Claude Fable 5 vs GPT-5.6 Sol: Price, Specs & Side-by-Side (2026)

Comparison · August 2026

Claude Fable 5 and GPT-5.6 Sol are two million-token flagships teams actually argue about in 2026: Anthropic’s Mythos-class knowledge-work model versus OpenAI’s GPT-5.6 series flagship. This is a decision guide, not a leaderboard dump. We use published API specs, three workload cost scenarios, and live side-by-side snippets on writing, a coding bug, and a false premise. Multi-model AI means you can keep both. Start in a multi-model AI workspace or on i10X.

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Persona picker

You are…	Start with	Why
Writer / CS / marketer	GPT-5.6 Sol (often); Fable for a fuller letter shell	Sol’s rewrite was complete and tight. Fable added a subject line, then truncated in our capture.
Developer / agent builder	Sol default; Fable when you want contract discussion	Both fixed the empty-list bug. Fable discussed return 0 vs ValueError; Sol added a smoke print.
Researcher / analyst	Either (~1M class); Sol slight context edge	Both sit in the million-token class with text, image, and file inputs. Sol lists 1,050,000 vs Fable 1,000,000.
Budget / high volume API	GPT-5.6 Sol	Same modality shape, about 5x cheaper on our chat, repo, and cached-agent scenarios at current list rates.
Anthropic-native knowledge work	Claude Fable 5	Vendor positions Fable as Mythos-class for autonomous knowledge work and coding. Pay the premium only if that product surface is the job.

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What we are comparing (exact versions)

Multi-model AI means using more than one large language model in one work system. This page compares two specific API models, not vague “Claude vs ChatGPT” brand names.

Field	Claude Fable 5	GPT-5.6 Sol
Provider	Anthropic	OpenAI
API model	Claude Fable 5	GPT-5.6 Sol
Listed API name	Anthropic: Claude Fable 5	OpenAI: GPT-5.6 Sol
Family / tier	Mythos-class; autonomous knowledge work and coding	GPT-5.6 series flagship; reasoning, coding, agentic workflows
App vs API note	Also in Claude products; this article uses the API model above	Also in ChatGPT / OpenAI API; this article uses the API model above

If a page still compares older Claude or GPT-5.x IDs without the Fable 5 / GPT-5.6 Sol labels, treat it as historical. For routing across many models, see AI model routing.

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Spec sheet (API, 2026-08-24)

Spec	Claude Fable 5	GPT-5.6 Sol
Context window	1,000,000 tokens	1,050,000 tokens
Max output (if published)	Not published on the card we pulled	Not published on the card we pulled
Input modalities (card)	text, image, file	file, image, text
Output	text	text
Reasoning	Vendor copy mentions reasoning support	Vendor copy highlights complex reasoning and agentic workflows
Open weights	Not listed as open weights on the card we pulled	Not listed as open weights on the card we pulled
Vendor positioning (short)	Mythos-class for autonomous knowledge work and coding	Flagship for reasoning, coding, agents; strong on command-line and multi-step coding

On paper these two are close cousins: million-token class, text/image/file in, text out. The gap you feel is price, plus voice and thoroughness in the live pack. Treat vendor copy as positioning, not a score. We are not citing third-party leaderboard numbers here.

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Pricing and real workload cost

List prices are easy to misread. Workload cost is what you feel. Rates below are from published API pricing on 2026-08-24.

Price	Claude Fable 5	GPT-5.6 Sol
Input / 1M tokens	$10.00	$2.00
Output / 1M tokens	$50.00	$10.00
Cache read / 1M	$1.00	$0.20

Sol is 5x cheaper on input and output stickers ($2 / $10 vs $10 / $50). Cache reads follow the same 5x pattern ($0.20 vs $1.00) if your stack actually hits cache.

Scenario	Assumed tokens	Est. Claude Fable 5	Est. GPT-5.6 Sol
Chat turn	1k in + 0.5k out	$0.035	$0.007
Repo / doc review	80k in + 4k out	$1.00	$0.20
Agent loop	200k in (50% cached) + 20k out	$2.10	$0.42

That 5x gap compounds on cached agent loops. For subscription math across Claude / ChatGPT style plans, see AI subscription stack cost. Always verify live vendor pages before budgeting.

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Performance by job (specs + live pack)

We are not inventing public benchmark crowns for this pair. The signals we trust here are the API card (context, modalities, price) and the three live prompts we actually ran. Confirm with your own pack. Method: side-by-side AI comparison.

Coding and agents

OpenAI positions GPT-5.6 Sol for complex reasoning, coding, and agentic workflows, “particularly strong at command-line and multi-step coding tasks.” Anthropic positions Claude Fable 5 as Mythos-class for autonomous knowledge work and coding. Our empty-list micro-test was a correctness tie. Fable discussed return 0 vs ValueError as a contract choice. Sol applied the same guard and added print(average([])). Use Sol as the cheaper default loop; bring Fable in for contract discussion or Anthropic-native sessions.

