{"id":511,"date":"2026-08-25T06:26:05","date_gmt":"2026-08-25T06:26:05","guid":{"rendered":"https:\/\/i10x.ai\/blog\/?p=511"},"modified":"2026-08-25T06:31:02","modified_gmt":"2026-08-25T06:31:02","slug":"grok-4-6-vs-claude-fable-5","status":"publish","type":"post","link":"https:\/\/i10x.ai\/blog\/grok-4-6-vs-claude-fable-5","title":{"rendered":"Grok 4.6 vs Claude Fable 5: Specs, Price, Side-by-Side Tests (2026)"},"content":{"rendered":"\n
\n\n

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

\nGrok 4.6 and Claude Fable 5 sit in the same \u201cfrontier knowledge work\u201d conversation and almost nowhere near the same price. This is a decision guide, not a leaderboard dump: exact API cards, three workload cost scenarios, live writing\/coding\/false-premise snippets, and a routing matrix you can rerun. 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 Grok 4.6 if:<\/strong> you want a cheaper frontier default for everyday coding, email, and agent loops, with matched text\/image\/file input and a 500K context window.<\/p>\n

Pick Claude Fable 5 if:<\/strong> you need Mythos-class Anthropic depth, a 1M context window, and you can absorb roughly 5\u00d7 input and 8\u00d7 output list rates for autonomous knowledge work.<\/p>\n

Best default for many SaaS teams:<\/strong> route by task. Use Grok as the volume worker. Reserve Fable for the jobs that justify the bill. Do not crown a permanent overall winner.<\/p>\n

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

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

500K<\/strong><\/p><\/td>\n

Grok 4.6 context (API)<\/p><\/td>\n<\/tr>\n

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

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

$2 \/ $6<\/strong><\/p><\/td>\n

Grok 4.6 input\/output per 1M tokens (API pricing, 2026-08-24)<\/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<\/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; Fable leads context; Grok leads output-cost efficiency. 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

Grok 4.6 for volume; Fable for high-stakes prose<\/p><\/td>\n

Grok kept every operational fact. Fable added a subject line and warmer meeting language.<\/p><\/td>\n<\/tr>\n

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

Grok 4.6 default; Fable for hard knowledge-work loops<\/p><\/td>\n

Both named ZeroDivisionError<\/code> and the same guard. Fable discussed return-0 vs ValueError<\/code>. Cost favors Grok.<\/p><\/td>\n<\/tr>\n

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

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

1M context vs 500K and Mythos-class positioning. Pay the premium when the pack is huge.<\/p><\/td>\n<\/tr>\n

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

Grok 4.6<\/p><\/td>\n

Chat, repo, and agent estimates are all several times cheaper at current list rates. An agent loop landed at $0.370 vs $2.10.<\/p><\/td>\n<\/tr>\n

Vision \/ files<\/p><\/td>\n

Split (same card modalities)<\/p><\/td>\n

Both list text, image, and file in, text out. Pick on quality and cost, not on a modality gap.<\/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 \u201cGrok vs Claude\u201d brand names and not a consumer-app bake-off.<\/p>\n\n\n\n\n\n\n\n\n

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

Grok 4.6<\/p><\/th>\n

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

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

xAI (listed as SpaceXAI on the API card)<\/p><\/td>\n

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

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

Grok 4.6<\/code><\/p><\/td>\n

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

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

SpaceXAI: Grok 4.6<\/p><\/td>\n

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

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

Frontier Grok flagship (coding, knowledge work, STEM)<\/p><\/td>\n

Mythos-class Claude (autonomous knowledge work and coding)<\/p><\/td>\n<\/tr>\n

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

Also in xAI \/ X products; this article uses the API card above<\/p><\/td>\n

Also in Claude apps \/ Anthropic plans; this article uses the API card above<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

If a page still compares older Grok 4 or Claude Opus labels without the 4.6 \/ Fable 5 IDs, treat it as historical. For routing across many models, see\nAI model routing<\/a>.\nSibling pair:\nGrok 4.6 vs Gemini 3.1 Pro<\/a>.<\/p>\n\n

\n\n

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

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

Grok 4.6<\/p><\/th>\n

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

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

500,000 tokens<\/p><\/td>\n

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

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

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

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

Input modalities<\/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

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

Not specified on this API card<\/p><\/td>\n

Reasoning support (listed on the card description)<\/p><\/td>\n<\/tr>\n

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

Not listed as open weights on this API card<\/p><\/td>\n

Not listed as open weights on this API card<\/p><\/td>\n<\/tr>\n

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

Smartest Grok model; frontier coding, knowledge work, and STEM<\/p><\/td>\n

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

The structural split is context and price, not modality. Both take text, image, and file. Fable doubles the context window (1M vs 500K) and prices like a premium autonomous worker. Grok prices like a high-volume frontier default.<\/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>. Verify live<\/strong> before you budget.<\/p>\n\n\n\n\n\n\n

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

Grok 4.6<\/p><\/th>\n

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

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

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

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

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

$6.00<\/p><\/td>\n

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

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

$0.50<\/p><\/td>\n

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

Fable is 5\u00d7 on input, about 8.3\u00d7 on output, and 2\u00d7 on cache reads. That is not a rounding error. It is a different product lane.<\/p>\n\n\n\n\n\n\n

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

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

Est. Grok 4.6<\/p><\/th>\n

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

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

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

$0.005<\/p><\/td>\n

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

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

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

$0.184<\/p><\/td>\n

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

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

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

$0.370<\/p><\/td>\n

$2.10<\/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). Chat $0.005 vs $0.035; repo $0.184 vs $1.00; agent $0.370 vs $2.10. Chart: i10X.<\/figcaption>\n<\/figure>\n

