{"id":917,"date":"2026-09-17T07:26:24","date_gmt":"2026-09-17T07:26:24","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=917"},"modified":"2026-09-28T07:23:12","modified_gmt":"2026-09-28T07:23:12","slug":"claude-fable-5-1-vs-gpt-6-astra","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/claude-fable-5-1-vs-gpt-6-astra","title":{"rendered":"Claude Fable 5.1 vs GPT-6 Astra: Benchmarks, Price & Which to Pick (2026)"},"content":{"rendered":"\n\n\n
\n\n

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

\nClaude Fable 5.1 (Anthropic API) and GPT-6 Astra (OpenAI API) are two flagship chat models teams actually route in 2026. This is a decision guide, not a leaderboard dump: published API rates, three workload cost scenarios, live writing and coding snippets, and a pick matrix you can rerun. If you want both without juggling tabs, use a\nmulti-model AI workspace<\/a>\nor start on\ni10X<\/a>.\n<\/p>\n\n

\nQuick verdict<\/strong>\n

Pick Claude Fable 5.1 if:<\/strong> you want Anthropic\u2019s agentic-coding and long-running workflow positioning, warmer client email with name slots, cheaper cache reads ($0.25 vs $1.00), and a slightly cheaper cached agent loop on matched $10\/$50 list rates.<\/p>\n

Pick GPT-6 Astra if:<\/strong> you need a slightly larger window (1.05M vs 1M), OpenAI\u2019s flagship stack, or a coding style that prefers explicit ValueError guards over returning zero.<\/p>\n

Best default for many teams:<\/strong> route by vendor stack and tone. Our three-prompt pack tied at 69\/75. List input\/output prices match; cache and card focus still split the week.<\/p>\n

Data checked: 2026-09-07. Prices and model cards change. Verify live API and vendor pages.<\/em><\/p>\n<\/div>\n\n

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

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

Claude Fable 5.1 context (API)<\/p><\/td>\n<\/tr>\n

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

GPT-6 Astra context (API)<\/p><\/td>\n<\/tr>\n

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

Claude Fable 5.1 input\/output per 1M tokens (API pricing, 2026-09-07)<\/p><\/td>\n<\/tr>\n

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

GPT-6 Astra input\/output per 1M tokens (API pricing, 2026-09-07)<\/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, cache-read price, writing warmth, coding micro-test, agent positioning). Higher is stronger for that axis. Chart: i10X.<\/figcaption>\n<\/figure>\n\n
\n

Persona picker<\/h2>\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 \/ marketer<\/p><\/td>\n

Claude Fable 5.1 (often)<\/p><\/td>\n

Both scored 23\/25. Fable used name slots and a warm week greeting. Astra was shorter and tighter.<\/p><\/td>\n<\/tr>\n

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

Fable for Anthropic agents; Astra for OpenAI stacks<\/p><\/td>\n

Both scored 24\/25. Fable card stresses agentic coding and long-running workflows. Empty-list fix philosophy differs.<\/p><\/td>\n<\/tr>\n

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

Either; slight Astra on window<\/p><\/td>\n

1M vs 1.05M. Both refused the false premise cleanly and offered practical protein plans.<\/p><\/td>\n<\/tr>\n

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

Slight Fable on cache-heavy loops<\/p><\/td>\n

Matched $10\/$50 list rates. Fable cache $0.25 vs Astra $1.00. Agent recipe $2.025 vs $2.10.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

\n\n

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

This page compares two specific API models, not vague \u201cClaude vs GPT\u201d brands and not Claude Opus 5. See\nClaude Opus 5 vs GPT-6 Astra<\/a>\nand\nGrok 4.6 vs GPT-6 Astra<\/a>.<\/p>\n\n\n\n\n\n\n\n\n

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

Claude Fable 5.1<\/p><\/th>\n

GPT-6 Astra<\/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 5.1 (Anthropic API)<\/code><\/p><\/td>\n

GPT-6 Astra (OpenAI API)<\/code><\/p><\/td>\n<\/tr>\n

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

Anthropic: Claude Fable 5.1<\/p><\/td>\n

OpenAI: GPT-6 Astra<\/p><\/td>\n<\/tr>\n

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

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

GPT-6 series flagship<\/p><\/td>\n<\/tr>\n

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

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

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

Fable is not Opus. Do not reuse Opus prices or snippets for this page. For routing across many models, see\nAI model routing<\/a>.<\/p>\n\n

