GPT-6.1 Sol auf Amazon Bedrock: viel Leistung für ein Fünftel des Preises

Posted on Oct 26 • Originally published at aidatalegends.com

OpenAI has introduced GPT-6.1 Sol, a new model designed to bring much of GPT-6 Astra's capability to workloads where cost matters.

The headline is simple: OpenAI says GPT-6.1 Sol approaches GPT-6 Astra on agentic coding, computer use, and professional work at one-fifth of Astra's standard API input and output token prices.

But the more important question for engineers is what happens when you put that model inside a production agent.

A real agent doesn't make one model call and disappear. It reads files, searches for information, calls tools, writes code, tests the result, recovers from failures, and sometimes repeats the whole process.

So a model that can deliver near-frontier capability at a much lower token price can change the economics of the entire workflow.

And GPT-6.1 Sol is now generally available through Amazon Bedrock, with AWS identity, networking, auditing, and security controls around the inference layer.

That combination makes Sol much more interesting than just another model release.

Think about a software-engineering agent working on an unfamiliar repository.

Understand the repository ↓ Find relevant files ↓ Trace dependencies ↓ Plan the change ↓ Write code ↓ Run tests ↓ Inspect failures ↓ Fix the implementation ↓ Run tests again Enter fullscreen mode Exit fullscreen mode A small difference in model capability or pricing can become a large difference across the full task.

OpenAI reports that GPT-6.1 Sol matches GPT-6 Astra on DeepSWE v1.1, a benchmark for long-horizon software-engineering tasks, while exceeding GPT-6 Sol's best result by 6.4 percentage points at a lower reasoning effort and cost.

The same pattern appears in other evaluations.

On AutomationBench, GPT-6.1 Sol scores 2.2 percentage points above Opus 5.5 at medium reasoning effort, while costing roughly one-third as much on the evaluated task. OpenAI also reports a 4.8-point improvement over GPT-6 Sol at the same reasoning setting.

On OSWorld 2.0, GPT-6.1 Sol improves by seven percentage points over GPT-6 Sol at maximum reasoning effort and comes within 2.1 percentage points of Astra's score, according to OpenAI.

For AI engineers, that creates an interesting question:

How much of your agent's workload really needs your most expensive model?

GPT-6.1 Sol is effectively an answer to that question.

OpenAI's standard API pricing compares the GPT-6 family like this:

That makes GPT-6.1 Sol approximately one-fifth the standard input and output token price of GPT-6 Astra.

GPT-6.1 Sol's cached input price is $0.10 per million tokens, which OpenAI describes as 95% below its standard input price and 50% below GPT-6 Sol's cached input price.

For an agent repeatedly working against the same repository, instructions, documents, or context, that difference can become significant.

A ten-token prompt isn't where economics get interesting.

A million-token workload repeated across hundreds of agent interactions is.

GPT-6.1 Sol became generally available on Amazon Bedrock on September 29, 2026.

AWS describes the Bedrock deployment as running on an inference engine designed for performance, security, and reliability at scale.

For enterprises already operating inside AWS, the important part isn't just the model.

Bedrock lets organizations control model access through AWS IAM, audit invocation activity through CloudTrail, and keep traffic inside their network boundaries using VPC endpoints powered by AWS PrivateLink.

AWS also states that inference runs on hardware-isolated infrastructure with zero-operator access, meaning AWS operators cannot access customer prompts and completions during inference. AWS says inference data isn't used for model training, and using GPT-6.1 Sol doesn't require customers to opt into sharing data with OpenAI.

For automated abuse detection, AWS says classifier-flagged traffic can be retained for up to 30 days and processed programmatically. Customers can request zero data retention through their AWS account team.

For regulated or sensitive workloads, that distinction matters.

This is an excerpt. The full article continues with:

Read the full article on AI Data Legends, with diagrams, code and sources.