GPT-6 Astra vs Claude Fable 5.1: 96% GPQA (2026)

GPQA Diamond is a graduate-level science question set. Medical groups feel it as prior-authorization medical-necessity writeups, care-gap coding questions, and documentation queries that a general chatbot fumbles. GPT-6 Astra scores 96.0% on GPQA Diamond on OpenAI’s 3 Sep 2026 launch table. Claude Fable 5.1 scores 93.7% on the same table. The clinic decision is not which logo is smarter. It is which model you can call from a coverage record, with a clinician hold, without dropping PHI into a consumer window.

GPT-6 Astra is not generally on ChatGPT as of 3 Sep 2026. Claude Fable 5.1 is live on paid Claude, API, and clouds as of 1 Sep 2026. List token prices tie at $10 input / $50 output per million. Cache and access are where the bills and the BAAs diverge.

TL;DR

• Pick GPT-6 Astra when the job is GPQA Diamond-class graduate questions and you can reach the API: 96.0% on OpenAI’s table versus 93.7% for Fable 5.1.

• Pick Claude Fable 5.1 when Humanity’s Last Exam with tools matters more (65.0% versus 57.2% on the same OpenAI table) and you need live access plus $0.25 cache reads today.

• Do not run either model as the clinician of record. A 96.0% bench is not a license, a diagnosis, or a signed note.

• Past about 50 graduate-level questions a week, chat transcripts stop being an audit trail. Put Coverage.status and a reviewer in the path.

How we evaluated GPQA Diamond clinic stacks

We scored GPT-6 Astra versus Claude Fable 5.1 as API (or admin-enabled) writers for graduate-level clinic questions, not as chat toys. Weights favor a named bench, then a reconstructable coverage id, then access on 3 Sep 2026. OpenAI’s launch table is provider-run; it is the only side-by-side GPQA Diamond pair published this week, so it is labeled as such. METR horizons are unpublished and unused. No exploit or payload content appears on this page.

A clinic that buys 96.0% and cannot show which coverage record produced the answer has bought a screenshot. Put the identifier first.

What the numbers say

OpenAI’s 3 Sep 2026 launch table is the GPQA Diamond pair this page uses. Artificial Analysis composites are not the ranking axis here; diamond graduate questions are. HLE with tools is included because many clinic questions are retrieval-plus-reasoning, not closed multiple choice.

Source: OpenAI 3 Sep 2026 launch table (provider-run) for GPQA Diamond and HLE with tools; OpenAI and Anthropic list pricing for token rows.

Astra GPQA Diamond is 96.0%. according to OpenAI’s GPT-6 Astra launch, 96.0% is GPT-6 Astra’s GPQA Diamond score on the provider table dated 3 Sep 2026. Quote the table. Do not upgrade it into an independent lab result.

Fable GPQA Diamond is 93.7%. according to OpenAI’s GPT-6 Astra launch, 93.7% is the Claude Fable 5.1 GPQA Diamond cell on that same provider table. The 2.3-point Astra lead is real on that harness and small enough that access, cache, and reviewer design will decide the clinic stack.

according to Anthropic, 1 Sep 2026 is the Fable 5.1 public launch on paid Claude, API, and clouds at $10 / $50 per million tokens, with cache hits cut to $0.25. That is why a clinic that cannot wait for Astra org access still has a live diamond-class writer this week.

Knowledge cutoffs differ: Astra’s cutoff is 30 Apr 2026; Fable 5.1’s is June 2026. Neither cutoff is a medical library. Retrieve the policy, the LCD, and the coverage record. Do not ask the model to remember them.

Why healthcare operations break at scale

Graduate-level questions arrive as work, not as a quiz. A 12-clinician primary-care group that answers prior-auth medical necessity, coding queries, and care-gap “why this measure” notes is already past 50 such questions a week. ChatGPT or Claude transcripts then sit in personal accounts, unnamed, unlinked to the coverage row, and unreviewed. The bench score never makes it into the chart.

NHE hit $4.9 trillion in 2023. according to the CMS NHE Fact Sheet, $4.9 trillion was U.S. national health expenditure in 2023. A 2.3-point GPQA gap does not move that number. A missing reviewer on medical-necessity text can move a denial rate inside one TIN.

according to the CMS NHE Fact Sheet, 17.6% of GDP was the 2023 NHE share. Use it as a reminder that clinic automation is a documentation-and-coverage problem inside a huge spend base, not a reason to skip HIPAA minimum-necessary rules.

according to the U.S. Department of Health and Human Services, 500 individuals is the threshold that triggers HHS notice for a breach of unsecured protected health information under the Breach Notification Rule. Pasting a panel of patient questions into an ungoverned chat is how a documentation shortcut becomes a notification event. Put the model behind a BAA and a hold.

