Silver Week Reshapes AIGPT-6 Astra, Sol and Luna vs Claude Fable, Opus and Sonnet

The AI announcements arriving during Japan’s Silver Week holiday might look like another round of new names. September 2026 deserves a closer reading: the relationship between capability and price is changing within each company as well as between them.

OpenAI introduced its flagship GPT-6 Astra first, then expanded the family with Sol and Luna 19 days later. Anthropic released Fable 5.1 early in the month, followed by Opus 5.5, which challenges the boundary between its premium tiers. These releases change both what AI can do and how often people can afford to use it.^[1][2][3]

This comparison brings together GPT-6 Astra, Sol and Luna, and Claude Fable 5.1, Opus 5.5 and Sonnet 5. It examines release order, same-price rivals, measured quality, actual evaluation costs and the practical conditions for using them.

Information checked September 23, 2026. Official specifications, independent measurements and the author’s calculations and interpretations are distinguished below. This is a researched comparison, not a claim that the author personally tested all six under controlled conditions.

1. Release order connects the announcements

Start with the timeline. Dates follow official documentation and announcements; they do not specify the exact time availability began in Japan.

Dates come from release notes, model documentation and announcements.^[1][2][4][3]

Sol and Luna arriving later does not make Astra obsolete. OpenAI still positions Astra for its hardest end-to-end work and lists all three in the GPT-6 family. A newer release date does not automatically mean a higher capability tier.^[5]

Anthropic’s names need similar care. Opus “5.5” is numerically greater than Fable “5.1,” but these are versions of different families. Neither the premium family name nor the larger version number establishes a universal winner.

The available Sonnet comparison is Sonnet 5 as of September 23. Anthropic says Sonnet 5.5 and Haiku 5.5 will follow in the coming weeks. Upcoming products should not appear in a table as if readers can already choose them.^[3]

2. Astra first: OpenAI expands from capability to affordability

Astra’s role extends beyond answering isolated questions. OpenAI emphasizes workflows across code, browsers and professional software: researching a subject, comparing evidence and producing a document, or investigating a problem, fixing it and checking the result.^[5]

Sol and Luna change the budget for using the GPT-6 family. At standard uncached input and output rates, Sol costs one fifth as much per token as Astra; Luna costs one hundredth as much. Sol is a candidate for complex development and agent workflows, while Luna targets narrower, high-volume work.^[6][7][8]

The sequence suggests a progression from demonstrating a capability ceiling to widening the range of affordable applications. That is the author’s interpretation of the releases and prices, not a statement about OpenAI’s internal strategy.

Classifying ten thousand support requests and advancing a difficult migration project over several hours are different jobs. The former might become economical with Luna, while the latter might justify Astra when Sol struggles. The significance of cheaper GPT-6 models is broader than replacing Astra everywhere.

The same family does not mean interchangeable settings

Astra has no none reasoning setting, and API tool calling requires the Responses API. Sol and Luna include none among their reasoning settings. Before switching on price alone, check the assumptions your application makes about reasoning and tools.^[1][7][8]

Nor should a model comparison assume that expensive models need longer instructions. Supply the materials and completion criteria, then examine how independently each model advances the work. Overly rigid procedural instructions can prevent a capable model from choosing a better route.

3. Opus challenges Fable—and changes Sonnet’s context

Anthropic describes Opus 5.5 as reaching Fable 5.1’s level on much of its work. That raises a practical question: can tasks previously assigned to a premium model now be completed at a lower unit price? This is a vendor claim; the independent evaluation later in this article is a separate source of evidence.^[3]

Fable 5.1 remains positioned for demanding reasoning and long-horizon agentic work. It can still be worth using where it produces more reliable results. What is harder to justify is paying extra solely because its family name occupies a premium position. The difference should appear in the work you actually need completed.^[2]

Sonnet 5 sits below these options. Its standard input and output prices match Sol, making Sol versus Sonnet 5 a natural same-budget comparison for everyday development, writing and research. Moving from Sonnet to Opus doubles those unit prices; fewer corrections or failed attempts could justify that difference.^[4][9]

There are therefore three useful comparisons: Sol versus Sonnet at the middle price tier; Astra versus Fable at the premium tier; and Opus versus Astra and Fable to see how much premium work can move to lower unit prices. Comparing Opus only with Sol and Luna misses that last relationship.

Where do Mythos and Haiku fit?

Anthropic describes Fable 5.1 and Mythos 5.1 as having the same capabilities with different safeguards. Mythos is available through restricted, vetted access programs, so it is not treated here as a freely available seventh choice.^[10]

The current Haiku 4.5 is another lower-cost option, priced at $1 input and $5 output per million tokens. This article focuses on the latest GPT-6 family and the reshaping of Claude’s premium and middle tiers. Luna’s ordinary unit rates are one tenth of Haiku’s, but that alone does not establish superior performance across tasks.^[4]

4. Six-model pricing: identify the same-price rivals

These are API usage prices in USD per million tokens. Tokens are processing units, not a fixed number of Japanese characters or English words. OpenAI prices are Standard rates for inputs up to 272K; Claude prices are ordinary rates. Taxes, tool charges and cache creation are excluded.^{[11][9][2][4]}

With Luna as 1, uncached input and output prices have a 1:20:20:40:100:100 ratio. Cache reads tell a different story: Fable costs one quarter of Astra, while Opus matches Sol and Sonnet. Repeatedly reading shared documents changes which column matters.

