Opus leads the aggregate benchmark. Astra makes a stronger cost case than its token prices suggest. Sol and Luna change what can be deployed at scale. Fable now has to defend its premium on the work that actually matters.
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By Thorsten Meyer | 23 September 2026 | Reality Check
The most revealing comparison in this group begins with two models carrying the same price tag.
Claude Fable 5.1 and GPT-6 Astra both list standard API prices of $10 per million input tokens and $50 per million output tokens. At maximum effort, both display a rounded score of 53 on the Artificial Analysis Intelligence Index. Yet their weighted benchmark costs are $7.63 and $3.26 per task respectively. [1, 3]
Same input and output rates. Same displayed aggregate score. A substantially different bill.
That is the problem with shopping for AI by model name—or by a token-price table alone.
Across Claude Fable 5.1, Claude Opus 5.5, GPT-6 Astra, GPT-6 Sol and GPT-6 Luna, the useful decision has three parts: what the task requires, how much reasoning to allocate and how much work remains after the model finishes.
My conclusion is that most organizations should evaluate a small set of models with distinct jobs. Paying for the strongest configuration on every request is difficult to defend. Sending everything to the cheapest one requires an equally demanding justification.
ThorstenMeyerAI.com / Reality Check
Five models.
Which one earns its cost?
Compare capability, effort and the cost of usable work.
Claude Fable 5.1 · Claude Opus 5.5 · GPT-6 Astra · GPT-6 Sol · GPT-6 Luna
01 Model choice and effort belong together
Anthropic entries include default fallback. Effort labels do not standardize compute across vendors.
| Model | Max effort | Medium effort | Input / output per 1M tokens | ||
|---|---|---|---|---|---|
| Score | Cost / task | Score | Cost / task | ||
| Fable 5.1 | 53 | $7.63 | 49 | $2.98 | $10 / $50 |
| Opus 5.5 | 58 | $5.98 | 51 | $1.34 | $4 / $20 |
| GPT-6 Astra | 53 | $3.26 | 50 | $1.54 | $10 / $50 |
| GPT-6 Sol | 48 | $1.06 | 40 | $0.25 | $2 / $10 |
| GPT-6 Luna | 37 | $0.07 | 29 | $0.02 | $0.10 / $0.50 |
Scores are not success percentages. Benchmark costs are not production quotes or costs per accepted result. Token rates exclude caching discounts and other charges.
02 A shortlist to test on your work
Editorial evaluation proposals—not benchmark-certified specialties.
Constrained, high-volume tasks
Start with LunaTest extraction, classification and transformations against inexpensive, explicit checks.
Recurring development and operations
Trial SolMeasure completion quality and escalation frequency on routine work.
Demanding professional workflows
Compare Opus + AstraTest deliverables, tool execution and review time. Include medium effort before defaulting to max.
Where Fable fits: keep it where a demonstrated task advantage or an established workflow justifies its premium. Require a replacement to earn the switch.
Measure cost per accepted result
Model + tools + review + rework spendingdivided by accepted results. Keep completion time and error severity alongside it.
Sources: Artificial Analysis model pages linked in the table; effort-setting pages below. Figures checked 23 September 2026. The 57% comparison is calculated as 1 − $3.26 / $7.63, rounded. Values may change.
Effort-setting sources and editorial context
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First, compare the same stated effort setting
“Fable” means Claude Fable 5.1 throughout this article. “Astra,” “Sol” and “Luna” refer to the GPT-6 releases.
The table below uses maximum effort for all five. That gives us a clearly specified starting point; it does not mean that “max” represents an identical amount of computation across vendors.
Both Anthropic entries were evaluated with default fallback enabled. All values are a snapshot of Artificial Analysis’s model pages on 23 September 2026. [1–5]
Model at max effort Intelligence Index Weighted cost per index task Input / output per 1M tokens Claude Fable 5.1, with fallback 53 $7.63 $10 / $50 Claude Opus 5.5, with fallback 58 $5.98 $4 / $20 GPT-6 Astra 53 $3.26 $10 / $50 GPT-6 Sol 48 $1.06 $2 / $10 GPT-6 Luna 37 $0.07 $0.10 / $0.50
USD. Index scores are not success percentages. Task costs describe a weighted evaluation mix, not a quote for completing a business task. Token prices are standard listed rates, not subscription fees.
Three different purchasing arguments emerge.
Opus offers the highest aggregate score. Astra reaches Fable’s displayed score at approximately 57% lower benchmark cost. Sol and Luna offer progressively lower aggregate capability at much lower spending levels. Equal rounded scores do not establish identical abilities. [6–10]
This is enough to identify candidates for testing. It is insufficient to decide who should write a particular report, maintain a particular repository or operate a particular application.