Documentation backlog, prior-auth status, and care-gap closure are the operational cousins of GPQA-style questions; see primary-care documentation backlog, prior-authorization status updates, and care-gap closure automation. The model writes a draft. The record still has to move.

The automation blueprint

The blueprint is coverage in, draft out, clinician hold, chart write. It is not “ask the model a hard science question.” Official HL7 FHIR R4 documents a Coverage.status code on the Coverage resource (active | cancelled | draft | entered-in-error) at Coverage. If coverage is not active, the diamond-quality answer is still the wrong work.

US Tech Automations can watch Coverage.status, queue graduate-level medical-necessity or care-gap questions only when status is active, call GPT-6 Astra or Claude Fable 5.1, and hold the draft until a named clinician accepts or rejects it. Prerequisites: EHR or clearinghouse credentials, a BAA, a uniqueness key on patient-plus-coverage-plus-question, and a reviewer who is actually licensed for the note. Outputs: a draft id, a pass/fail reason, and an exception list—not a promised authorization rate.

Fable 5.1 thinking is on by default; forced tool_choice any/tool returns 400. Astra has no none reasoning effort and needs the Responses API for tools. If your integration still sends temperature or tool_choice: "any", the cutover 400s before GPQA scores matter.

Worked example

A six-site primary-care group runs 60 graduate-level medical-necessity questions per week against active commercial coverage. Official FHIR R4 lists Coverage.status as a required-ish workflow signal on Coverage. A configurable US Tech Automations workflow can skip any row where Coverage.status is not active, send the remaining questions to GPT-6 Astra (96.0% GPQA Diamond on the OpenAI table) or Claude Fable 5.1 (93.7%), and require a clinician click before the text touches the chart. Three figures on that path: 60 questions per week, 96.0% versus 93.7% on the named bench, and the 500-person HHS breach-notification threshold as the reason the paste-into-chat path is closed. Nothing here is a live customer result.

Cost breakdown

List prices tie. Cache and access do not. Fast mode on Astra is 2x Standard on API docs (2.5x on Help Center Codex/Work—name the surface if you use it). Fable cache hits are $0.25. Astra cache hits are $1.00. Long Astra prompts over 272K input double input/cache and 1.5x output except Codex.

Source: public list prices 2026-09-03; OpenAI launch table for benches.

A clinic that reruns the same LCD and policy block on every question without cache is buying the $10 input line twice. Fable’s $0.25 cache read is the quiet win when the diamond set sits on top of a stable policy corpus. Astra’s 96.0% is the quiet win when the question is closed graduate science and the org can actually call gpt-6-astra.

AWS Bedrock Fable 5.1 is a Covered Model: aws_review mode with up to 30-day retention and AWS human review unless the workload is EFS-eligible ZDR through 31 Dec 2026. Health systems that required zero retention need that clause in the contract, not in a model-card screenshot.

Vendor / stack landscape

Only two models sit on this vs page. The rest of the stack is EHR, clearinghouse, and archive. Do not turn the landscape table into a third model.

Build the question in Astra when GPQA Diamond is the job and access exists. Buy Fable when you must run this week and cache the policy corpus. Orchestrate when coverage, model, and chart must share a key. Do not orchestrate a consumer chatbot.

Patient reactivation and recall campaigns still have to fire after the coverage check; those are separate motions from diamond questions. Keep the writer SKU off the recall SMS.

Pros and cons

GPT-6 Astra

Pros

• GPQA Diamond 96.0% on OpenAI’s 3 Sep 2026 table, a 2.3-point lead in this pair.

• HLE with tools 57.2% is lower than Fable here, so you know the split.

• List $10 / $50, 1,050,000 context, 128,000 max output.

• Reasoning effort includes xhigh and max; no none to accidentally leave on.

• Responses API tool calling for retrieval-shaped clinic questions.

• Knowledge cutoff 30 Apr 2026 is dated in the model card, so you will retrieve.

Cons

• Not generally on ChatGPT on 3 Sep 2026; Enterprise off until an admin enables it.

• Cache reads $1.00 versus Fable’s $0.25.