The GPT-6 models have 1.05-million-token context windows; the three Claude models have one million. All six list a standard maximum output of 128K. Capacity is not the same as reliably finding and using the right information inside it. A five-percent capacity difference is not automatically a five-percent improvement in usefulness.^{[6][7][8][9][2][4]}

What would 1,000 identical token budgets cost?

Assume 10,000 input tokens and 3,000 billable output tokens per task, 1,000 tasks and no caching. This is the author’s fixed-usage calculation, not a prediction that all models will consume equal token counts.

• Luna: $2.50, comprising $1 input and $1.50 output.
• Sol or Sonnet 5: $50 each, comprising $20 input and $30 output.
• Opus 5.5: $100, comprising $40 input and $60 output.
• Astra or Fable 5.1: $250 each, comprising $100 input and $150 output.

A cheap model that passes on its first attempt is attractive. But if the extra $50 from Sol to Opus saves 100 minutes of review and correction, it breaks even at an assumed labor cost of $30 an hour. Put human correction time next to the API bill.

5. Intelligence and measured cost disrupt the price hierarchy

The following uses one evaluator: Artificial Analysis’s Intelligence Index v4.3.2, as displayed on the check date. All models use max settings; Opus and Fable include Default Fallback, which can switch models after a refusal. This is therefore not a perfectly controlled comparison of isolated models.^{[12][13][14][15][16][17]}

The index combines multiple evaluations; 58 does not mean 58-percent accuracy. Task cost is a weighted evaluation average, not the expected price of one of your emails. Equally named effort levels do not guarantee equal compute budgets.^[18]

Three relationships stand out.

First, Opus 5.5 scores above Astra and Fable on this aggregate. Higher unit prices do not buy a higher result on every metric. Claude users have a reason to test Opus before assuming they need Fable.

Second, Astra and Fable both score 53, but cost $3.26 and $7.63 per evaluation task. The ordinary unit rates match, yet measured costs differ by more than twofold. Astra also costs less per evaluation task than Opus despite its higher unit prices. Token consumption and other usage differences can reverse the price-table ordering.

Third, Sol and Sonnet have equal ordinary unit prices, but Sol produces a higher index score at much lower measured cost in these settings. That gives existing Sonnet users a concrete reason to try Sol. It does not erase the value of a working integration, preferred Japanese prose or suitability for a particular codebase.

Astra for science, Opus overall? Separate the workloads

Anthropic’s published comparisons offer another example of changing order. On Terminal-Bench-Science 0.1, its table lists Astra at 64.6, Opus 5.5 at 58.7 and Fable 5.1 at 52.6. On Terminal-Bench 4.0, it lists Opus at 66.4, Astra at 57.9 and Fable at 55.8.^[3]

Those are vendor-published results with different evaluations and execution conditions from the independent aggregate above. Reasoning settings are not fully matched across models either. They do not prove that Astra always wins scientific work. They illustrate why keeping only the aggregate winner can discard a strong specialist candidate.

Also check index versions before comparing with older articles. A changed evaluation suite can change the score. Seeing Astra at 61 in an older version and 53 here does not by itself demonstrate that the model deteriorated.

6. Caching, reasoning and waiting time change the economics

Repeated document reads are an important part of Fable 5.1’s value proposition. Its ordinary input price matches Astra at $10, but cache reads cost $0.25. Comparing only one million cached read tokens gives $1 versus $0.25. Initial writes, expiry, cache misses and output still matter; the complete task does not necessarily become four times cheaper.^[2][6]

For all three OpenAI models, inputs above 272K trigger double input and cache rates and 1.5-times output rates for the entire request. A large context window does not imply short-context prices all the way to its limit.^[11]

Visible answer length also does not reveal all consumption. OpenAI bills reasoning tokens as output even when they are not shown in the answer. The previous section’s cost reversals are a reminder to separate unit price from quantity used.^[19]

Fast text and fast completion are different

Output speed, time to the first visible answer and time to a usable result measure different things. A model can generate quickly but take longer overall through extensive reasoning, failed attempts and corrections. A higher-priced model can sometimes finish in fewer steps and reduce both waiting and review.

For collaborative editing, conversational responsiveness matters. For an overnight research assignment, completion quality and total cost may matter more than a few seconds of response latency. Decide whether you are waiting for the first sentence or the finished work before choosing a speed metric.