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Opus 5.5: the strongest starting case for complex knowledge work
Opus 5.5 has the clearest aggregate performance argument. Artificial Analysis’s launch assessment reports leading scores on six of the ten Intelligence Index evaluations and particular strength in agentic knowledge work. On AA-Briefcase, it leads in analytical quality and presentation, while sitting slightly behind Fable 5.1 on rubric-based scoring. [11]
That last distinction deserves attention. A deliverable can be persuasive and well presented while still missing a requirement. Organizations buying document production, analysis or research assistance should grade all three: the reasoning, the presentation and completeness against the brief.
My practical inference is to put Opus on the first shortlist for demanding work that has to arrive as a usable artifact. It should compete on how much checking and reconstruction the recipient needs to do.
Its position does not justify selecting maximum effort automatically. Medium and high deserve their own trials, particularly when a task already has a clear structure and supplied evidence.
cost-effective AI text generation tools
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Astra: the premium alternative with a different task-cost profile
Astra’s $10/$50 token rates make it look expensive beside Opus’s $4/$20. At max effort, however, the benchmark task bill is lower: $3.26 versus $5.98, alongside a lower index score of 53 versus 58. [2, 3]
The relationship is straightforward: the price of each token is only one component of the cost. How many tokens the evaluated workflow consumes, and how they are billed, also matters.
OpenAI emphasizes Astra’s computer and browser use, software engineering and scientific capabilities. Those are vendor claims that help define what to test, rather than a neutral five-model ranking. [13]
For application-heavy work, I would therefore compare Astra directly with Opus in the actual environment: the same files, permissions, tools and acceptance criteria. A model that reasons well about a task can still lose time navigating the software needed to complete it.
This is also where the surrounding product matters. The model and the application that gives it tools are separate parts of the result. A benchmark lead does not automatically transfer to every agent interface.
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Fable 5.1: retain it where the evidence earns the premium
Fable now faces a demanding comparison. In the max-effort snapshot, Opus has a higher aggregate score at lower cost; Astra matches its displayed score at lower cost. [1–3]
That weakens the case for choosing Fable by reputation alone. It does not prove that existing Fable workflows should be replaced immediately.
A business may have prompts, integrations and review procedures that perform reliably with it. The relevant question is whether the alternative improves those results after migration and validation costs are counted. If Fable consistently handles a difficult task better, the aggregate ranking does not invalidate that finding.
There is also a configuration trap. Anthropic says Fable 5.1 defaults to high effort in Claude Code and medium in Claude Cowork and Claude.ai. Its launch description emphasizes coding, knowledge work and long-running tasks. [14]
A comparison that leaves each product on its default settings may be a useful product test. It should not be presented as a controlled model comparison.
Fable’s place in a new deployment should be earned through a specific advantage. In an established deployment, a replacement should earn the switch.
Sol: the middle tier deserves a serious trial
Sol sits in a different economic category from the three premium contenders: 48 on the max-effort index at $1.06 per benchmark task. [4]
That makes it a plausible candidate for work where the premium models’ additional capability does not reduce enough errors or review time to justify their cost. The question is how often the task needs capabilities that Sol does not deliver reliably.
There is independent evidence worth considering for coding. Artificial Analysis reports that GPT-6 Sol improves two points over GPT-5.6 Sol in its Coding Agent Index, reaching 57 at max effort, at roughly half the cost per task. It also reports regressions in knowledge-work evaluations for the new Sol and Luna releases. [12]
That is a reason to test by workload rather than promote a single model across an organization. A good coding result does not establish equally strong document production. An acceptable first draft does not establish reliable tool execution.
My starting use for Sol would be a broad trial on recurring development and operational work, with clear escalation when tests fail or the brief remains incomplete. Its business case is enough capability at a manageable cost, measured on actual deliverables.
Luna: low prices change the volume equation
Luna’s max-effort benchmark cost is $0.07, with an index score of 37. Its standard input and output token rates are one-hundredth of Fable’s and Astra’s. [1, 3, 5]
The sensible inference is to test it on high-volume, constrained work where acceptance can be checked economically: extracting fields, assigning categories, transforming supplied text or preparing material for another stage.
Those are evaluation candidates, not claims that Luna is validated for every task in those categories. An error in extraction can be costly if nobody notices it. A low generation price becomes useful when the workflow can identify and handle failures.
Cheap inference can also make an extra processing stage affordable. But an additional model call should serve a defined purpose. A second answer is not automatically a verification, and two models can agree on the same mistake.
The design question is which checks connect the answer back to evidence: a schema, a calculation, a source passage or a test.