Writing and tone

Benchmarks barely measure voice. Fable produced a letter shell with a subject line and a flexible Wednesday ask. The captured excerpt cuts off at the competitive-slide note, so we do not claim Fable omitted Acme pricing. Sol’s captured excerpt is complete: Q3 deck, missing Finance numbers, Friday promise, Wednesday move, Acme pricing, short thanks. Sol reads like a tight ops email. Fable reads like a fuller inbox draft.

Research, math, reasoning

Both refused the green-cheese Moon premise. Fable explained silicate rock and metals and offered real follow-ups (water ice, oxygen from regolith, helium-3). Sol refused in one line, then gave a four-step plan: polar water ice, purify and split, habitat protein, recycle. Sol was more operational. Fable was more topical. Neither invented a cheese mine as the plan.

Multimodal and long context

Structural near tie. Fable 1,000,000 tokens; Sol 1,050,000. Both take text, image, and file, and emit text. If your day is PDFs and screenshots, either card can take the input. Pick on quality and cost, not a 50k token delta.

Speed

This pack did not measure tokens per second or time-to-first-token. Third-party speed screenshots go stale by region and harness. Measure p50 latency from your region before you ship a UX claim.

Job	Edge	Why
Hard coding / agents	Split (test your repo)	Vendor copy differs; our micro-fix was a tie
Everyday writing	GPT-5.6 Sol (often)	Complete, tight ops email in our capture; Fable had the subject line
Long docs / files / images	Near tie / slight Sol	Same modality set; Sol 1.05M vs Fable 1M
False-premise handling	Split (style)	Both refuse; Fable explains, Sol plans the real resource path
Cost at volume	GPT-5.6 Sol	About 5x cheaper on chat, repo, and cached agent loops

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Side-by-side test (live API test, 2026-08-24)

We ran the same prompts on Claude Fable 5 and GPT-5.6 Sol in a multi-model workspace. This batch captured three live prompts. Scores are editorial 1-5 on instruction following, depth, factual caution, style, and usefulness (max 25 per prompt). Run a long-paste contradict check in your own workspace before you lock a default.

Test 1: Client email rewrite

Task: Keep every fact. Warmer. Under 120 words.

Claude Fable 5 (excerpt): Opened with Subject: Q3 Deck Update & Proposed Meeting Change, then “Hi [Name],” follow-up on last Tuesday’s Q3 deck, Finance numbers still missing after a Friday promise, ask to move stakeholders to next week (Wednesday, happy to adjust). Capture cuts off at “we’ll be updating the com.”

GPT-5.6 Sol (excerpt): “Hi [Name],” same Q3 follow-up, Finance numbers expected Friday and still missing, ask to move to next Wednesday if that works, plus “We’ll also need to update the competitive slide with the new Acme pricing we found. Thanks!”

Edge: Sol for a complete, tight letter in the captured window. Fable for a subject line and a more flexible reschedule sentence. If your brand hates filler, Sol is the safer first paste.

Test 2: Empty-list average bug

Both named ZeroDivisionError when nums is empty and proposed if not nums: return 0 (Fable also allows a ValueError). Sol appended a print of average([]). Tie on correctness. Fable is the better mini-spec. Sol is the better smoke test.

Test 3: False premise (Moon cheese)

Both refused first. Fable: the Moon is rock and minerals (silicates such as feldspar and pyroxene, metals such as iron and titanium), no cheese or protein to mine, then optional real topics. Sol: silicate rock and metal, no mineable protein, then a four-step lunar-resource plan (polar ice, purify/split, habitat protein, recycle). Both pass. Sol is more operational; Fable is more explanatory. For publishable claims, still add a second-model check: multi-model hallucination checks.

Prompt	Claude Fable 5	GPT-5.6 Sol	Note
Email rewrite	22/25	23/25	Fable: subject line, truncated capture. Sol: complete, names Acme
Bug fix	24/25	24/25	Tie; Fable discusses contract, Sol adds a print
False premise	23/25	24/25	Both refuse; Sol more practical, Fable more topical
Total (this pack)	69/75	71/75	Close; jobs still split. Cost is not close.

The interesting result is not the two-point gap. Quality was close while Sol was about 5x cheaper. If Fable wins for you, it should win on Anthropic product fit or a job this pack did not measure.

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Ecosystem and where you run them

• Claude Fable 5: Anthropic API and Claude product surfaces. Strength: Mythos-class knowledge-work positioning, Projects-style long sessions, Anthropic safety stack.
• GPT-5.6 Sol: OpenAI API and ChatGPT. Strength: GPT-5.6 flagship lane, tool/agent workflows, broad connector coverage. Vendor copy calls out command-line and multi-step coding.
• Both in one place: Multi-model workspaces like i10X let you compare the same prompt without two browser profiles. See also the multi-model AI guide.

