Claude Fable 5.1 vs GPT-5.6 Sol: HLE Science (2026)

Claude Fable 5.1 vs GPT-5.6 Sol for Humanity’s Last Exam-style science questions is a research-desk decision for registered investment advisers, not a chat ranking. The job is a hard, sourced answer that can survive a CCO glance before it lands in a quarterly letter, a life-event note, or a planning memo. HLE is the public proxy for that difficulty: 2,500 specialist questions that are not supposed to fall out of a single web search.

This page names two products only. Fable 5.1 is the live, expensive-cache-cut knowledge-work model. Sol is the cheaper OpenAI control you can already run. US Tech Automations is not a third model; it is the workflow layer that files the answer, the citations, and the human hold.

TL;DR

• Pick Claude Fable 5.1 when the memo must survive specialist science questions: independent HLE is 59.1%, and OpenAI’s 3 September table lists Fable at 65.0% on HLE with tools.

• Keep GPT-5.6 Sol when the desk is cost-capped and the questions are routine, because list is $4/$20 and independent Intelligence cost per task is $0.95 versus Fable at $3.69.

• Fable 5.1 is live on paid Claude, API, and clouds today; Mythos 5.1 is invite-only and is not a picker option on this page.

• Do not paste HLE-hard answers straight into a client file. Store the question id, the sources, and a reviewer sign-off.

Who this is for

This comparison is for a CIO, research lead, or operations manager at an RIA or hybrid advisory firm that already writes client memos and occasionally has to answer a science-heavy question — drug mechanism, climate physical risk, biotech pipeline, actuarial table — without pretending the model is the advice.

Typical shape: 8–60 licensed people, a CRM of record, an archive, and a CCO who will ask where the paragraph came from. Average advisor book size is, according to Cerulli Associates, $98M AUM in the RIA channel, which is enough money that a wrong science claim in a letter is not a small-talk problem.

Red flags: skip Fable if the only “research” you do is rebalancing commentary you already template; skip Sol if the desk is already failing specialist questions and you have budget for $10/$50 list; skip a custom orchestration layer if the answer never leaves a private scratch pad.

Zapier, Make, or n8n can take a CRM stage change, post the draft to Slack, retry a failed write, and keep a run log if you design uniqueness, access, and retention. That is a fair DIY choice for one stable recipe. A proposed agent design would add a durable opportunity-id ledger and a human hold before the paragraph hits the client vault — not a claim that no-code tools cannot retry.

When NOT to use US Tech Automations: leave it out when Claude or Codex already drafts into a folder your archive already captures, when native CRM automation already files the note, or when a no-code scenario already notifies the CCO on failure.

The hidden cost of manual science memos

The expensive version of this workflow is still a person: an analyst opens three PDFs, writes a paragraph, and a supervisor reads it on Friday. Mid-size RIA annual compliance cost sits, according to FINRA, in the $750K–$1.5M band for the $50M–$500M AUM slice, which is the overhead that grows when every hard question is an untracked chat paste. SEC-registered retail-serving RIAs number, according to SIFMA, 15,400+, so this is not a niche desk problem.

Humanity’s Last Exam is, according to the HLE paper, a 2,500-question multimodal set designed to be difficult even for specialists and not quickly answerable via internet search. If your internal questions look like that — mechanism of action, trial endpoint, climate attribution — a cheap model that “sounds sure” is the cost, not the $4 input line.

The table is a planning sheet, not a savings guarantee. The load-bearing comparison is whether Fable’s HLE lift is worth 2.5× list when Sol already drafts the easy 80%.

A desk that treats every question as “just ask Sol” pays twice: once on the cheap tokens, and again when a supervisor rebuilds the paragraph from primary papers because the draft had no mechanism, no endpoint, and no citation. Fable 5.1 does not remove that supervisor. It changes whether the first draft is worth supervising. Sol remains the right first pass when the question is a restatement of a fact already in the IPS, the ADV brochure, or last quarter’s letter. The failure mode is using Sol on a question that looks like HLE — specialist, multimodal, easy to phrase confidently — and then discovering the error in a client meeting instead of in the archive.