Medium effort changes the shortlist
Max-effort comparisons tell only part of the story. The same five models at medium effort produce a different set of trade-offs. [6–10]
Model at medium effort Intelligence Index Weighted cost per index task Claude Fable 5.1, with fallback 49 $2.98 Claude Opus 5.5, with fallback 51 $1.34 GPT-6 Astra 50 $1.54 GPT-6 Sol 40 $0.25 GPT-6 Luna 29 $0.02
At medium, Opus scores one point above Astra at a slightly lower benchmark cost. This does not settle their suitability for a particular task; it makes them a natural pair to compare before spending on their highest settings. [7, 8]
Fable’s xhigh and max settings both display 53, while their costs are $5.98 and $7.63. The rounded score does not rule out differences on individual evaluations, but it gives no obvious aggregate reason to select max automatically. [6]
Luna at xhigh and Sol at low both display 34, at $0.04 and $0.13 respectively. Again, equal index scores do not mean interchangeable behavior. The result shows why the choice should include both model family and reasoning effort. [9, 10]
A useful evaluation can compare more reasoning on a cheaper model against less reasoning on a stronger one. Keeping the model fixed while adjusting effort is only half of that experiment.
Caching, context and speed can change the result
Caching is particularly easy to misread. The listed cache-read discounts are 98% for Fable, 95% for Opus and 90% for Astra, Sol and Luna. Applied to their respective input rates, those imply cache-read prices of $0.20, $0.20, $1.00, $0.20 and $0.01 per million tokens. [1–5]
A larger percentage discount does not necessarily mean a lower absolute price. Nor does a cache-read price include cache creation, new input, output or other workflow costs.
Context limits also differ. Artificial Analysis lists one million tokens for Fable, Opus, Astra and Luna, and 872,000 for Sol. Those limits describe capacity, not a promise of accurate reasoning over every token supplied. [6–10]
For speed, measure elapsed time to an accepted result. Streaming tokens per second excludes some of what a user experiences, including time before the answer starts and time spent using tools. A model can generate tokens quickly yet spend longer completing the task.
The practical experiment should therefore run from the original request through review and acceptance. A faster answer that needs another attempt has not necessarily made the workflow faster.
The buying decision is a workload decision
For a new deployment, my initial shortlist would be:
- Luna for constrained, high-volume tasks with inexpensive checks.
- Sol for recurring development and operational work that needs more capability.
- Opus 5.5 for complex professional deliverables, with medium and high tested first.
- Astra alongside Opus for demanding workflows, especially those involving software and browser interaction.
- Fable 5.1 where a specific task advantage or established workflow justifies keeping it.
These are proposed evaluation roles, not five benchmark-certified specialties.
The organization should decide what counts as success before seeing the outputs. Then measure acceptance, correction time, retries and total spending. Include unsuccessful attempts. Keep material mistakes visible instead of averaging them into an attractive overall score.
Fallback behavior belongs in that record too. The Anthropic benchmark entries explicitly include default fallback, so the reported result describes that evaluated configuration. Product settings, safeguards and tool access can change what happens in a real deployment. [6, 7]
The measure I would put on the management dashboard is:
Cost per accepted result = model, tool, review and rework spending divided by accepted results.
Keep completion time and error severity alongside it. A low average cost cannot compensate for failures the business cannot tolerate.
The strategic advantage is knowing when additional capability changes the outcome. Some tasks warrant the strongest available reasoning. Others need a clear brief, a modest model and a dependable check.
Opus, Astra, Fable, Sol and Luna make those choices visible. The next productivity gain depends on making them deliberately.
Sources and comparison method
Sources checked on 23 September 2026. Main tables use Artificial Analysis Intelligence Index v4.3.2 figures and displayed weighted cost per task. Costs retain source precision; percentage comparisons are calculated from rounded values. Effort labels are reported settings, not standardized compute budgets across vendors. Both Anthropic families use default fallback in the cited evaluations. All five accept text and images and produce text according to the cited model pages; this article compares those model configurations, not every feature of their consumer applications.
This is a source-based comparison, not a hands-on five-model test. Workload recommendations and economic interpretation are the author’s analysis. Rankings, prices and product defaults may change.
- Artificial Analysis — Claude Fable 5.1, max with fallback
- Artificial Analysis — Claude Opus 5.5, max with fallback
- Artificial Analysis — GPT-6 Astra, max
- Artificial Analysis — GPT-6 Sol, max
- Artificial Analysis — GPT-6 Luna, max
- Artificial Analysis — Fable 5.1 effort settings
- Artificial Analysis — Opus 5.5 effort settings
- Artificial Analysis — Astra effort settings
- Artificial Analysis — Sol effort settings
- Artificial Analysis — Luna effort settings
- Artificial Analysis — Opus 5.5 launch assessment
- Artificial Analysis — Sol and Luna launch assessment
- OpenAI — GPT-6 Astra
- Anthropic — Claude Fable 5.1 and Mythos 5.1
This article was created with the assistance of artificial intelligence.
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