Claude Opus 5.5 vs Fable 5.1: is Fable still worth the premium?

TL;DR Anthropic says Opus 5.5 performs at the level of Fable 5.1 on most tasks, and its launch table has Opus 5.5 ahead on every shared row, from Terminal-Bench 4.0 (66.4% vs 55.8%) to Humanity’s Last Exam (67.7% vs 65.6%). Fable 5.1 lists at $10/$50, Opus 5.5 at $4/$20. Start with Opus 5.5; keep Fable 5.1 for the hardest reasoning where you have measured it winning. On apimodels.app: Opus 5.5 $2.40/$12, Fable 5.1 $5/$25.

Fable 5.1 was Anthropic’s top model until Opus 5.5 arrived on 22 September 2026 with a claim that is unusual for a more affordable model: roughly Fable-level results on most work. The launch table backs it up on every benchmark the two share, so the real question is narrower — which tasks, if any, still justify paying about two and a half times more.

Benchmarks from Anthropic’s Opus 5.5 launch

The widest gaps are in agentic coding and automation (Terminal-Bench 4.0 +10.6 points, AutomationBench +8.6). On chart reading and hard exams the two are within about two points, which is where Fable 5.1’s deeper default reasoning keeps it competitive.

Benchmark	Opus 5.5	Fable 5.1	Opus 5
Terminal-Bench 4.0	66.4%	55.8%	52.3%
FrontierCode 1.1	54.4%	50.3%	48.0%
CursorBench 4.0	57.8%	51.8%	46.6%
GDPval-AA v2.1	1846	1735	1708
AutomationBench	40.0%	31.4%	26.9%
Chartography	89.0%	88.4%	83.4%
Humanity’s Last Exam	67.7%	65.6%	63.6%
Terminal-Bench-Science	58.7%	52.6%	29.0%

Differences that matter in practice

Price: Fable 5.1 lists at $10 / $50 per million tokens, Opus 5.5 at $4 / $20. On apimodels.app they are $5 / $25 and $2.40 / $12, so a job that costs $1 on Opus 5.5 costs about $2.08 on Fable 5.1 before counting that Fable also tends to write more reasoning.

Default effort: Opus 5.5 defaults to medium effort, Fable 5.1 to high. Part of Fable’s edge on the hardest problems is simply that it thinks longer by default; raising Opus 5.5 to high or xhigh closes much of that gap for less money. Opus 5.5 cannot turn thinking off.

Thinking blocks across models: Fable 5.1 can read Opus 5.5 thinking blocks, but not the other way round. If you escalate a conversation from Opus 5.5 to Fable 5.1 it works; going back down means dropping Fable’s thinking blocks.

Início rápido

cURL

curl https://api.apimodels.app/v1/messages \
  -H "x-api-key: $APIMODELS_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-opus-5-5",
    "max_tokens": 16000,
    "messages": [{"role": "user", "content": "Review this function for race conditions: ..."}]
  }'

Python

from openai import OpenAI

client = OpenAI(base_url="https://api.apimodels.app/v1", api_key="YOUR_APIMODELS_KEY")

# Same prompt, two models, one key: compare them on your own task before you commit.
for model in ["claude-opus-5-5", "claude-fable-5-1"]:
    r = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "Refactor this module and explain the risky parts: ..."}],
    )
    print(model, r.usage.prompt_tokens, r.usage.completion_tokens)
    print(r.choices[0].message.content[:400])

Perguntas frequentes

Is Opus 5.5 better than Fable 5.1?

On every benchmark in Anthropic’s Opus 5.5 launch table, yes, by margins from 0.6 points (Chartography) to 10.6 points (Terminal-Bench 4.0). Anthropic’s own wording is that Opus 5.5 performs at the level of Fable 5.1 on most tasks.

When should I still use Fable 5.1?

When you have measured it winning on your own hardest reasoning tasks and the extra cost is small next to the cost of a wrong answer. Try Opus 5.5 at high or xhigh effort first; much of Fable’s edge comes from its higher default effort.

How much do they cost on apimodels.app?

Opus 5.5 is $2.40 / $12 per million tokens (Anthropic list $4 / $20); Fable 5.1 is $5 / $25 (list $10 / $50). Both are billed per token on the same key, and failed calls are free.

Do both have a 1M-token context?

Yes. Both support a 1M-token context window and up to 128,000 output tokens per response, with a June 2026 knowledge cutoff.

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