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Claude Fable 5 vs SpaceXAI: Grok 4.6

Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Claude Fable 5 scores higher, SpaceXAI: Grok 4.6 costs less — it depends on your workload.

Claude Fable 5 is ahead by 4.9 points overall, and SpaceXAI: Grok 4.6 lists 6.7× cheaper per blended million tokens. Whether 4.9 points is worth that depends on how much a wrong answer costs you. SpaceXAI: Grok 4.6 also leads on measured cost per point of capability, at $0.1181 per point.

anthropic

Claude Fable 5

Blended / 1M
$20.00
Context
1M
Released
Jun 9, 2026
Overall score
83.0
reasoningtool callingimage inputfile inputprompt caching

x-ai

SpaceXAI: Grok 4.6

Blended / 1M
$3.00
Context
500K
Released
Aug 12, 2026
Overall score
78.0
reasoningtool callingimage inputfile inputprompt caching

Specs and pricing

MetricClaude Fable 5SpaceXAI: Grok 4.6
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

83.0win78.0
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.7925$0.1181win
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$20.00$3.00win
Input price / 1M$10.00$2.00win
Output price / 1M$50.00$6.00win
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$1.00$0.500win
Context window1Mwin500K
Max output tokens128K

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Fable 5 on top, SpaceXAI: Grok 4.6 below, both out of 100.

Agentic coding
62.2
57.0
Coding
86.0
76.8
Reasoningtoo close to call
89.7
90.5
Mathematics
96.0
92.6
Data analysis
80.5
73.9
Language
90.7
83.7
Instruction following
75.8
71.9

What each one costs to run

Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.

WorkloadClaude Fable 5SpaceXAI: Grok 4.6
Support chatbot

1.2K in / 400 out × 200K requests

$5752.00/mo$852.00/mo
RAG assistant

8K in / 600 out × 100K requests

$7400.00/mo$1360.00/mo
Coding agent

40K in / 4K out × 20K requests

$6960.00/mo$1240.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$13,300/mo$2375.00/mo
Bulk classification

500 in / 20 out × 5M requests

$25,500/mo$4850.00/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

SpaceXAI: Grok 4.6

Lowest measured cost per point of capability at $0.1181 per point — the gap compounds with every request.

Quality matters more than the bill

Claude Fable 5

Highest overall LiveBench score of the two at 83.0.

The workload is coding or agentic work

Claude Fable 5

Leads on agentic coding — 62.2 against 57.0.

You need to fit large documents in one call

Claude Fable 5

Wider context window — 1M against 500K.

Claude Fable 5 vs SpaceXAI: Grok 4.6 FAQ

Which is better, Claude Fable 5 or SpaceXAI: Grok 4.6?

Claude Fable 5 scores higher, SpaceXAI: Grok 4.6 costs less — it depends on your workload. Claude Fable 5 is ahead by 4.9 points overall, and SpaceXAI: Grok 4.6 lists 6.7× cheaper per blended million tokens. Whether 4.9 points is worth that depends on how much a wrong answer costs you. SpaceXAI: Grok 4.6 also leads on measured cost per point of capability, at $0.1181 per point.

Is Claude Fable 5 cheaper than SpaceXAI: Grok 4.6?

SpaceXAI: Grok 4.6 is cheaper. On a 3:1 input:output blend, Claude Fable 5 lists at $20.00 per million tokens and SpaceXAI: Grok 4.6 at $3.00 — SpaceXAI: Grok 4.6 is 6.7× cheaper. Input and output are priced separately — Claude Fable 5 charges $10.00 in and $50.00 out, SpaceXAI: Grok 4.6 charges $2.00 and $6.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Claude Fable 5 vs SpaceXAI: Grok 4.6: which scores higher on benchmarks?

Claude Fable 5 scores 83.0 and SpaceXAI: Grok 4.6 scores 78.0 overall on LiveBench, the mean of its seven categories. That is a 4.9-point lead for Claude Fable 5. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.

Which gives better value for money, Claude Fable 5 or SpaceXAI: Grok 4.6?

SpaceXAI: Grok 4.6. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Claude Fable 5 works out at $0.7925 per point and SpaceXAI: Grok 4.6 at $0.1181.

Does Claude Fable 5 or SpaceXAI: Grok 4.6 have a bigger context window?

Claude Fable 5 has the larger context window: 1M for Claude Fable 5 against 500K for SpaceXAI: Grok 4.6. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.

Do Claude Fable 5 and SpaceXAI: Grok 4.6 support prompt caching?

Both publish a cached-input rate: $1.00 per million for Claude Fable 5 and $0.500 for SpaceXAI: Grok 4.6, against full input rates of $10.00 and $2.00. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.

Related comparisons

How these numbers are produced

  • Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
  • ScoresLiveBench release 2026-06-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
  • Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
  • “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.

Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.