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Opus used 11.7M tokens, Fable 5.3M, Astra 2.9M

Tokens used, helpers included. The dark tick is the figure each model reported at the end.

OpenAIAstra
2.9M, reported 0.7M
AnthropicFable
5.3M, reported 4.6M
AnthropicOpus
11.7M, reported 9.8M
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Fable 5.2 vs GPT-6 Astra vs Opus 5.5Japan's growth industries, by CAGR

Claude Fable 5.2 Medium from Anthropic and GPT-6 Astra Medium from OpenAI got the same Japanese research request on the same morning. Claude Opus 5.5 High from Anthropic got it at midday. All three used the same three-round research skill. This page compares how long each run took, how many tokens it used, and whether its cited pages back what it wrote. The answers are in Japanese. This page shows them as AI translations, and the original is one click away.

14 Nov 2026

Download all three answers (.md)
Our takeaways
  • Opus is too meticulous to do broad research.
  • Astra can still get a good overview.
  • Using the models in combination is still the best approach.
Prompt

The prompt used for all three models.

As sent, in Japanese

では、2026年から向こう3年で伸びる日本の業界を調べてきてほしい

CAGRだね
大きな大局を調べつつも、ニッチでも伸びている、みんなが見てないところで儲かっている業界があるのでそういうのも調べてきて
$d-loop-search x 3 go cheaper

English translation

Next, I'd like you to research the Japanese industries that will grow over the next three years from 2026.

Use CAGR.
Cover the big picture, but there are also industries that are growing in niches and making money where nobody is looking, so research those too.
$d-loop-search x 3 go cheaper

"$d-loop-search x 3" tells the model to run Dorado's research skill for up to three rounds, each round filling the gaps of the last. "go cheaper" asks it to keep token use down. Astra and Fable started at 10:15 on 14 November 2026. Each model planned the research itself, handed the searching to helpers on a cheaper model, checked its quotes against the saved pages, and wrote the answer in Japanese. Opus started at 12:05 the same day, with "/d-loop-search", the Claude Code form of the same command. Before researching, Opus found Fable's note on the same question, read its opening, and asked Minoru whether to research again. Minoru told it to start over.

The main result · research notes

The research note each run wrote.

The answer each run returned in chat summarizes the research note it wrote. The note holds, for each finding, the quoted source text and its link, plus the coverage status, the source audit, and a record of each round. All three notes can be read in full under Actual results below.

CheckOpenAIGPT-6 Astra MediumOpenAIAnthropicClaude Fable 5.2 MediumAnthropicAnthropicClaude Opus 5.5 HighAnthropic
Note length280 lines1,413 lines1,223 lines
Findings (with quote and source)11 (plus 3 pilot records)9878
Distinct links1415498
Websites cited65137
Where each quote sits (section path)RecordedNot recorded for anyRecorded for all
Coverage items6 items. States that no 2026 to 2029 ranking could be made6 items: 2 covered, 4 partly covered7 items: 3 covered, 4 partly covered
The model's own quote checkChecked the 11 against saved originals and recorded all as backedA script matched all 98 quotes to the saved pagesA script matched each round's quotes to the saved pages. 13 table rows were matched by the order of their figures
What the model's own audit fixedCorrected a mix-up of MonotaRO's standalone and consolidated figuresRecorded per round, for example F5 removed, G17 downgraded, and the growth rates of F4 and G5 removedRemoved 1 finding (R1-31) and dropped 1 figure that rested only on a search snippet
Dorado's sample check10 of 10 (5 verbatim sentences, 5 table figures)10 of 10 (all verbatim sentences)10 of 10 (8 verbatim sentences, 2 table figures)
Note languageEnglish (the opening conclusion in Japanese)Japanese (some headings in English)Japanese

Dorado picked 10 findings from each note at even intervals, opened each source, and checked that the quoted sentence or the table figures are on the page. Headings, AI comments, and calculated rates were not checked.