Price the reviewer, not only the model. Fifteen minutes of a CFA or CCO on 36 quarterly questions is nine hours. If Fable cuts the rewrite rate and Sol does not, the $10/$50 list can still be the cheaper accepted memo. If Fable’s extra tokens are spent on a longer wrong draft, Sol plus a tighter prompt is cheaper. That is why this page insists on a question class, a source floor, and a named reviewer before anyone “standardizes on Fable.”

How we evaluated

Weights assume a U.S. advisory research desk that must archive the answer and name a reviewer. A trading desk that only wants a cheap summary should raise “list price” and lower “HLE-with-tools.”

Provider tables are labeled provider-run. Independent HLE for Fable is the Artificial Analysis 59.1% figure. METR horizons are unpublished and are not scored.

How the automation actually works

The motion is not “ask HLE.” The motion is: a CRM record moves, a hard question is attached, a model drafts with tools, a reviewer accepts, the archive stores the packet.

Salesforce documents Opportunity.StageName as a picklist field on Opportunity in the Opportunity standard object. A 12-opportunity quarterly-review batch, 3 science questions per review, and a 15-minute reviewer SLA is a real desk: 36 questions, 36 drafts, 36 accept-or-rewrite decisions. US Tech Automations can trigger when Opportunity.StageName hits the review stage, pull the question text, send it to Claude Fable 5.1 or GPT-5.6 Sol, require at least 2 source URLs on the draft, and hold the client letter until a named reviewer checks the packet. Adjacent advisor motions — new-client onboarding, CRM workflow, and quarterly portfolio review reminders — should reuse the same opportunity id, not a second chat log.

Fable 5.1 thinking is adaptive and on by default. Forced tool_choice of any or a named tool returns 400. Earlier models cannot read Fable 5.1 thinking blocks. Editing earlier turns invalidates thinking. Build the workflow so the reviewer sees the final memo, not a broken thinking blob.

Sol remains the right engine when the question is a restatement of a fact the firm already published. Fable is the engine when the question looks like HLE: specialist, multimodal, easy to fake.

Benchmarks: before vs after

Independent HLE for Fable 5.1 is, according to Artificial Analysis, 59.1%, ahead of the previous published best of 55.5% from Claude Fable 5 on that slice. OpenAI’s 3 September 2026 launch table (provider-run) lists Claude Fable 5.1 at 65.0% on Humanity’s Last Exam with tools. Treat the 65.0% cell as OpenAI-run, not as a second independent lab. Fable’s Intelligence Index eval used Anthropic’s default safety fallback, with about 4% of output tokens routed to Opus; do not describe that Intelligence run as a pure Fable-only sample.

Fable HLE (AA): 59.1%. Fable HLE with tools (OpenAI table): 65.0%. HLE question count: 2,500.

Sol’s public job on this page is cost and access, not a published HLE-with-tools cell in that same OpenAI table. Independent Intelligence cost per task is $0.95 for Sol versus $3.69 for Fable. List input/output is $4/$20 versus $10/$50. Cache reads on Fable 5.1 are $0.25 per 1M versus Sol’s $0.40 cached input on the Work/Codex card — Fable is cheaper on cache hits, more expensive on uncached generation.

Before: analyst pastes into Sol, copies a paragraph, no sources, CCO finds it later. After: Fable or Sol is chosen per question class, sources are required, reviewer is on the opportunity, archive has the packet. The “after” is a process change. The model is only the draft engine.

Build vs buy vs orchestrate

Build means your research team keeps a prompt library in Claude or in OpenAI and pastes. Buy means you pay Fable 5.1 or Sol as the engine. Orchestrate means the CRM event, the model call, the source check, and the hold are one workflow.

Claude Fable 5.1 list I/O is unchanged from Fable 5 at $10/$50; cache reads fell 75% from $1 to $0.25. Anthropic estimates typical token bills about 25% cheaper, up to about 45% on agent loops. Sol remains ~2.5× cheaper on uncached list. Pick the engine for the question class, then decide whether the CRM must see the packet.

