{"id":501,"date":"2026-08-25T06:32:28","date_gmt":"2026-08-25T06:32:28","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=501"},"modified":"2026-08-25T06:32:29","modified_gmt":"2026-08-25T06:32:29","slug":"claude-fable-5-vs-gpt-5-6-sol","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/claude-fable-5-vs-gpt-5-6-sol","title":{"rendered":"Claude Fable 5 vs GPT-5.6 Sol: Price, Specs & Side-by-Side (2026)"},"content":{"rendered":"\n
\n\n

Comparison \u00b7 August 2026<\/p>\n\n

\nClaude Fable 5 and GPT-5.6 Sol are two million-token flagships teams actually argue about in 2026: Anthropic\u2019s Mythos-class knowledge-work model versus OpenAI\u2019s 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\nmulti-model AI workspace<\/a>\nor on\ni10X<\/a>.\n<\/p>\n\n

\nQuick verdict<\/strong>\n

Pick Claude Fable 5 if:<\/strong> you want Anthropic Mythos-class positioning for autonomous knowledge work and coding, a full email with a subject line, and a coding writeup that discusses the contract (return 0 vs raise) rather than only the patch.<\/p>\n

Pick GPT-5.6 Sol if:<\/strong> you want the same text\/image\/file shape at about one-fifth the API cost, a slightly larger listed context (1.05M vs 1M), tighter complete emails, and a practical redirect when a prompt is false.<\/p>\n

Best default for many SaaS teams:<\/strong> route volume and everyday loops to Sol. Keep Fable for Anthropic-style knowledge-work sessions you are willing to pay for. Do not crown a permanent overall winner.<\/p>\n

Data checked: 2026-08-24 via live side-by-side API tests. Prices change. Verify live vendor pages.<\/em><\/p>\n<\/div>\n\n

\n\n\n\n\n\n\n

1M<\/strong><\/p><\/td>\n

Claude Fable 5 context (API)<\/p><\/td>\n<\/tr>\n

1.05M<\/strong><\/p><\/td>\n

GPT-5.6 Sol context (API)<\/p><\/td>\n<\/tr>\n

$10 \/ $50<\/strong><\/p><\/td>\n

Claude Fable 5 input\/output per 1M tokens (API pricing, 2026-08-24)<\/p><\/td>\n<\/tr>\n

$2 \/ $10<\/strong><\/p><\/td>\n

GPT-5.6 Sol input\/output per 1M tokens (API pricing, 2026-08-24)<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n

\n\"Bar\n
Figure 1.<\/strong> Where each model wins on relative axes (context, modalities, output cost efficiency). Higher is stronger for that axis. Modalities match; Sol leads context slightly and cost efficiency clearly. Chart: i10X.<\/figcaption>\n<\/figure>\n\n
\n\n

Persona picker<\/h2>\n\n\n\n\n\n\n\n\n

You are\u2026<\/p><\/th>\n

Start with<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Writer \/ CS \/ marketer<\/p><\/td>\n

GPT-5.6 Sol (often); Fable for a fuller letter shell<\/p><\/td>\n

Sol\u2019s rewrite was complete and tight. Fable added a subject line, then truncated in our capture.<\/p><\/td>\n<\/tr>\n

Developer \/ agent builder<\/p><\/td>\n

Sol default; Fable when you want contract discussion<\/p><\/td>\n

Both fixed the empty-list bug. Fable discussed return 0 vs ValueError; Sol added a smoke print.<\/p><\/td>\n<\/tr>\n

Researcher \/ analyst<\/p><\/td>\n

Either (~1M class); Sol slight context edge<\/p><\/td>\n

Both sit in the million-token class with text, image, and file inputs. Sol lists 1,050,000 vs Fable 1,000,000.<\/p><\/td>\n<\/tr>\n

Budget \/ high volume API<\/p><\/td>\n

GPT-5.6 Sol<\/p><\/td>\n

Same modality shape, about 5x cheaper on our chat, repo, and cached-agent scenarios at current list rates.<\/p><\/td>\n<\/tr>\n

Anthropic-native knowledge work<\/p><\/td>\n

Claude Fable 5<\/p><\/td>\n

Vendor positions Fable as Mythos-class for autonomous knowledge work and coding. Pay the premium only if that product surface is the job.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\n

What we are comparing (exact versions)<\/h2>\n

Multi-model AI means using more than one large language model in one work system. This page compares two specific API models, not vague \u201cClaude vs ChatGPT\u201d brand names.<\/p>\n\n\n\n\n\n\n\n\n