10 of 10sampled findings in Astra's note with the quote or figures on the page
10 of 10sampled findings in Fable's note with the quote or figures on the page
10 of 10sampled findings in Opus's note with the quote or figures on the page
0sampled findings with neither the quote nor the figures on the page (of 30)

Astra's note starts with a record written when its first helper stopped (marked "INCOMPLETE HANDOFF") and records from the pilot step, and the second helper's continuation follows. The final conclusion is at the top. In the sample, Astra's E08 gives p.11 for Nittoc's figures, but they are on page 14 of the PDF.

Measured · elapsed time

Which model finished first?

12:13Astra, start to finish. Handed over at 10:27.
16:23Fable, start to finish. Handed over at 10:31.
29:14Opus, start to finish. Handed over at 12:34.
2.4×Opus took 2.4 times as long as Astra
Astra finished first, and Opus took more than twice as long

Time from prompt to answer. Astra and Fable started at 10:15 JST, Opus at 12:05

Opus's time includes the 22 seconds it waited for Minoru's answer on whether to research again.

Measured · tokens

How many tokens did each run use?

2.9MAstra's total in the logs. It reported 0.7M
5.3MFable's total in the logs. It reported 4.6M
11.7MOpus's total in the logs. It reported 9.8M
4.0×Opus's tokens against Astra's. Fable's were 1.8 times

Fable planned 6 million tokens and reported 4.6 million at the end. The logs show 5.3 million. Astra set a cap of 12 million and reported 0.7 million, adding that its meter missed its helpers' calls. The logs show 2.9 million, and its two GPT-6.1 Sol helpers used 2.0 million of them. Opus planned 12 million and reported 9.8 million. The logs show 11.7 million.

Helpers used most of Astra's and Fable's tokens. Opus's main session used most of its own

Tokens by session, all three runs on the same scale

OpenAIAstra

2.94M

AnthropicFable

5.28M

AnthropicOpus

11.70M

Fable: main session (Fable 5.2) 2.08M, three Sonnet 5.5 helpers 0.91M, 1.07M, and 1.22M. Astra: main session (GPT-6 Astra) 0.95M, GPT-6.1 Sol helpers 0.49M and 1.49M. Astra's first helper stopped after 19 calls, so Astra gave the same task to a second one, which finished the three rounds. Opus: main session (Opus 5.5) 6.87M, three Sonnet 5.5 helpers 1.62M, 1.64M, and 1.57M. Opus's main session made 49 model requests. It prepared the page-fetching script, checked each round's quotes, and wrote the note itself.

About nine in ten tokens in all three runs were earlier context read again (Astra 91.0%, Fable 89.5%, Opus 94.2%). Astra wrote 17,583 output tokens, Fable 106,454, and Opus 164,526.

Measured · link check of the answers

Does each chat answer hold up as written?

After the runs, we opened every link cited in the answers and compared the page with the passage that cites it. A passage is backed when every figure, name, date, and condition in it is on the page. The sample check of the notes is in the research notes section.

9 of 10cited passages fully backed in Astra's answer
6 of 7cited passages fully backed in Fable's answer
5 of 6cited passages fully backed in Opus's answer
0passages contradicted by their page, or dead links, in any answer
Each answer has one passage that is only partly backed

One square is one cited passage, checked on 14 Nov 2026

OpenAIAstra

9 of 10 backed

AnthropicFable

6 of 7 backed

AnthropicOpus

5 of 6 backed

BackedPartly backedNot backed

Fable put 7 links on 7 passages, and Opus 6 links on 6 passages. Astra put 11 links on 10 passages: one sentence cites both the JES earnings release and the MonotaRO results presentation.

