GPT-6 Astra vs Claude Fable 5.1: Tier-4 Math (2026)

FrontierMath Tier 4 is a research bench, not a portfolio product. RIA research teams still quote it because the same models that saturate hard math are the models being aimed at RMD math, tax-lot selection, Social Security claiming paths, and concentrated-stock exercise trees. GPT-6 Astra and Claude Fable 5.1 are the two models this page compares on that Tier-4 row and on the operational question that follows: who is allowed to write the number into the household file.

A 97.6% bench score does not authorize an unsupervised write to a CRM estimate field. Saturation on a public math set is evidence of capability. The firm still needs a reviewer, a unique household ID, and a record of the prompt, the model ID, and the approved output. This is not tax, legal, or investment advice.

TL;DR

• GPT-6 Astra leads the OpenAI-run FrontierMath Tier 4 (v2) row at 97.6%; Claude Fable 5.1 prints 87.8% on the same table. Treat those as provider-run, same-table numbers, not as an independent lab.

• List price ties at $10 input / $50 output per 1M tokens. Cache reads do not tie: Astra $1.00 versus Fable 5.1 $0.25. Independent Intelligence cost per task is the other way: Astra $1.67 versus Fable $3.69.

• Do not confuse FrontierMath with ARC-AGI-3. ARC Prize’s standard harness is 62.7% at max; the 99.9% figure is a provider adapter / Responses API harness and is not a Tier-4 score.

• File math into CRM only after a human hold. A saturated bench is not a supervisory procedure.

Who this is for

This page is for RIA research leads, CIO / P&I committees, and operations owners who want to attach a frontier model to tax-lot, RMD, or scenario math and then land an approved figure in the CRM or planning file. It assumes the firm already has a planning tool and a CRM, and that someone will own model-ID logging. Typical fit is a 10–40 advisor firm with a research desk, not a solo who pastes ChatGPT into a PDF.

Red flags: do not buy Astra because a blog said 97.6% if you need a ChatGPT button on 3 September 2026 — Astra is limited orgs, Trusted Access / Daybreak, and Foundry Limited Access first, and Enterprise stays off until an admin enables it. Do not treat Mythos 5.1 or Daybreak as a public picker. Do not let either model invent a required minimum distribution or a tax-lot gain. Pause if the firm cannot name the source of the account values the model is allowed to see.

When native planning software already produces the RMD and tax-lot numbers your advisors use, stay there. Model scoring is for the residue: messy, multi-constraint questions the planning tool does not encode. For the annual RMD operations path, see RMD calculation workflow automation. For rebalancing after the number is approved, see portfolio rebalancing across Orion, Schwab, and Redtail. Estimating and proposal tools remain a different buy; start with estimating software for financial advisors if the gap is fee quotes, not math.

How we evaluated

We compared only GPT-6 Astra and Claude Fable 5.1, dated 3 September 2026, for Tier-4 math used as a proxy for hard household calculations that still need a reviewer. Weights: documented Tier-4 score 30%, independent Intelligence / cost 20%, access on this date 20%, cache and long-context economics 15%, records design 15%. OpenAI’s FrontierMath row is provider-run. Artificial Analysis is the independent composite (Intelligence 66 vs 61; cost/task $3.69 vs $1.67). ARC-AGI-3 is cited only to stop a common mix-up; it is not the decision metric. METR horizons are unpublished.

The three ways teams solve this today

Research desks currently do hard household math in one of three patterns. Pattern A is the planning tool only: MoneyGuide, eMoney, or a custodian calculator, with a screenshot saved to the CRM. Pattern B is a frontier model in a chat product, with the advisor pasting the answer into a note. Pattern C is an API call with reasoning.effort pinned, a logged prompt, and a human hold before any CRM write. This page exists because Pattern B is leaking into Pattern C without the hold.

Source: cycle times are design targets for a 20-household sample, not a measured panel. Model-ID and reviewer columns are process requirements, not vendor scores.

Pattern A is enough when the question is a standard RMD and the planning tool already matches the custodian. Pattern B is how 97.6% becomes an unlogged number in a client file. Pattern C is the only path this page will recommend once you decide a frontier model is in scope.