\n\n

Spec sheet (API card, 2026-09-07)<\/h2>\n\n\n\n\n\n\n\n\n

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

Claude Fable 5.1<\/p><\/th>\n

GPT-6 Astra<\/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

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

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

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

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

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

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

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

No<\/p><\/td>\n

No<\/p><\/td>\n<\/tr>\n

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

Improves on Claude Fable 5 across the board; biggest gains in agentic coding, long-running agentic workflows, and knowledge work (long refactors, front-end and visual work)<\/p><\/td>\n

OpenAI flagship for demanding end-to-end work; advanced analysis, software engineering, deep research, scientific work, and document creation, with long-horizon strengths<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

We did not invent max-output, reasoning-mode, or realtime-search rows. Those fields were not on the cards we pulled for this pair. If your product depends on a specific tool or effort switch, confirm it on the live vendor page before you ship.<\/p>\n\n

\n\n

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

List prices are easy to game. Workload cost is what you feel. Numbers below use published per-million rates as of 2026-09-07<\/strong>. Verify live<\/strong> before you budget.<\/p>\n\n\n\n\n\n\n

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

Claude Fable 5.1<\/p><\/th>\n

GPT-6 Astra<\/p><\/th>\n<\/tr>\n

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

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

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

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

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

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

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

$0.25<\/p><\/td>\n

$1.00<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Matched input and output stickers. Fable is 4\u00d7 cheaper on cache reads ($0.25 vs $1.00). If your agent stack actually hits cache, that gap shows up. If it does not, chat and repo cost match.<\/p>\n\n\n\n\n\n\n

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

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

Est. Claude Fable 5.1<\/p><\/th>\n

Est. GPT-6 Astra<\/p><\/th>\n<\/tr>\n

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

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

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

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

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

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

$1.000<\/p><\/td>\n

$1.000<\/p><\/td>\n<\/tr>\n

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

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

$2.025<\/p><\/td>\n

$2.100<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\n\"Bar\n
Figure 2.<\/strong> Estimated USD per run using published API list rates (2026-09-07). Chat is 1k in + 0.5k out. Repo review is 80k in + 4k out. Agent loop is 200k in with 50% cache hits + 20k out. Chart: i10X.<\/figcaption>\n<\/figure>\n

Chat and repo tie at $0.035 and $1.00. Fable edges the cached agent loop ($2.025 vs $2.10) on cheaper cache. Re-run the math if you cache harder. For cheaper Anthropic flagship rates nearby, see\nClaude Opus 5 vs GPT-6 Astra<\/a>.\nFor subscription stacks, see\nAI subscription stack cost<\/a>.<\/p>\n\n

\n\n

Performance by job (not one score)<\/h2>\n

Public benches disagree by harness, effort mode, and date. We are not inventing index numbers we do not have for this pair. Treat vendor cards as positioning, treat our snippets as a small live pack, and prefer your own side-by-side on your prompts. Method:\nside-by-side AI comparison<\/a>.<\/p>\n\n

Coding and agents<\/h3>\n

Anthropic\u2019s card aims Fable 5.1 at agentic coding, long-running agentic workflows, and knowledge work including long refactors and front-end or visual tasks. OpenAI\u2019s card frames Astra for demanding end-to-end software engineering and long-horizon work. Our micro-test tied at 24\/25: both named ZeroDivisionError<\/code>. Fable returned 0<\/code> (with a ValueError option noted). Astra raised a descriptive ValueError<\/code> first. Context is close (1M vs 1.05M). Pick by vendor agent stack more than by this snippet.<\/p>\n\n

Writing and tone<\/h3>\n

Both scored 23\/25. Fable wrote a warmer letter with name slots and a week greeting. Astra compressed the same facts into a shorter follow-up. If your brand wants send-ready warmth, Fable was closer. If you hate filler, Astra was tighter.<\/p>\n\n

