There is no universal winner for every business task. Compare these models using current provider documentation and a repeatable test of the work you actually need completed. Prices below were checked on 7 October 2026; this article does not report an independent DeployOps head-to-head benchmark.
Standard text API rate per million tokens
| Token Type | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|
| Input | US$4 | US$10 |
| Output | US$20 | US$50 |
Claude Opus 5.5 API pricing has lower listed base rates in this comparison. GPT-6 Astra API pricing also varies with applicable modes and usage conditions. Check provider pricing before purchase.
Compare the completed task
AI model cost per completed task includes failed attempts, retries, tools and reviewer time. For a simple illustrative uncached request using 10,000 input and 2,000 output tokens, the listed base token cost is US$0.08 for Opus and US$0.20 for Astra. This arithmetic does not establish equal task quality or total workflow cost.
Benchmark comparison by category
Research
Evaluate source accuracy, relevance and unsupported claims.
Coding
Evaluate tests passed, regressions and human review effort.
Tool use
Evaluate correct tool choice, arguments and final state.
Long tasks
Evaluate completion, recovery behavior and total operational cost.
Frequently Asked Questions
Which is better, Claude Opus 5.5 or GPT-6 Astra?
The better choice depends on the task, tool setup and acceptance criteria. This article does not establish a universal winner or report an independent benchmark. Run both models on representative work, review the completed outputs and compare failure handling, reviewer effort and total cost before selecting a deployment configuration.
Are API token prices the same as subscription prices?
No. API token rates describe usage-based charges for requests, while subscription plans cover a separate product offering with its own terms and limits. Additional tools, caching and service modes may affect API costs. Check the current provider pricing for the exact configuration rather than treating one rate as universal.
How should coding models be compared fairly?
Give each model the same repository state, task, tool access and acceptance tests. Record model versions and relevant settings, then review the resulting code for correctness and regressions. Include several representative tasks and count retries and human fixes instead of comparing only a single impressive demonstration or self-reported completion.
Why measure cost per completed task?
A low price per token can still produce a costly workflow if it needs repeated attempts or extensive correction. Cost per completed task includes the resources used to reach an acceptable result. Combine usage charges with tool costs and reviewer effort, using the same success definition for each model.