Field<\/p><\/th>\n

Claude Fable 5<\/p><\/th>\n

GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

Provider<\/p><\/td>\n

Anthropic<\/p><\/td>\n

OpenAI<\/p><\/td>\n<\/tr>\n

API model<\/p><\/td>\n

Claude Fable 5<\/code><\/p><\/td>\n

GPT-5.6 Sol<\/code><\/p><\/td>\n<\/tr>\n

Listed API name<\/p><\/td>\n

Anthropic: Claude Fable 5<\/p><\/td>\n

OpenAI: GPT-5.6 Sol<\/p><\/td>\n<\/tr>\n

Family \/ tier<\/p><\/td>\n

Mythos-class; autonomous knowledge work and coding<\/p><\/td>\n

GPT-5.6 series flagship; reasoning, coding, agentic workflows<\/p><\/td>\n<\/tr>\n

App vs API note<\/p><\/td>\n

Also in Claude products; this article uses the API model above<\/p><\/td>\n

Also in ChatGPT \/ OpenAI API; this article uses the API model above<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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\nAI model routing<\/a>.<\/p>\n\n

\n\n

Spec sheet (API, 2026-08-24)<\/h2>\n\n\n\n\n\n\n\n\n\n\n

Spec<\/p><\/th>\n

Claude Fable 5<\/p><\/th>\n

GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

Context window<\/p><\/td>\n

1,000,000 tokens<\/p><\/td>\n

1,050,000 tokens<\/p><\/td>\n<\/tr>\n

Max output (if published)<\/p><\/td>\n

Not published on the card we pulled<\/p><\/td>\n

Not published on the card we pulled<\/p><\/td>\n<\/tr>\n

Input modalities (card)<\/p><\/td>\n

text, image, file<\/p><\/td>\n

file, image, text<\/p><\/td>\n<\/tr>\n

Output<\/p><\/td>\n

text<\/p><\/td>\n

text<\/p><\/td>\n<\/tr>\n

Reasoning<\/p><\/td>\n

Vendor copy mentions reasoning support<\/p><\/td>\n

Vendor copy highlights complex reasoning and agentic workflows<\/p><\/td>\n<\/tr>\n

Open weights<\/p><\/td>\n

Not listed as open weights on the card we pulled<\/p><\/td>\n

Not listed as open weights on the card we pulled<\/p><\/td>\n<\/tr>\n

Vendor positioning (short)<\/p><\/td>\n

Mythos-class for autonomous knowledge work and coding<\/p><\/td>\n

Flagship for reasoning, coding, agents; strong on command-line and multi-step coding<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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.<\/p>\n\n

\n\n

Pricing and real workload cost<\/h2>\n

List prices are easy to misread. Workload cost is what you feel. Rates below are from published API pricing on 2026-08-24<\/strong>.<\/p>\n\n\n\n\n\n\n

Price<\/p><\/th>\n

Claude Fable 5<\/p><\/th>\n

GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

Input \/ 1M tokens<\/p><\/td>\n

$10.00<\/p><\/td>\n

$2.00<\/p><\/td>\n<\/tr>\n

Output \/ 1M tokens<\/p><\/td>\n

$50.00<\/p><\/td>\n

$10.00<\/p><\/td>\n<\/tr>\n

Cache read \/ 1M<\/p><\/td>\n

$1.00<\/p><\/td>\n

$0.20<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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

Scenario<\/p><\/th>\n

Assumed tokens<\/p><\/th>\n

Est. Claude Fable 5<\/p><\/th>\n

Est. GPT-5.6 Sol<\/p><\/th>\n<\/tr>\n

Chat turn<\/p><\/td>\n

1k in + 0.5k out<\/p><\/td>\n

$0.035<\/p><\/td>\n

$0.007<\/p><\/td>\n<\/tr>\n

Repo \/ doc review<\/p><\/td>\n

80k in + 4k out<\/p><\/td>\n

$1.00<\/p><\/td>\n

$0.20<\/p><\/td>\n<\/tr>\n

Agent loop<\/p><\/td>\n

200k in (50% cached) + 20k out<\/p><\/td>\n

$2.10<\/p><\/td>\n

$0.42<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n\"Bar\n
Figure 2.<\/strong> Estimated USD per run using published API list rates (2026-08-24). Sol is about 5x cheaper on each stylized workload. Chart: i10X.<\/figcaption>\n<\/figure>\n

That 5x gap compounds on cached agent loops. For subscription math across Claude \/ ChatGPT style plans, see\nAI subscription stack cost<\/a>. Always verify live vendor pages before budgeting.<\/p>\n\n

\n\n

Performance by job (specs + live pack)<\/h2>\n

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:\nside-by-side AI comparison<\/a>.<\/p>\n\n