What automating Tier-4 household math changes

The workflow is not “run FrontierMath.” The workflow is: pull the approved account values, ask a constrained question, pin the model and effort, store the raw output, hold for a person, then write a single approved figure to the CRM estimate or note. Automating that changes three things. First, the model ID is on the run, so next year’s exam can see gpt-6-astra versus claude-fable-5-1. Second, the prompt cannot silently include a holding the client does not own. Third, a 97.6% bench no longer gets to skip the reviewer.

OpenAI documents reasoning.effort on GPT-6 Astra, including max, according to OpenAI, 5 effort values (low, medium, high, xhigh, max) and no none. In an illustrative research-desk pilot of 20 household files, a workflow can issue 20 Responses API calls with reasoning.effort set to max, park 5 exceptions (missing cost basis, ambiguous beneficiary, or a requested tax conclusion the model must not give), and write 15 approved figures into the CRM estimate field after release. These are design figures, not a claim about FrontierMath accuracy on client books.

A shadow week should look like a ledger, not like a chat transcript. The desk runs the same 20 files through the planning tool (control) and through the chosen model (draft), then a reviewer records only the 15 files where both the source values and the question were in policy. Files that fail the ID check never reach the model. Files that fail the reviewer never reach the CRM. That is the entire automation: fewer silent pastes, not fewer humans.

Source: counts are an illustrative 20-file research-desk sample. They are not FrontierMath item counts and not a measured client result.

US Tech Automations is the Pattern C layer: it stores the prompt hash, the model ID, and the reviewer identity, and it will not write the CRM estimate until release. Astra tools require the Responses API. Fable 5.1 thinking is always on; forced tool_choice any/tool returns 400; editing earlier turns invalidates thinking. Neither model should be asked for a legal or tax opinion. The output is a draft calculation for a credentialed reviewer.

Time + cost deltas

OpenAI’s launch table is the only public side-by-side on FrontierMath Tier 4 (v2) that names both products. Astra prints 97.6% and Fable 5.1 prints 87.8% according to OpenAI, 97.6% versus 87.8% on that 3 September 2026 table. Do not promote those rows to “independent.” They are useful as a same-table delta. Fable 5.1 still leads independent Intelligence (66 vs 61) and is the model you can actually log into on paid Claude today.

ARC-AGI-3 is a different bench. The ARC Prize standard harness at max is 62.7% according to ARC Prize, 62.7% at about $26,098, with the 99.9% figure reserved for OpenAI’s provider adapter / Responses API harness. Never quote 99.9% as if it were the independent number, and never treat it as a FrontierMath score.

List I/O is $10 / $50 both ways. Cache reads are $1.00 (Astra) versus $0.25 (Fable 5.1). Independent Intelligence cost per task is $1.67 (Astra) versus $3.69 (Fable 5.1). A long, cached research prompt is a Fable 5.1 cache story. A max-effort, low-reuse derivation is closer to Astra’s task-cost row if your eval looks like AA’s. Astra long context above 272K input doubles input/cache and 1.5× output except Codex. Fast mode is 2× Standard in API docs and 2.5× on the Help Center Codex/Work card; name the surface.

Fable 5.1 context is 1M tokens and list I/O is $10 / $50 according to Claude, 1M context with $10 / $50 per million tokens and cache reads at $0.25. Supervision still sits with the firm. FINRA Rule 3110 is the broker-dealer supervisory backbone according to FINRA, Rule 3110, and it does not disappear because a bench saturated. RIAs have their own Rule 206(4)-7 analogue; this page is not a substitute for counsel.

Source: OpenAI launch table 2026-09-03 (FrontierMath); Artificial Analysis 2026-09-03 (Intelligence and cost/task); Anthropic Fable 5.1 overview (context and cache).

Where US Tech Automations fits

US Tech Automations is not a third model. It is the queue that takes a household ID, calls gpt-6-astra or claude-fable-5-1, stores reasoning.effort or Fable effort, and blocks the CRM write until a reviewer releases the figure. Use it when Pattern C is the decision and the research desk refuses to paste from a chat window. Do not use it to “make 97.6% production.”

Zapier, Make, or n8n can move an already-approved estimate into Slack or a folder, retry a failed write, and keep a run log if you design those pieces. That is a fair DIY path after a human has signed the number. It is not a substitute for pinning reasoning.effort and refusing auto-write on tax-lot math.

