities to workloads where both performance and cost matter. GPT-6.1 Sol helps agents investigate codebases and iterate on solutions. Agents can also use it to understand complex documents and complete business and computer use workflows across multiple steps.

Input Modalities	Output Modalities
Audio	Embedding
Image	Image
Speech	Speech
Text	Text
Video	Video

Endpoints and APIs supported

The following tables show which endpoints and APIs GPT-6.1 Sol supports. For more information, see APIs supported by Amazon Bedrock and Endpoints supported by Amazon Bedrock.

Endpoint support

Endpoint	Supported
bedrock-runtime
bedrock-mantle

APIs supported on bedrock-runtime

Messages	Responses	Chat Completions	Converse	Invoke

APIs supported on bedrock-mantle

Messages	Responses	Chat Completions	Converse	Invoke

Capabilities and Features

Bedrock Features

Features supported using bedrock-runtime

Features supported using bedrock-mantle

Prompt caching

GPT-6.1 Sol supports both Implicit Prompt Caching and Explicit Prompt Caching. API support differs by endpoint:

For explicit caching, set prompt_cache_options.mode to explicit. Set prompt_cache_options.ttl to 30m, the only supported TTL and the default. In Responses requests, add "prompt_cache_breakpoint": {"mode": "explicit"} to an input_text content block. In Chat Completions and InvokeModel requests, add it to a text content part.

A cacheable prompt prefix must contain at least 1,024 tokens. You can include multiple explicit breakpoints, and each request can create up to four cache writes. Check the cache usage fields in the response to determine whether tokens were written to or read from the cache.

JSON Schema output

Use JSON Schema to set the format of the model response for streaming and non-streaming requests. Chat Completions and Responses support JSON Schema output on both the bedrock-runtime and bedrock-mantle endpoints. InvokeModel, InvokeModelWithResponseStream, Converse, and ConverseStream support JSON Schema output on the bedrock-runtime endpoint. Choose a model ID or inference profile ID from Programmatic access.

Use an object schema. Put all fields in required. Set additionalProperties to false. Validate your schema before you send it. Check for refusals or incomplete responses before you parse the output.

{
  "additionalModelRequestFields": {
    "text": {
      "format": { "strict": true }
    }
  }
}

Pricing

All prices are in USD per 1 million tokens for the Standard tier. Global CRIS rates match OpenAI first-party Standard pricing.

Commercial In-Region and US geographic cross-Region inference (US CRIS) prices include a 10% premium over the global base rates. You do not need to add this premium.

GPT-6.1 Sol supports both implicit and explicit prompt caching. Cache-write tokens are billed at 1.25× the uncached input-token rate, and cache-read tokens are billed at 0.05× the uncached input-token rate. For configuration and API support, see Prompt caching.

Long-context rates apply to the full request when input exceeds 272,000 tokens.

Priority and Flex tiers are not supported for this model.

Commercial Regions — short context (272K input tokens or fewer)

Commercial Regions — long context (more than 272K input tokens)

Programmatic Access

To call this model from code, use the following model IDs and endpoint URLs. For more information, see APIs supported by Amazon Bedrock and Endpoints supported by Amazon Bedrock.

For in-Region access, use bedrock-mantle in us-east-1 (N. Virginia). On bedrock-runtime, use us.openai.gpt-6.1-sol for US geographic cross-Region inference or global.openai.gpt-6.1-sol for global cross-Region inference. Direct in-Region invocation is not supported on bedrock-runtime. Use a source Region enabled for the profile you choose; see Route model inference requests across AWS Regions with cross-Region inference.

Service Tiers

Amazon Bedrock offers several service tiers for different workloads. Standard gives you pay-per-token access with no commitment. To use it, set "service_tier": "default" or omit the field. For more information, see service tiers.

Standard	Priority	Flex	Reserved

Regional Availability

Regional availability at a glance

Mantle access is available in us-east-1 (N. Virginia). Runtime access supports both US geographic and global inference profiles. For more information, see Regional availability by models.

Availability using the bedrock-mantle endpoint

Region	In-Region	Geo	Global
us-east-1 (N. Virginia)

Availability using the bedrock-runtime endpoint

Scope	In-Region	Geo	Global
US geographic and global inference

Quotas and Limits

Quotas vary by account and Region. See Quotas for Amazon Bedrock and the model's service quota settings.

The output-token burndown rate is 10: each output token consumes 10 tokens of quota.

Sample Code

Step 1 - AWS Account: If you already have an AWS account, skip this step. If you are new to AWS, sign up for an AWS account.

Step 2 - API key: Go to the Amazon Bedrock console and generate a long-term API key.

Step 3 - Get the SDK: You must have Python installed to use this guide. Then install the OpenAI SDK.

python3 -m pip install openai

Step 4 - Set environment variables

Set up your environment to use the API key for authentication.

Step 5 - Run your first inference request

Save the file as bedrock-first-request.py.

bedrock-mantle

Use the settings from Step 4 - Set environment variables. Choose the bedrock-mantle tab. The Chat tab uses the Chat Completions API.

bedrock-runtime: OpenAI SDK

Use the settings from Step 4 - Set environment variables. Choose the bedrock-runtime tab. Send your request with the Responses API. The example uses the US CRIS profile. For Global CRIS, replace us.openai.gpt-6.1-sol with global.openai.gpt-6.1-sol.