Opus 5.5 vs Sonnet 5.5 vs GPT-6 Astra: Benchmarks and API Pricing

Three models set the bar for the market this autumn: Claude Opus 5.5 (released on 22 September 2026), Claude Sonnet 5.5 (28 September) and OpenAI’s current flagship, GPT-6 Astra. The short version: Opus 5.5 leads most of the published tests, Sonnet 5.5 comes close at half the price, and Astra wins a few specialised tasks while being the most expensive per token.

Below are the numbers from the vendors’ own pages, a cost estimate on a realistic scenario, and our view on which model fits which job.

$2 / $10

Sonnet 5.5: input / output per 1M tokens

cheapest of the three

$4 / $20

Opus 5.5: input / output per 1M tokens

Opus 5 was $5 / $25

$10 / $50

GPT-6 Astra: input / output per 1M tokens

cached input is $1

The three contenders

Claude Opus 5.5 is Anthropic’s flagship for long-running agentic work on code and documents. It is cheaper than Opus 5: input dropped from $5 to $4 per million tokens and output from $25 to $20. Thinking is always on, and its depth is controlled by the effort parameter (low through max), with medium as the default. A fast mode runs up to 2.5x quicker at double the price: $8 / $40.

Claude Sonnet 5.5 is the mid-tier model, described by Anthropic as the best balance of speed and intelligence. Pricing stays at the Sonnet 5 level of $2 / $10. Anthropic says it produces output more than 30% faster than its predecessor and costs up to 30% less per typical task.

GPT-6 Astra is OpenAI’s current top model. The API charges $10 per million input tokens, $1 for cached input and $50 for output. The context window is 1.05M tokens, responses go up to 128K tokens, and the knowledge cutoff is April 2026.

On raw capacity the three are close: both Claude models offer a 1M-token window and the same 128K output ceiling. A contract, a product catalogue or a support knowledge base fits into any of them, so quality and price decide.

What the official benchmarks say

Anthropic published comparison tables with both launches. The Opus 5.5 table includes a GPT-6 Astra column, and some of those Astra figures (Terminal-Bench) come from OpenAI’s own reporting. The Sonnet 5.5 page does not list Astra, so Sonnet and Astra can only be compared on tests that appear on both pages.

Benchmark	What it measures	Sonnet 5.5	Opus 5.5	GPT-6 Astra
Terminal-Bench 4.0	agentic work in a terminal	70.6%	66.4%	57.9%
GDPval-AA v2.1 (Elo)	professional office tasks	1844	1846	1542
Humanity’s Last Exam (with tools)	hard expert questions	64.5%	67.7%	57.2%
CursorBench 4.0	coding inside an IDE	55.5%	57.8%	—
OSWorld 2.1 (partial)	operating a computer	80.1%	81.8%	—
FrontierCode v1.1	hard coding problems	—	54.4%	53.3%
AutomationBench	business process automation	—	40.0%	41.4%
Terminal-Bench-Science 0.1	scientific tasks in a terminal	—	58.7%	64.6%

A dash means the vendor did not publish a result for that test. Anthropic reports Opus 5.5 at its highest effort settings, so results at the default setting may be lower.

How we read it:

• Opus 5.5 is the most consistent. It leads or sits next to the leader on nearly every row and beats Astra by a wide margin on GDPval-AA office tasks.
• Sonnet 5.5 is close behind Opus. The gap is usually a few points, and on Terminal-Bench 4.0 Sonnet is actually ahead, at half the token price.
• Astra shines in science and automation. It beats Opus on Terminal-Bench-Science and narrowly on AutomationBench, but trails clearly on GDPval-AA and Terminal-Bench 4.0.

An independent view

Vendor benchmarks are a home game: each company picks the tests, settings and effort level. Independent measurements help. According to Artificial Analysis (testing published on 9 September 2026), GPT-6 Astra at max settings scored 53 on its Intelligence Index, tying Claude Fable 5.1. The more interesting part is efficiency: by their count Astra uses about 27K output tokens per task and costs $3.26 per task, against $7.63 for Fable 5.1. Artificial Analysis also reports that Astra’s hallucination rate on its AA-Omniscience test fell from 92% to 51%. Opus 5.5 and Sonnet 5.5 were not part of that report, and these figures have so far been reported by a single independent source.

The practical lesson: price per token and price per task are different things. A model that reasons more briefly can end up cheaper even with a higher list price. Test that on your own workload.

What it costs in practice

Take a hypothetical example: an online store’s support bot handles 10,000 conversations a month. Each one sends the model about 3,000 input tokens (instructions, a slice of the catalogue, chat history) and gets back 500 output tokens. That adds up to 30M input and 5M output tokens. At list prices, without caching and without counting thinking tokens:

• Claude Sonnet 5.5 110 %
• Claude Opus 5.5 220 %
• GPT-6 Astra 550 %

Calculated from API prices: Claude Platform Docs, OpenAI API Docs (October 2026)

Treat this as a rough guide, not a quote. Three things move the real bill:

1. Caching. When instructions and catalogue data repeat in every request, caching cuts input costs sharply: Claude cache reads cost $0.20 per million tokens, Astra cached input costs $1.
2. Thinking tokens. All three models can reason before answering, and those tokens are billed as output. Higher effort means a higher bill.
3. Batch processing. For work that is not time-sensitive (tagging products, analysing reviews), Anthropic’s Batch API is 50% cheaper.

Which one to pick

This is our assessment of the published data, not a rule; a test on your own tasks has the final say.

Job	Our pick	Why
Support bot answering from a knowledge base	Sonnet 5.5	fast, half the price of Opus, nearly the same quality
Product descriptions, translation, SEO copy	Sonnet 5.5	high volume, where cost matters more than the last few points of quality
Complex agents: multi-step flows, code, CRM	Opus 5.5	the most consistent results across tests
Analysing documents, contracts, reports	Opus 5.5	leads on GDPval-AA and Humanity’s Last Exam
Scientific and engineering calculations	GPT-6 Astra	best score on Terminal-Bench-Science
Simple routing and ticket classification	none of the three	cheaper models such as Claude Haiku 5.5 exist for this

One model for everything is rarely the best setup. A more sensible design lets a cheap model take in and sort requests, a mid-tier model answer routine questions, and the flagship step in only for hard cases. That way the budget goes where customers actually notice the difference in quality.

One more tip: before launch, run 30 to 50 real conversations or documents through two or three models and compare the output. Benchmarks give the big picture, but your catalogue, your customers’ language and your scenarios can reshuffle the ranking. If you would rather not do this yourself, our AI automation for business service covers it, from choosing the model to launching the bot or agent.

What to do next

• For most business tasks, start with Sonnet 5.5: the best price-to-quality ratio of the three.
• Use Opus 5.5 for complex agents and document work where a mistake costs more than the tokens.
• GPT-6 Astra makes sense for scientific and some automation tasks, but compare cost per task, not per token.
• Test on your own data, turn on caching, and do not pay for max effort where you do not need it.

If you want help choosing a model and estimating the budget for your use case, get in touch with us and we will review the task and suggest an architecture.