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Pros, cons, and failure modes

Claude Fable 5

• Pros: Mythos-class knowledge-work positioning; 1M context; text/image/file; subject-line email shell; contract-aware coding note; explanatory false-premise refusal.
• Cons: $10 / $50 list rates, about 5x Sol on our workloads; 50k fewer listed context tokens; captured email truncated.
• Fails when: you need high-volume API generation at Sol-class spend, or you pick Fable only because the name sounds newer.

GPT-5.6 Sol

• Pros: About 5x cheaper on matched workloads; 1.05M context; same text/image/file shape; complete tight email; smoke-test print; operational lunar-resource redirect.
• Cons: Not the Anthropic product surface; shorter on contract design in the coding writeup.
• Fails when: switching cost dwarfs the API delta, or you need a subject line on the first try.

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Decision guide: pick one or route both

If you need…	Choose
Output-cheap high volume at this quality band	GPT-5.6 Sol
Tight ops email with every fact in one short letter	GPT-5.6 Sol (A/B once)
Subject-line letter shell / Anthropic voice	Claude Fable 5 (A/B once)
Everyday coding loop / CLI-style agents (vendor copy)	Start GPT-5.6 Sol; verify on your tools
Contract-aware coding explanation	Claude Fable 5 in our micro-test
Mixed SaaS week	Both: volume and defaults → Sol; paid Anthropic knowledge work → Fable

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Application walkthroughs: where each model is better

1) Customer support email

Better often: GPT-5.6 Sol for a short complete rewrite that names Acme pricing. Fable looked more like a real inbox draft (subject plus flexible Wednesday language) but the capture cut off. Switch to Fable if you need the subject filled in on the first try.

2) Long PDF / research pack

Near tie on the card. Both are million-token class with file input. Sol’s 1.05M is a small edge. Summarize with your default, then run the other as a critic. Method: side-by-side AI comparison.

3) Everyday Python scripting

Often GPT-5.6 Sol as the cheaper interactive partner. Our micro bug-fix was a correctness tie. Use Fable when you want the contract spelled out (return 0 vs raise).

4) Output-heavy generation at API scale

Better on cost: GPT-5.6 Sol. Matched modality shape, about one-fifth the stickers. Agent-loop estimate: $0.42 vs $2.10. Pay Fable only when the Anthropic Mythos-class surface is the product.

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Consumer plans vs API (do not mix them up)

Keep subscriptions separate from API names. This page compares Claude Fable 5 vs GPT-5.6 Sol in the API. Claude.ai and ChatGPT may wrap different tools, rate limits, and mid-tiers. Phone UX is a lived week in both apps. Agent IDs are this page.

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What this means for routing

Fable 5 and GPT-5.6 Sol are close enough on context and modalities that routing should be job-based and cost-based. Volume and cached agent loops → Sol. Paid Anthropic knowledge work → Fable. Email → A/B; Sol won completeness here. Publishable claims → second-model check. Related: Claude Fable 5 vs Claude Opus 5 (Opus is half the Fable sticker). Playbook: AI model routing.

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Frequently asked questions

Which is better overall, Claude Fable 5 or GPT-5.6 Sol?
Neither permanently. This pack was close (69/75 vs 71/75). Sol wins list price by about 5x. Fable is the Mythos-class Anthropic pick. Choose by job and budget.

Which is better for coding?
Vendor copy puts Sol on command-line and multi-step coding. Our empty-list test was a tie. Fable discussed the contract; Sol added a print. Re-run on your repo.

Which is better for writing?
Taste. Sol’s captured letter was complete and tight. Fable added a subject line. A/B on brand voice.

Which is cheaper?
GPT-5.6 Sol, at published API rates (2026-08-24): $2 / $10 / $0.20 cache vs Fable $10 / $50 / $1.00. Workloads: $0.007 vs $0.035 chat, $0.20 vs $1.00 repo, $0.42 vs $2.10 cached agent loop.

Which has the larger context window?
GPT-5.6 Sol (1,050,000) vs Claude Fable 5 (1,000,000). Practically a near tie.

Do I need both?
If you live in Claude for knowledge work and also burn a lot of agent tokens, yes. That is the multi-model thesis.

Are we comparing apps or API models?
API models Claude Fable 5 and GPT-5.6 Sol. Consumer apps may wrap different defaults.

How often should I re-test?
After any major version bump. Monthly is sane. Re-run writing, coding, and a false-premise trap at minimum.

Where can I run them side by side?
i10X. Method: side-by-side AI comparison.

What about hallucinations and trust?
Both refused our Moon-cheese premise. Still dual-check publishable claims. Multi-model hallucination checks.

Is Claude Opus 5 a cheaper Anthropic alternative?
On the cards we pulled, Opus 5 is $5 / $25 vs Fable $10 / $50, same 1M context and text/image/file. See Claude Fable 5 vs Claude Opus 5.

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