On the stylized agent loop, Fable costs about 5.7\u00d7 Grok. That compounds across a week of tool calls. For subscription math across consumer plans, see\nAI subscription stack cost<\/a>.\nAlways verify live vendor pages before budgeting.<\/p>\n\n

\n\n

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

We are not pasting third-party leaderboard numbers here. Public benches disagree by harness, effort mode, and date. This page uses the API cards, the three workload costs, and the live snippets below. Prefer your own side-by-side on your prompts. Method:\nside-by-side AI comparison<\/a>.<\/p>\n\n

Coding and agents<\/h3>\n

Vendor copy puts both in the coding and knowledge-work lane. Our empty-list micro-test was a near tie on correctness. Fable added a contract note (return 0 vs raise ValueError<\/code>). For agent loops at volume, the $0.370 vs $2.10 estimate is the louder signal until you measure your own tool traces.<\/p>\n\n

Writing and tone<\/h3>\n

Benchmarks barely measure voice. In the rewrite, Grok stayed tight and complete: Q3 deck from last Tuesday, finance still missing after Friday, Wednesday stakeholder ask, Acme pricing on the competitive slide. Fable opened with a subject line and softer \u201cwould you be open to moving\u201d language, then our capture cut mid-sentence. Edge: Grok for fidelity; Fable for polished meeting tone when the full letter lands.<\/p>\n\n

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

Fable\u2019s card lists reasoning support and a 1M window. Grok\u2019s card emphasizes STEM and knowledge work at 500K. On the false-premise prompt, both refused first. Grok was short: rock, not cheese, so no mining plan exists. Fable added composition detail and a redirect to real lunar resources. Neither invented a cheese-mining flowchart.<\/p>\n\n

Multimodal and long context<\/h3>\n

Modalities match (text, image, file). Context does not: 1M vs 500K. If analysts paste 200-page decks, Fable is the structural primary. If the job is a screenshot plus a short ticket, either can take the image; pick on quality and cost. We did not run a vision eval in this pack, so do not treat Figure 1\u2019s matched-modality bar as a quality claim.<\/p>\n\n

Speed<\/h3>\n

This pack did not measure tokens per second or time-to-first-token. Do not ship a latency SLO from someone else\u2019s screenshot. Measure p50 from your region in the same workspace you will productionize.<\/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: quality near-tie, cost Grok<\/p><\/td>\n

Same bug diagnosis in our micro-test; Fable slightly more thorough; agent-loop cost favors Grok<\/p><\/td>\n<\/tr>\n

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

Grok 4.6 (this run)<\/p><\/td>\n

Complete facts, no filler; Fable warmer but truncated in capture<\/p><\/td>\n<\/tr>\n

Long docs \/ 1M packs<\/p><\/td>\n

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

1M vs 500K context<\/p><\/td>\n<\/tr>\n

Image + file input<\/p><\/td>\n

Split<\/p><\/td>\n

Same listed modalities; test on your screenshots<\/p><\/td>\n<\/tr>\n

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

Grok 4.6<\/p><\/td>\n

$2\/$6 vs $10\/$50; agent loop $0.370 vs $2.10<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

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

When two models share modalities and both pass a micro-test, cost and context become the routing keys. Re-test on your repo and your brand voice. 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 three prompts on Grok 4.6<\/code> and Claude Fable 5<\/code> side by side. Scores are editorial 1-5 across instruction following, depth, factual caution, style, and usefulness (max 25 per prompt). This batch pack is three prompts, not five. Re-run a longer pack (long-paste summary, refuse-if-unknown research) on your own material.<\/p>\n\n

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

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

Grok 4.6 (excerpt):<\/strong> Direct follow-up: Q3 deck from last Tuesday; finance still missing after the Friday promise; push stakeholders to next week, perhaps Wednesday; competitive slide needs Acme pricing. Short close. No invented cheer.<\/p>\n

Claude Fable 5 (excerpt):<\/strong> Subject line (\u201cQ3 Deck Update & Proposed Meeting Change\u201d), name placeholder, \u201cwould you be open to moving\u201d the meeting, then a competitive-slide note that our capture cut mid-sentence.<\/p>\n

Edge:<\/strong> Grok for completeness and brevity. Fable for polished meeting register. If your brand voice hates filler, prefer Grok. If managers want a subject line and softer ask, prefer Fable and check the full output.<\/p>\n\n

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

Both named ZeroDivisionError<\/code> when len(nums)<\/code> is 0 and proposed if not nums: return 0<\/code> before dividing. Fable also noted that raising ValueError(\"empty list\")<\/code> may match the intended contract better. Near tie<\/strong> on this micro-task; Fable slightly more thorough.<\/p>\n\n

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

Both refused the premise first. Grok: the Moon is a rocky silicate body, so no cheese or protein to mine, and no mining plan exists. Fable: same refusal, plus Apollo-sample composition and a redirect to real lunar extraction topics. Both pass<\/strong>. Grok more concise. Fable more pedagogical. 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

Grok 4.6<\/p><\/th>\n

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

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

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

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

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

Grok complete; Fable warmer, capture truncated<\/p><\/td>\n<\/tr>\n

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

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

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

Both correct; Fable discusses contract<\/p><\/td>\n<\/tr>\n

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

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

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

Both refuse; Fable adds real-resource redirect<\/p><\/td>\n<\/tr>\n

Total (3-prompt pack)<\/strong><\/p><\/td>\n

70\/75<\/strong><\/p><\/td>\n

70\/75<\/strong><\/p><\/td>\n

Tie on this pack; jobs still split on cost and context<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

A tied micro-pack is the point of routing. Quality is close enough that you should not pay Fable rates for every chat turn, and you should not force 500K Grok to swallow a 1M diligence pack.<\/p>\n\n

\n\n

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