// head_to_head
Claude Fable 5.2 vs GPT-6 Astra
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.
Effectively the same quality — Claude Fable 5.2 is the cheaper way to get it.
The two are within 0.4 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Claude Fable 5.2 lists 25% cheaper per blended million tokens. When quality ties, cost is the whole decision. The two cost measures disagree here, which is worth knowing: Claude Fable 5.2 has the lower sticker price, but GPT-6 Astra earns each point of capability for less — $0.0532 against $0.0985 — because per-token rates do not predict how many tokens a model actually spends on a task.
anthropic
Claude Fable 5.2
- Blended / 1M
- $16.00
- Context
- 1M
- Released
- Nov 9, 2026
- Overall score
- 84.9
openai
GPT-6 Astra
- Blended / 1M
- $20.00
- Context
- 1.1M
- Released
- Sep 4, 2026
- Overall score
- 84.5
Specs and pricing
| Metric | Claude Fable 5.2 | GPT-6 Astra |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 84.9 | 84.5 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0985 | $0.0532win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $16.00win | $20.00 |
| Input price / 1M | $8.00win | $10.00 |
| Output price / 1M | $40.00win | $50.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.200win | $1.00 |
| Context window | 1M | 1.1M |
| Max output tokens | 128K | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Claude Fable 5.2 on top, GPT-6 Astra below, both out of 100.
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.
| Workload | Claude Fable 5.2 | GPT-6 Astra |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $4,558/mo | $5,752/mo |
| RAG assistant 8K in / 600 out × 100K requests | $5,680/mo | $7,400/mo |
| Coding agent 40K in / 4K out × 20K requests | $5,232/mo | $6,960/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $10,610/mo | $13,300/mo |
| Bulk classification 500 in / 20 out × 5M requests | $20,100/mo | $25,500/mo |
Which should you pick?
You are running this at volume
GPT-6 Astra
Lowest measured cost per point of capability at $0.0532 per point — the gap compounds with every request.
The workload is coding or agentic work
Claude Fable 5.2
Leads on long-horizon agentic — 82.9 against 75.6.
Claude Fable 5.2 vs GPT-6 Astra FAQ
Which is better, Claude Fable 5.2 or GPT-6 Astra?
Effectively the same quality — Claude Fable 5.2 is the cheaper way to get it. The two are within 0.4 points overall, which is inside the range that effort settings alone move a LiveBench score, so treat them as quality-equivalent. Claude Fable 5.2 lists 25% cheaper per blended million tokens. When quality ties, cost is the whole decision. The two cost measures disagree here, which is worth knowing: Claude Fable 5.2 has the lower sticker price, but GPT-6 Astra earns each point of capability for less — $0.0532 against $0.0985 — because per-token rates do not predict how many tokens a model actually spends on a task.
Is Claude Fable 5.2 cheaper than GPT-6 Astra?
Claude Fable 5.2 is cheaper. On a 3:1 input:output blend, Claude Fable 5.2 lists at $16.00 per million tokens and GPT-6 Astra at $20.00 — Claude Fable 5.2 is 25% cheaper. Input and output are priced separately — Claude Fable 5.2 charges $8.00 in and $40.00 out, GPT-6 Astra charges $10.00 and $50.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Claude Fable 5.2 vs GPT-6 Astra: which scores higher on benchmarks?
Claude Fable 5.2 scores 84.9 and GPT-6 Astra scores 84.5 overall on LiveBench, the weighted mean of its seven categories. That gap is inside the range that effort settings alone move a score, so treat them as equivalent on published quality. 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 5.2 or GPT-6 Astra?
GPT-6 Astra. 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 5.2 works out at $0.0985 per point and GPT-6 Astra at $0.0532.
Does Claude Fable 5.2 or GPT-6 Astra have a bigger context window?
They are effectively the same — 1M for Claude Fable 5.2 and 1.1M for GPT-6 Astra.
Do Claude Fable 5.2 and GPT-6 Astra support prompt caching?
Both publish a cached-input rate: $0.200 per million for Claude Fable 5.2 and $1.00 for GPT-6 Astra, against full input rates of $8.00 and $10.00. 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.
- Scores — LiveBench release 2026-11-06, 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.