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

We did not run a science-QA pack, so we will not fake one. Both refused the green-cheese Moon and scored 22\/25. Fable cited Apollo and Luna samples, then offered a practical protein plan with microbes, polar ice, and solar power. Astra refused, then sketched bioreactors and recycled water. Both pass. Fable was more detailed on the redirect. For publishable work, add\nmulti-model hallucination checks<\/a>.<\/p>\n\n

Multimodal and long context<\/h3>\n

Both cards list text, image, and file in, text out. No audio or video in this pull. Windows are close: 1M vs 1.05M. Neither is the audio\/video specialist. For multimodal reach, see\nGemini 3.1 Pro vs GPT-6 Astra<\/a>\nand\nKimi K3 vs GPT-6 Astra<\/a>.<\/p>\n\n

Speed<\/h3>\n

No tok\/s in this pack. Measure p50 from your API region. Do not ship on a third-party screenshot.<\/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 by stack<\/p><\/td>\n

Micro-test tie. Fable card: agentic coding and long-running workflows. Astra: long-horizon OpenAI flagship.<\/p><\/td>\n<\/tr>\n

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

Claude Fable 5.1 (warmth)<\/p><\/td>\n

Same 23\/25. Fable more client-ready length. Astra shorter.<\/p><\/td>\n<\/tr>\n

Long docs \/ multimodal<\/p><\/td>\n

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

1.05M vs 1M. Same text\/image\/file set.<\/p><\/td>\n<\/tr>\n

Cache-heavy agent loops<\/p><\/td>\n

Claude Fable 5.1<\/p><\/td>\n

Cache $0.25 vs $1.00. Small edge on our 50% cache recipe.<\/p><\/td>\n<\/tr>\n

Cost at volume (uncached)<\/p><\/td>\n

Tie<\/p><\/td>\n

Matched $10\/$50. Chat and repo estimates match.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

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

Leaderboards mix harnesses, tool settings, and effort modes. When we do not have a primary table for a pair, we do not invent one. Re-test on your workload.<\/p>\n<\/div>\n\n

\n\n

Side-by-side test (i10X pack, 2026-09-07)<\/h2>\n

Same three prompts, scored 1-5 on instruction following, depth, factual caution, style, and usefulness (max 25). Writing, coding, and false premise only. No invented extra scores.<\/p>\n\n

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

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

Claude Fable 5.1 (excerpt, sanitized):<\/strong> Name slot, week greeting, Q3 deck last Tuesday, finance still missing after Friday, Wednesday stakeholder ask, Acme pricing update, warm close.<\/p>\n

GPT-6 Astra (excerpt, sanitized):<\/strong> Same facts in a shorter follow-up, Finance capitalized, Wednesday ask, Acme line, \u201cThanks for your help!\u201d<\/p>\n

Edge:<\/strong> Soft tie on score (23\/25). Fable for warmth. Astra for brevity.<\/p>\n\n

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

Both named ZeroDivisionError<\/code> and scored 24\/25. Fable returned 0<\/code> by default and noted raising ValueError as an option, plus a sum\/len simplification note. Astra raised ValueError<\/code> first and offered None<\/code> as an alternate. Tie on quality; pick the contract you want.<\/strong><\/p>\n\n

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

Both refused first and scored 22\/25. Fable: silicate rock, Apollo and Luna samples, then a practical protein plan with microbes, polar ice, and solar. Astra: same refuse, then bioreactors and recycled water. Both pass.<\/strong> Fable more detailed on the redirect.<\/p>\n\n\n\n\n\n\n\n\n

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

Claude Fable 5.1<\/p><\/th>\n

GPT-6 Astra<\/p><\/th>\n

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

Client email rewrite<\/p><\/td>\n

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

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

Fable warmer. Astra tighter.<\/p><\/td>\n<\/tr>\n

Bug explain + minimal fix<\/p><\/td>\n

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

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

Both catch ZeroDivisionError. Return 0 vs raise ValueError.<\/p><\/td>\n<\/tr>\n

Logic + false premise<\/p><\/td>\n

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

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

Both refuse then redirect to practical protein plans.<\/p><\/td>\n<\/tr>\n

Total<\/strong><\/p><\/td>\n

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

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

Pack tie. Jobs still split on cache, tone, and vendor stack.<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

A tied pack with matched list rates means cache behavior, agent tooling, and brand voice decide the route.<\/p>\n\n

\n\n

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