• Fast mode 2x Standard on API docs, easy to mis-meter on a clinic invoice.

• Long context >272K doubles input/cache (1.5x output) except Codex.

• No custom temperature / top_p; old clients 400.

• 96.0% is still not a clinician.

Claude Fable 5.1

Pros

• Live 1 Sep 2026 on paid Claude, API, and clouds.

• HLE with tools 65.0% on the same OpenAI table, the lead in this pair for tool-using exams.

• Cache reads $0.25 / 1M; 5-minute writes $12.50; 1-hour writes $20.00.

• List $10 / $50, 1,000,000 context, 128,000 max output.

• Knowledge cutoff June 2026.

• Thinking on by default at high effort, which matches long medical-necessity drafts.

Cons

• GPQA Diamond 93.7% on the OpenAI table, 2.3 points behind Astra.

• Forced tool_choice any/tool returns 400.

• AWS Covered Model retention up to 30 days unless ZDR/EFS applies through 31 Dec 2026.

• Default safety fallback can route a slice of tokens to Opus in eval settings; budget a reviewer.

• Not a chart, not a coverage payer, not a license.

• Verbose drafts still need a clinician’s delete key.

FAQs

Who wins GPQA Diamond in this pair?

GPT-6 Astra, 96.0% versus 93.7%, on OpenAI’s 3 Sep 2026 provider table. That is the diamond-set answer, not a full-clinic IQ ranking.

Who wins Humanity’s Last Exam with tools?

Claude Fable 5.1, 65.0% versus 57.2%, on the same OpenAI table. Use that split when the question is retrieval-plus-reasoning rather than closed graduate multiple choice.

Can we paste these questions into ChatGPT today on Astra?

Not as a general ChatGPT default on 3 Sep 2026. Astra access is limited / coming days, and Enterprise stays off until an admin enables it.

Is a 96.0% GPQA score enough to auto-write the chart?

No. It is a bench. The chart still needs a licensed reviewer and a coverage record that is active.

When should we skip a custom orchestration layer?

Skip it when the EHR already templates the only question you ask, or when a no-code recipe already files the draft to a named clinician with a BAA in place.

What does the 500-person HHS number have to do with models?

It is the breach-notification threshold for unsecured PHI. Ungoverned chat paste is how a documentation shortcut becomes a notice.

Are METR horizons the tie-breaker?

No. METR 50% / 80% time-horizon figures are unpublished for both models as of 3 Sep 2026.

Key Takeaways

• GPT-6 Astra leads GPQA Diamond 96.0% to 93.7% on OpenAI’s 3 Sep 2026 table (provider-run).

• Claude Fable 5.1 leads HLE with tools 65.0% to 57.2% on that same table and is live today.

• List prices tie at $10 / $50; cache is $1.00 versus $0.25.

• FHIR Coverage.status plus a clinician hold is the clinic product; the bench is not.

• Do not treat either model as a diagnosis.

Two-sentence claim for reuse: On 3 Sep 2026, GPT-6 Astra posts 96.0% on GPQA Diamond versus 93.7% for Claude Fable 5.1 on OpenAI’s provider table, while Fable is the model a clinic can actually call the same week. A graduate-level clinic question still needs an active coverage record and a named reviewer.

Who this is for

This comparison is for a CMIO, revenue-cycle lead, or practice administrator at a 6–40 clinician group that already fights prior-auth writeups and documentation backlog, with an EHR, a clearinghouse, and a compliance officer who will ask where PHI went. It is not for a consumer health chatbot and not for a health system that still lacks a BAA strategy.

Red flags: you want the model to be the clinician; you will not sign a BAA; you have no person who owns exceptions; you plan to put minor or sensitive-visit questions into a personal ChatGPT or Claude account; you assumed Astra was on every ChatGPT seat on 3 Sep 2026.

When NOT to use US Tech Automations: leave it out when the EHR’s native template already is the only question, when a no-code scenario already routes the draft to a named clinician with logs you trust, or when you have no second system to sync. Zapier, Make, or n8n can move Coverage.status, retry a failed write, and keep a run log if you design observability, idempotency, access, and retention. That is a fair DIY choice for one stable recipe. A proposed agent design would add a durable coverage-id ledger and a human hold before chart write—not a claim that no-code cannot retry.

The team at US Tech Automations can map a configurable coverage-to-draft-to-chart trail once the model, the BAA, and the clinician reviewer are named. Review agentic workflows after that sentence exists.