When NOT to use US Tech Automations: the planning tool already emits the RMD you file; the research desk’s only need is a Claude side panel; or a no-code recipe already copies an approved PDF into the household folder. Native software wins when there is no second system and no model call.

Adoption timeline

Access, not the bench, is the 3 September 2026 constraint. Fable 5.1 is live. Astra is staggered. A research desk that needs a model this week uses Fable 5.1 for drafts and keeps Astra on a wait-list for the API. Enterprise Astra remains off until an admin enables it. Free ChatGPT has no announced Astra date.

Source: week counts are an illustrative 20-household rollout, not a measured implementation. Astra column stays at 0 calls until the org actually has API or Foundry access.

Pros and cons

GPT-6 Astra

Pros

• OpenAI-table FrontierMath Tier 4 (v2) at 97.6%, the higher of the two named products on that row.

• AA Intelligence cost per task $1.67, below Fable 5.1’s $3.69.

• reasoning.effort includes xhigh and max; 1.05M context; Responses API tools when you wire them.

• Stronger provider-run AutomationBench (41.4%) if the same agent must also operate the planning UI.

Cons

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

• Cache reads $1.00 / 1M versus Fable 5.1’s $0.25, which hurts a reused research preamble.

• Independent Intelligence Index 61 vs 66, so “generally smarter” is not Astra’s independent claim.

• Long-context multiplier above 272K (except Codex) can surprise a desk that pastes whole household files.

Claude Fable 5.1

Pros

• Live on paid Claude and major clouds today, which is the access fact that beats a higher bench you cannot call.

• Independent Intelligence 66; cache reads $0.25 / 1M; 1M context; $10 / $50 list.

• 87.8% on the same OpenAI FrontierMath T4 v2 table — behind Astra, still a high same-table print.

• Adaptive thinking always on; default effort high.

Cons

• Trails Astra by 9.8 points on the provider FrontierMath T4 v2 row (87.8% vs 97.6%).

• AA Intelligence cost per task $3.69, with ~4% of AA eval tokens routed to Opus via safety fallback.

• Forced tool_choice any/tool returns 400; thinking blocks are model-tied and break if you edit earlier turns.

• AWS Covered Model 30-day review unless EFS/ZDR, which a research desk on Bedrock must read.

FAQs

Does 97.6% on FrontierMath mean Astra should file RMDs?

No. 97.6% is a provider-run bench row. An RMD is a client-specific calculation with account values, decedent rules, and a reviewer. Use the score to pick a draft model, not to skip the hold.

Is the 99.9% ARC-AGI-3 number the same as Tier 4?

No. ARC Prize’s standard harness is 62.7% at max. 99.9% is the provider adapter / Responses API harness. FrontierMath Tier 4 (v2) is a different set. Do not mix them in a committee slide.

Which model is cheaper for a cached research preamble?

Claude Fable 5.1 on cache reads ($0.25 vs $1.00). Astra is cheaper on AA’s Intelligence cost/task row ($1.67 vs $3.69). Measure your own tokens. List I/O is $10 / $50 either way.

Can we run this in Zapier without an orchestration layer?

You can, after a human has approved the figure. Zapier, Make, or n8n can post the approved number to the CRM or a channel. They should not be the unattended caller of reasoning.effort = max on a tax-lot question.

When NOT to use US Tech Automations for this math?

Skip it when the planning tool already produces the number you file, when the desk only needs a Claude or ChatGPT side panel, or when a no-code recipe already files an approved PDF. Buy the hold-and-log layer only when Pattern C is the decision.

Key Takeaways

• Astra 97.6% vs Fable 5.1 87.8% on OpenAI’s FrontierMath T4 v2 table; independent Intelligence still sits with Fable 5.1 at 66 vs 61.

• $10 / $50 list is a tie; cache $1.00 vs $0.25 and AA task $1.67 vs $3.69 are not.

• ARC-AGI-3 99.9% is adapter-only; standard harness 62.7%. METR horizons are unpublished.

• Access on 3 September 2026 favors Fable 5.1. Astra is staggered; Enterprise off until an admin enables it.

• Pin reasoning.effort, log the model ID, and hold the CRM write. Saturation is not supervision.

The team at US Tech Automations can wire Pattern C on pricing after you name the CRM estimate field, the reviewer, and which model ID is allowed to draft.