On AWS, Fable 5.1 is a Covered Model: aws_review mode, up to 30-day retention plus AWS human review unless the firm is EFS-eligible for ZDR through 31 December 2026. That is a compliance fact, not a reason to skip the model, and not a reason to skip the archive.

Knowledge cutoff for Fable 5.1 is June 2026; Sol’s cutoff is whatever OpenAI publishes for that SKU on your contract. Neither cutoff is a substitute for retrieving the paper you are about to cite. If the science question depends on a trial readout after the cutoff, the workflow must fetch, not remember. If the question is a restatement of a 2024 paper already in the firm’s vault, Sol may be enough and Fable is wasted list price. Write that rule down: cutoff plus retrieval for post-cutoff facts; vault first for firm-owned facts; HLE-class questions go to Fable with tools; everything else stays on Sol until a reviewer says otherwise.

Pros and cons

Claude Fable 5.1

Pros

• Independent HLE 59.1% and OpenAI-table HLE with tools 65.0% are the published specialist-question scores on this page.

• Live today on paid Claude, API, AWS, Google Cloud, and Foundry (Anthropic-hosted).

• Cache reads at $0.25 per 1M, a 75% cut versus Fable 5’s $1 cache read.

• 1,000,000-token context and 128,000 max output, with adaptive thinking on.

Cons

• List $10/$50 and $3.69 independent cost per task versus Sol at $4/$20 and $0.95.

• Intelligence eval used a ~4% Opus safety fallback; do not treat that composite as a pure Fable-only run.

• Forced tool_choice any/tool returns 400; thinking blocks are not readable by earlier models.

• Mythos 5.1 is the looser-safeguard twin and is invite-only; do not put it on a public picker.

GPT-5.6 Sol

Pros

• List $4/$20 and $0.95 independent cost per task make it the default for routine facts.

• Already on the seats most firms already paid for.

• Enough when the “science” question is a restatement of a paper the firm already filed.

• Lower bill while you wait to see whether Fable’s HLE lift shows up on your own question set.

Cons

• Not the published HLE leader on this page; Fable holds the 59.1% / 65.0% cells cited above.

• Cache reads are not the $0.25 Fable 5.1 number.

• Cheap drafts still become expensive when a CCO has to unwind an unsourced paragraph.

• Using Sol for HLE-hard questions to “save 2.5×” is how the compliance cost line grows instead of the model line.

FAQs

Which model should an RIA use for specialist science questions?

Use Claude Fable 5.1 when the question looks like HLE; use GPT-5.6 Sol when the answer is a restatement of a fact the firm already holds.

Is Fable 5.1 cheaper than Sol?

No on uncached list and no on independent Intelligence cost per task ($3.69 vs $0.95); Fable can be cheaper on cache hits at $0.25 per 1M reads.

Can we pick Mythos 5.1 instead?

No. Mythos 5.1 is invite-only trusted access, not a public picker option on this page.

Do we need a workflow layer on top of Claude or Codex?

Only if the draft must attach to a CRM id, an archive packet, and a named reviewer before a client can see it.

What does HLE actually measure?

It measures 2,500 hard, specialist, multimodal questions that are not supposed to fall out of a quick search; it does not measure suitability, fiduciary duty, or your IPS.

When NOT to use US Tech Automations for this memo path?

Skip it when the model output never leaves a private scratch pad, when the CRM already files the note with sources, or when a no-code scenario already pages the reviewer.

Key Takeaways

• Fable 5.1 is the published HLE lead on this page (59.1% independent, 65.0% OpenAI-table with tools); Sol is the cheaper control.

• Independent cost/task: Fable $3.69 vs Sol $0.95. Do not invert that column.

• Source the draft, file it on the opportunity, and hold the client letter. HLE skill without an archive is still a paste job.

• Fable is live; Mythos is not on the picker. Sol is live. Choose the engine per question class.

• Review workflow pricing after you can name the CRM field, the reviewer, and the question class that actually needs Fable.

The team at US Tech Automations can map a configurable review-stage-to-archive trail. Bring the opportunity field, the model ids, and the person who is allowed to release the letter.