What the check caught

  • OpenAIAstra: a rate that does not match its source.It puts data center services at about 8.2% a year. Fuji Keizai's release gives a 60.0% rise from 2024 to 2030, which works out to about 8.1%. The release also does not say the figures cannot be added to cloud. That is Astra's own conclusion.
  • AnthropicFable: a label its source does not use.It calls MIDAC Holdings an industrial waste company. The cited page backs the ¥11.8 billion in sales and the 39.9% operating margin, but describes the business as waste disposal and recycling, without the term industrial waste.
  • AnthropicFable: a comment that drops a later year.Its comment under the SEAJ figure says the same release forecasts a decline for FPD equipment. That holds for FY2026 and FY2027. The release forecasts FPD equipment up 27% in FY2028.
  • AnthropicOpus: the same label as Fable, not in its source.It calls MIDAC Holdings an industrial waste company. The cited earnings release backs net sales of ¥11,844 million, operating profit of ¥4,723 million, and the 39.9% operating margin, but does not contain the word waste. Opus cited the earnings release rather than an aggregator site, and the label problem stayed the same.
  • AnthropicOpus: "without reading the previous note" overstates it.The answer opens with this, but before researching, Opus read the opening of Fable's note (its summary of the answer), its coverage table, and its list of gaps. It told its helpers not to open the note.
  • OpenAIAnthropicAnthropicAll three: token reports that were too low.Fable reported 4.6 million tokens, Astra 0.7 million, and Opus 9.8 million. The logs show 5.3 million, 2.9 million, and 11.7 million.
Compared · what each answer found

Where the three answers agree, and where they part.

TopicFableAstraOpus
Battery storage stations (the same Yano release)45.3% a year from FY2024 to FY2030, calculated. Notes that the price cap on its revenue source is expected to fall by more than halfAbout 45.3% a year from FY2024 to FY2030, calculated. Notes the same price cap41.4% a year from FY2025 to FY2030, calculated. Does not mention the price cap
Funeral services (Yano, 2025→2034)0.2% a year, calculatedAbout 0.17% a year, calculatedNot in the answer
AIDomestic AI spending, 36.0% a year (IDC)Installation, operation, and maintenance of AI infrastructure, 19.4% a year (IDC)Domestic AI systems market, 25.6% a year (IDC, 2024→2029)
CloudCloud infrastructure, 18.0% a year (Yano)Public cloud, 17.6% a year (IDC)Named as a growth field, with no figure
Niches that make moneySingle-company operating margins: MIDAC 39.9%, Sho-Bond 23.4%, Nihon M&A Center 37.3% (aggregator site)JES (elevator maintenance) 19.1%, MonotaRO 14.7% (calculated from earnings documents)Calculated from earnings releases: OBIC 65.7%, MIDAC 39.9%, Nihon M&A Center 37.3%, Daiseki 20.3%, JES 19.1%. Compared with 4.0% for all industries (MOF corporate statistics)
Other nichesCall center AI, 31.7% a year, plus a table of growth rates for 11 more industriesHalal testing, about 21.7% a year; data center services, about 8.2% a yearSpot work brokerage up 22.5% in one year (Yano). Timee's operating margin 18.1% (six months)
Evidence against growthHome-care provider bankruptcies at a record for the third year (Tokyo Shoko Research)An aging population is not enough: the funeral market is almost flatRobot orders up 25.7%, but domestic shipments down 10.8% (Japan Robot Association)
The 2026 to 2029 windowLists forecasts with different periods and labels each periodStates that it found no CAGR aligned to 2026 to 2029States that IDC's rate covers 2024 to 2029, not 2026 to 2029

All three found the same Yano release on battery storage stations, but Opus calculated from a different base year (FY2025). Fable lists 18 growth rates. Astra narrows to 5 growth markets and 2 companies with actual profits. Opus spends 2 of its 6 findings on company and all-industry margins, and takes the margins from earnings releases.

Measured · tokens per unit of output

What did one unit of research cost?

Each panel has its own scale. The first two count the research notes, and Fable used the fewest tokens. The other four count the chat answers. There Astra used the fewest, and the gap is widest per fully backed passage.

