GPT-6.1 Sol vs Claude Opus 5.5: Price & Benchmarks

Quick verdict: GPT-6.1 Sol leads on reported cost-performance, but the comparison is not fully independent

But benchmark leadership is not the same as the lowest cost in production. Your actual bill depends on input and output tokens, cached context, reasoning effort, fallbacks, retries, latency requirements, and how often a response succeeds on the first attempt. The published comparisons also come primarily from OpenAI's own evaluation claims, so they should be treated as a starting point rather than a final verdict.

GPT-6.1 Sol vs Claude Opus 5.5 pricing

• Input: $2 per million tokens
• Cached input: $0.10 per million tokens
• Output: $10 per million tokens

That cached-input rate is important for long-running agents. A system repeatedly sending the same instructions, repository context, or document prefix may pay far less than the standard input rate after the content is cached.

However, output is five times more expensive than uncached input. A task that generates extensive reasoning, code, tool calls, or explanations can therefore cost substantially more than its prompt suggests.

Claude Opus 5.5 should be compared using the same accounting method, not simply by placing one model's input rate beside another's. The meaningful figure is the cost of a completed task, including every attempt and every model used in the workflow.

A useful calculation is:

Cost per successful result = total spend, including retries and fallbacks, divided by successful completed tasks.

Track input tokens, cached tokens, output tokens, tool calls, latency, and failure rates. This exposes pricing traps that a per-million-token table cannot show.

What the reported benchmarks show

That suggests a meaningful improvement for software-engineering tasks, particularly when the model must inspect a codebase, plan changes, edit files, and validate its work. Still, the result does not prove that every repository or programming language will produce the same economics. Dataset composition, tool configuration, patch limits, and success criteria all matter.

The fallback detail matters. A fallback can improve reliability, but it changes what is being compared. If one system uses multiple model passes or escalation routes and the other does not, compare the complete workflow, not just the primary model's nominal price. This result is also based on OpenAI's evaluation claims rather than an independent head-to-head study.

This is one of the more practical comparisons because it connects quality with a specified reasoning setting. Yet “medium” is not a universal unit. Different vendors may expose reasoning controls differently, and a model's token usage can change sharply between medium and maximum effort.

The result is impressive on paper, but maximum effort may increase latency and output volume. A cheaper task can become less attractive if users must wait substantially longer or if the system generates many more intermediate actions. Quality, speed, and cost need to be measured together.

The biggest pricing and benchmark traps

Also record:

• The selected reasoning effort
• Number of retries
• Fallback models and escalation rules
• Output and tool-call tokens
• Human review time
• Successful completion rate

A workflow that appears to use GPT-6.1 Sol alone may actually use a fallback for difficult cases. That is sensible operationally, but the fallback cost belongs in the comparison.

Before switching a production workload, reproduce a small test using representative tasks. Include easy, typical, and adversarial examples. Review not only the score, but also the types of errors: incorrect code, skipped steps, unsafe actions, weak citations, or unnecessary tool calls.

Measure time to a correct completed result. A slower response that succeeds immediately may beat a faster response requiring two retries. Conversely, a fast model can be valuable when thousands of short calls create a queue. Workload shape determines whether the premium or discount is worthwhile.

Which model makes more sense for different workloads?

Bottom line: compare completed tasks, not headline prices

GPT-6.1 Sol's reported results make it a serious cost-performance contender. The strongest claims include Astra-like DeepSWE v1.1 performance at roughly one-fifth the cost, a reported GDP.pdf advantage over Claude Opus 5.5 at less than half the task cost, and a 2.2-point AutomationBench lead at roughly one-third the cost.

Treat those figures as directional until independently tested. Run both models on a representative sample and record quality, retries, fallback usage, input and output tokens, latency, and total spend. The winner is the model with the lowest cost per successful result - not necessarily the lowest price per million tokens.

Step-by-Step Guide

1. 1

   Define the completed task

   Specify what counts as success for each workflow, including correctness, required output format, tool actions, safety checks, latency, and human review requirements.

2. 2

   Normalize model settings

   Run both models with comparable prompts, context, tool access, reasoning effort, stopping rules, and fallback policies so the comparison measures equivalent workflows.

3. 3

   Measure every cost component

   Record input, cached-input, output, and tool-call tokens along with retries, fallback usage, API charges, latency, and human intervention time.

4. 4

   Test representative workloads

   Use easy, typical, and adversarial examples drawn from your real coding, document, browser, or automation tasks rather than relying only on public benchmark scores.

5. 5

   Calculate cost per successful result

   Divide total workflow spend, including retries and fallbacks, by the number of successfully completed tasks, then compare quality and time-to-success alongside price.

Key Statistics

• GPT-6.1 Sol is reported to cost $2 per million input tokens, $0.10 per million cached-input tokens, and $10 per million output tokens.Pricing figures stated in the article's comparison of reported GPT-6.1 Sol API rates.
• GPT-6.1 Sol reportedly matches Astra on DeepSWE v1.1 at roughly one-fifth the cost and improves on GPT-6 Sol's best result by 6.4 percentage points.OpenAI-reported evaluation claims summarized in the article; the article notes that dataset, tooling, patch limits, and success criteria affect interpretation.
• GPT-6.1 Sol reportedly leads Claude Opus 5.5 by 2.2 percentage points on AutomationBench at medium reasoning effort while costing roughly one-third as much.OpenAI-reported AutomationBench comparison summarized in the article; reasoning settings may not be equivalent across vendors.
• GPT-6.1 Sol reportedly comes within 2.1 percentage points of Astra on OSWorld 2.0 at maximum reasoning effort for about one-seventh of the task cost.OpenAI-reported offline OSWorld 2.0 comparison summarized in the article; maximum reasoning effort may increase latency and output volume.

Frequently Asked Questions