Default API reasoning settings also differ. Sonnet 5 and Fable 5.1 default to high; Opus 5.5 defaults to medium. Impressions from ordinary defaults and the max-effort results above are not observations under the same conditions.^[4][9]

7. Include subscriptions and access in the choice

The dollar amounts above describe API use. ChatGPT and Claude subscriptions, and Codex and Claude Code allowances, are separate comparisons. A halved API unit price does not imply a halved subscription or twice as many permitted requests. Check model access, limits and additional-usage terms for the service you actually buy.

Selecting a model name also does not guarantee identical browser control, file editing or integrations. The application and tools surrounding the model affect the final result. Once migration effort is included, staying with a reliable environment can make sense even when another model scores somewhat higher.

Based on this comparison, these are the author’s suggested starting points, not official vendor recommendations:

• Everyday development, writing and research: Sol versus Sonnet 5. Equal ordinary prices make quality and correction time easy to compare. Current independent evidence gives Sol a concrete case for a trial.
• Difficult deliverables within Claude: Opus 5.5, then Fable 5.1. If Opus meets the requirements, it may reduce costs. Ask Fable to demonstrate a specific improvement worth its premium.
• Complex research and long autonomous work: Astra, Opus and Fable. Compare the premium candidates using the same materials and completion requirements. Astra’s scientific results are a reason to keep it in the candidate set.
• High-volume, narrow product workflows: start with Luna. Test extraction, classification and other tasks whose correctness can be checked. The large unit-price gap matters when quality is sufficient.

Combining models is another option. Under the earlier fixed-usage assumptions, processing 1,000 items with Luna and reprocessing 100 with Sol costs $7.50, compared with $50 for Sol throughout. But Luna will not necessarily recognize its own mistakes. The calculation becomes useful only if format checks, known answers, sampling or other controls can reliably identify work requiring escalation.

8. September expands where useful intelligence can reach

Astra and Fable 5.1 at the beginning of the month pushed the question of how much difficult work AI can take on. Sol, Luna and Opus 5.5 on September 22 sharpened the question of which budgets can support that work. Release order turns separate product announcements into a connected story.

Within OpenAI, can Sol handle work that previously justified Astra, and can Luna take over tasks previously routed to Sol? Within Anthropic, can Opus finish work previously assigned to Fable, and how much rework disappears when moving up from Sonnet? Across companies, the same-price contests are Sol versus Sonnet and Astra versus Fable.

When the next announcement arrives, ask three questions: What improved at the same price? What does the same acceptable result now cost? Which previously unaffordable uses become practical? These reveal more than memorizing a leaderboard winner.

To try the models after the holiday, collect a few tasks that recently gave you trouble and choose two or three candidates. Match the materials and acceptance criteria. Record cost, completion time, serious mistakes and correction effort. Score stylistic preference separately from factual accuracy.

The interesting change is that the reasons to pay for an expensive model and the range of jobs a cheap model can handle are shifting together. The complete GPT-6 family, including Astra, and the relationships among Fable, Opus and Sonnet make that shift visible.

Images are AI-generated conceptual illustrations. Calculations use the stated assumptions and do not represent measured performance or guaranteed usage allowances. Prices, access conditions and evaluation results can change; check the linked sources when adopting a model.

References

1. [1]API Changelog — September 22, 2026. Checked September 23, 2026. ↩
2. [2]Anthropic — Claude Fable 5.1 overview. Checked September 23, 2026. ↩
3. [3]Introducing Claude Opus 5.5. Checked September 23, 2026. ↩
4. [4]Anthropic — Claude Sonnet 5 overview. Checked September 23, 2026. ↩
5. [5]OpenAI — Model guidance: Using GPT-6. Checked September 23, 2026. ↩
6. [6]OpenAI — GPT-6 Astra. Checked September 23, 2026. ↩
7. [7]GPT-6 Sol. Checked September 23, 2026. ↩
8. [8]GPT-6 Luna. Checked September 23, 2026. ↩
9. [9]Claude Opus 5.5 overview. Checked September 23, 2026. ↩
10. [10]Anthropic — Introducing Claude Fable 5.1 and Claude Mythos 5.1. Checked September 23, 2026. ↩
11. [11]API Pricing. Checked September 23, 2026. ↩
12. [12]Artificial Analysis — GPT-6 Astra (max). Checked September 23, 2026. ↩
13. [13]GPT-6 Sol (max). Checked September 23, 2026. ↩
14. [14]GPT-6 Luna (max). Checked September 23, 2026. ↩
15. [15]Artificial Analysis — Claude Fable 5.1 (max with fallback). Checked September 23, 2026. ↩
16. [16]Claude Opus 5.5 — max, default fallback. Checked September 23, 2026. ↩
17. [17]Artificial Analysis — Claude Sonnet 5 (max). Checked September 23, 2026. ↩
18. [18]Benchmarking methodology. Checked September 23, 2026. ↩
19. [19]Reasoning models. Checked September 23, 2026. ↩