Tokens per finding in the note

OpenAIAstra
267K
AnthropicFable
54K
AnthropicOpus
150K

Against Astra: Fable 0.2 times, Opus 0.6 times

Tokens per distinct link in the note

OpenAIAstra
210K
AnthropicFable
34K
AnthropicOpus
119K

Against Astra: Fable 0.2 times, Opus 0.6 times

Tokens per character of the answer

OpenAIAstra
1,283
AnthropicFable
1,832
AnthropicOpus
4,608

Against Astra: Fable 1.4 times, Opus 3.6 times

Tokens per cited link

OpenAIAstra
367K
AnthropicFable
755K
AnthropicOpus
1,949K

Against Astra: Fable 2.1 times, Opus 5.3 times

Tokens per fully backed passage

OpenAIAstra
326K
AnthropicFable
881K
AnthropicOpus
2,339K

Against Astra: Fable 2.7 times, Opus 7.2 times

Tokens per minute

OpenAIAstra
240K
AnthropicFable
323K
AnthropicOpus
400K

Against Astra: Fable 1.3 times, Opus 1.7 times

What the extra tokens bought

Fable's research note holds 98 quotes from three rounds of searching, and a script matched each one to its saved page. Opus's note holds 78 findings, records where on each page a quote sits (its section path), and takes margins from 10 companies' earnings releases. Astra's note holds 11 findings. The answers themselves are close in length: 2,884 characters for Fable, 2,538 for Opus, and 2,289 for Astra.

All three answers

All three answers, with their numbers.

CheckOpenAIGPT-6 Astra MediumOpenAIAnthropicClaude Fable 5.2 MediumAnthropicAnthropicClaude Opus 5.5 HighAnthropic
Tokens used (logs)2,936,4585,284,70511,696,249
Tokens the model reported0.7M (helpers not counted)4.6M9.8M
Token plan or cap12M cap6M plan12M plan
Elapsed time12 min 13 s16 min 23 s29 min 14 s
Helpers2 GPT-6.1 Sol threads (the first stopped after 19 calls)3 Sonnet 5.5 agents3 Sonnet 5.5 agents
Answer length2,289 characters2,884 characters2,538 characters
Cited links (distinct)876
Websites cited555
Cited passages checked1076
Fully backed965
Partly backed111
Not backed or dead000
Research note11 findings (plus 3 pilot records)98 quotes78 findings

Answer length is the character count of the Japanese original, without link addresses.

All three outputs

Actual results

The research note and the final answer from each run, as produced. Switch the model, and switch between the note and the answer, to compare them. Links open the cited pages in a new tab.

AI translation of the Japanese original by Opus 5.5. Figures, links, and structure follow the original, which is the record. Read the original in Japanese

Research notes are shown as written, untranslated: Fable's and Opus's in Japanese, Astra's in English.

  • Looking at both market size and growth rate, domestic cloud and the installation, operation, and maintenance of AI infrastructure are strong candidates. This is my judgment based on the primary sources below.
  • Among niches, watch halal testing and elevator maintenance. The first rests on a market growth forecast and the second on a company's actual profits. I did not confirm both "high growth and high profit" for either.
  • Battery storage station operation grows fast, but profit forecasts need caution. The price rules of electricity trading determine revenue.
  • I could not find CAGRs aligned to 2026 to 2029. The figures below are forecasts for other periods that include those three years. Figures for different periods cannot be turned directly into a ranking for the next three years.

Five fields to look at as growth markets

FieldForecast CAGRForecast periodMarket size and judgment
Battery storage station operationAbout 45.3%, calculatedFY2024 to FY2030¥45.0 billion→¥424.0 billion. Operator revenue from electricity trading, not battery manufacturing. Watch the price rules. Yano Research Institute, 2026-01-20
Halal testingAbout 21.7%, calculated2025 to 2035Forecast to grow 7.1 times. Still a small market of ¥1.0 billion in 2035. Fuji Keizai, 2026-05-14
Installation, operation, and maintenance of AI infrastructure19.4%2025 to 2030¥95.7 billion→¥232.0 billion. Worth watching as demand for professional services. IDC, 2026-04-08
Public cloud17.6%2025 to 2030¥4.4930 trillion→¥10.0962 trillion. Combines size and growth rate. IDC, 2026-03-12
Data center servicesAbout 8.2%, calculated2024 to 2030¥6.8189 trillion in 2030. Its market definition overlaps with cloud, so the two cannot be added together. Fuji Keizai, 2026-05-11

Calculated values were computed from the published multiple or the start and end points, as CAGR = (end ÷ start)^(1 ÷ years) − 1.