Coding and agents<\/h3>\n

OpenAI positions GPT-5.6 Sol for complex reasoning, coding, and agentic workflows, \u201cparticularly strong at command-line and multi-step coding tasks.\u201d 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([]))<\/code>. Use Sol as the cheaper default loop; bring Fable in for contract discussion or Anthropic-native sessions.<\/p>\n\n

Writing and tone<\/h3>\n

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\u2019s 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.<\/p>\n\n

Research, math, reasoning<\/h3>\n

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.<\/p>\n\n

Multimodal and long context<\/h3>\n

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.<\/p>\n\n

Speed<\/h3>\n

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.<\/p>\n\n\n\n\n\n\n\n\n\n

Job<\/p><\/th>\n

Edge<\/p><\/th>\n

Why<\/p><\/th>\n<\/tr>\n

Hard coding \/ agents<\/p><\/td>\n

Split (test your repo)<\/p><\/td>\n

Vendor copy differs; our micro-fix was a tie<\/p><\/td>\n<\/tr>\n

Everyday writing<\/p><\/td>\n

GPT-5.6 Sol (often)<\/p><\/td>\n

Complete, tight ops email in our capture; Fable had the subject line<\/p><\/td>\n<\/tr>\n

Long docs \/ files \/ images<\/p><\/td>\n

Near tie \/ slight Sol<\/p><\/td>\n

Same modality set; Sol 1.05M vs Fable 1M<\/p><\/td>\n<\/tr>\n

False-premise handling<\/p><\/td>\n

Split (style)<\/p><\/td>\n

Both refuse; Fable explains, Sol plans the real resource path<\/p><\/td>\n<\/tr>\n

Cost at volume<\/p><\/td>\n

GPT-5.6 Sol<\/p><\/td>\n

About 5x cheaper on chat, repo, and cached agent loops<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\nHow to read this<\/strong>\n

Do not crown a permanent winner from one email, one Python guard, and one folklore trap. When confidence is low, re-test on your prompts. Method:\nside-by-side AI comparison<\/a>.<\/p>\n<\/div>\n\n

\n\n

Side-by-side test (live API test, 2026-08-24)<\/h2>\n

We ran the same prompts on Claude Fable 5<\/code> and GPT-5.6 Sol<\/code> 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.<\/p>\n\n

Test 1: Client email rewrite<\/h3>\n

Task:<\/strong> Keep every fact. Warmer. Under 120 words.<\/p>\n

Claude Fable 5 (excerpt):<\/strong> Opened with Subject: Q3 Deck Update & Proposed Meeting Change<\/em>, then \u201cHi [Name],\u201d follow-up on last Tuesday\u2019s 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 \u201cwe\u2019ll be updating the com.\u201d<\/p>\n

GPT-5.6 Sol (excerpt):<\/strong> \u201cHi [Name],\u201d same Q3 follow-up, Finance numbers expected Friday and still missing, ask to move to next Wednesday if that works, plus \u201cWe\u2019ll also need to update the competitive slide with the new Acme pricing we found. Thanks!\u201d<\/p>\n

Edge:<\/strong> 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.<\/p>\n\n

Test 2: Empty-list average bug<\/h3>\n

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

Test 3: False premise (Moon cheese)<\/h3>\n

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.<\/strong> Sol is more operational; Fable is more explanatory. For publishable claims, still add a second-model check:\nmulti-model hallucination checks<\/a>.<\/p>\n\n\n\n\n\n\n\n\n

Prompt<\/p><\/th>\n

Claude Fable 5<\/p><\/th>\n

GPT-5.6 Sol<\/p><\/th>\n

Note<\/p><\/th>\n<\/tr>\n

Email rewrite<\/p><\/td>\n

22\/25<\/p><\/td>\n

23\/25<\/p><\/td>\n

Fable: subject line, truncated capture. Sol: complete, names Acme<\/p><\/td>\n<\/tr>\n

Bug fix<\/p><\/td>\n

24\/25<\/p><\/td>\n

24\/25<\/p><\/td>\n

Tie; Fable discusses contract, Sol adds a print<\/p><\/td>\n<\/tr>\n

False premise<\/p><\/td>\n

23\/25<\/p><\/td>\n

24\/25<\/p><\/td>\n

Both refuse; Sol more practical, Fable more topical<\/p><\/td>\n<\/tr>\n

Total (this pack)<\/strong><\/p><\/td>\n

69\/75<\/strong><\/p><\/td>\n

71\/75<\/strong><\/p><\/td>\n

Close; jobs still split. Cost is not close.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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.<\/p>\n\n

\n\n

Ecosystem and where you run them<\/h2>\n