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Claude Fable 5.1 vs GPT-5.6

Claude Fable 5.1 is served by invitation. Its figures below are the live rates, but calls need a workspace grant first; ask us for access before you build on this comparison.

vs

Which one, when

These two are close on shape - both take text and image in, return text, cap output at 128000 tokens, and offer roughly a million tokens of context (1000000 for claude-fable-5-1, 1050000 for gpt-5.6) - so the split is price and control. gpt-5.6 costs half as much on input ($5 vs $10) and about 1.67x less on output ($30 vs $50), and its thinking can be turned off, making it the default for high-volume or latency-sensitive work; claude-fable-5-1 always reasons and charges half as much on cache reads ($0.25 vs $0.5), so it suits prompt-heavy pipelines that want thinking on every call.

Benchmarks

LeadsAbove averageNo peer higherClaude Fable 5.177 / 77 / 7GPT-5.6090 / 10935 / 109

7 measured on both.

Claude Fable 5.1 GPT-5.6 other models measured peer average no peer scored higher
Terminal-bench 4.0
no peer scored higher 55.8%
37.3%
BioMysteryBench hard
N/A
44.7%
OSWorld-Verified
N/A
83%
Cybergym
N/A
83.6%
HealthBench
N/A
57%
GDPval-AA v2 Elo · 1711-1853 per Anthropic · 2026-09-05
no peer scored higher 1853
1711
Harvey Lab-AA
N/A
87.2%
Humanity's Last Exam no tools
no peer scored higher 60.9%
44.5%
AutomationBench
no peer scored higher 31.4%
18.1%
LVBench
N/A
82.1%

Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

Pricing

Claude Fable 5.1 GPT-5.6 Δ
Input / 1M tokens $10 $5
Output / 1M tokens $50 $30 1.7×
Cache read / 1M tokens $0.25 $0.5 0.5×
Cache write 1.25x (5m) / 2x (1h) no separate charge -

Rates from the live catalog at build time; each model page carries the current card.

Where they sit - input price per 1M tokens across all 67 chat models on this billing unit (log scale)

Claude Fable 5.1 · $10 GPT-5.6 · $5
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

Capabilities

Claude Fable 5.1 GPT-5.6
Tool use yes yes
Thinking control always on configurable
Structured output yes yes
Prompt caching explicit (you mark the prefix) implicit (automatic)
Cache lifetime 5m default, 1h option 5-10m, up to 1h
Minimum cached prefix 1024 tokens 1024 tokens

Specs

Claude Fable 5.1 GPT-5.6
Input modalities text image text image
Output modalities text text
Released 2026-09-01 2026-07-09
Knowledge cutoff 2026-06 2026-02
Context window 1M 1.1M
Max output 128K 128K
Thinking parameter output_config.effort (thinking is adaptive-only and always on) reasoning.effort
Accepted values
effort
  • low
  • medium
  • high
  • xhigh
  • max
reasoning.effort
  • none
  • low
  • medium
  • high
  • xhigh
  • max
Default

thinking always on (adaptive)

effort
  • high
medium

Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: Claude Fable 5.1 · GPT-5.6

Switch between them with one line

Both ids are in every tab below - the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="claude-fable-5-1",
    # model="gpt-5.6",  # uncomment this line, comment the one above
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

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FAQ

Which is cheaper, Claude Fable 5.1 or GPT-5.6?

GPT-5.6 is cheaper on input / 1m tokens ($5 vs $10, 2.0× apart). Other rows may point the other way - the table above carries the full card, and real cost depends on your mix.

Can I A/B test Claude Fable 5.1 against GPT-5.6 without two integrations?

Yes. Both are served through the same OpenAI-compatible endpoint with one API key - switching is a one-line model-string change, so you can route a fraction of traffic to each and compare bills directly.

Do Claude Fable 5.1 and GPT-5.6 support prompt caching?

Yes - both bill cached reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.

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