AI-adjacent services are especially worth digging into. IDC explains that equipment growing more complex, including liquid cooling, increases demand for professional services. My reading is to look not only at investment in the equipment itself but for companies that provide operation and maintenance after installation. However, this research did not confirm those providers' profit margins. IDC "Expansion of the AI Infrastructure Services Market and Changes in Competitive Dynamics", 2026-04-08

Halal testing is a candidate to look at as an added service for food testing companies. The original reads "● Halal testing ¥1.0 billion (7.1 times)". It covers the "2035 market forecast (compared with 2025)". The AI's judgment is that this is less a market for building a large standalone business than one for companies that can use their existing testing equipment and customer base. Testing alone cannot confirm every condition of halal certification. Fuji Keizai, 2026-05-14

Battery storage station operation cannot be chosen on growth rate alone. Yano's January 2026 report states that "the price cap in the balancing market is expected to be changed to less than half of the current level from FY2026". This is the outlook as of that report, and I have not treated it as the current, confirmed rule. Market growth and the profitability of each facility need to be checked separately. Yano Research Institute "2. Featured topics", 2026-01-20

Examples that confirm "actually making money"

Field and companyResultsOperating marginHow to read it
Elevator maintenance and modernization: JESConsolidated, fiscal year ended March 2026. Net sales ¥57.6 billion, operating profit ¥11.0 billionAbout 19.1%Net sales up 16.7% from the previous year, operating profit up 27.7%. A company example that confirms growth and profit. Not an industry-wide CAGR.
Procurement of supplies for factories and offices: MonotaRONon-consolidated, FY2025. Net sales ¥322.8 billion, operating profit ¥47.4 billionAbout 14.7%An example of profit in a business that supports procurement of consumables and similar items. Non-consolidated results, not an industry average.

Sources: JES "Consolidated results for the fiscal year ended March 2026" earnings release, 2026-05-14, and MonotaRO's FY2025 results in "2026 business plan, non-consolidated plan" results presentation, 2026-05-07. Margins were calculated as operating profit ÷ net sales.

If it were up to me, I would make equipment services with ongoing contracts, close to elevator maintenance and modernization, the next niche to research. JES's results are grounds for researching in that direction. However, they do not show that other maintenance businesses have the same margin.

Cautions when looking at the big picture

Higher demand from an aging population is not enough to pick high-growth industries. The funeral market is forecast to go from ¥1.8633 trillion in 2025 to ¥1.8915 trillion in 2034, a calculated CAGR of about 0.17%. It does not support the judgment that "the funeral industry as a whole will grow a lot because more people are dying". Yano Research Institute "3. Outlook", accessed 2026-11-14

From these sources, it is useful to look separately at growth in AI and cloud spending, businesses that balance electricity supply and demand, maintenance that keeps equipment in use longer, and specialized testing for specific food uses. On the other hand, how much attention a market gets ("nobody is watching") and high profits across whole niche industries remain unconfirmed.

The research ended after 3 rounds.

  • Round 1: checked 12 fields, separating domestic markets from overseas sales and CAGR from year-on-year change.
  • Round 2: strengthened the evidence on cloud, AI-adjacent services, and food testing.
  • Round 3: checked companies' actual profits and the limits set by prices and costs.

Unresolved: CAGRs for a common 2026 to 2029 period, checks of the original sources for AI software and cybersecurity, and an independent macro forecast. Source excerpts and the audit record are saved in the research note.

The meter showed 0.7M tokens of a 12M budget, 10 steps, and about 12 minutes elapsed. However, it did not capture every call, including those by delegates, so total usage cannot be determined.

The research note link at the end of each answer opens that note's download. One malformed link in Opus's note, which has no target, is shown as plain text.

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