Claude Opus 5.5 vs Fable 5.1 vs GPT-6 Astra for coding

Anthropic released Claude Opus 5.5 on September 22, 2026. OpenAI shipped GPT-6 Astra on September 3, and Anthropic released Claude Fable 5.1 earlier in the month.

This post compares the three for coding work. The benchmark figures come from Anthropic's announcement and migration guide, and the independent scores come from Artificial Analysis.

Price and limits

All three allow 128k output tokens per request. GPT-6 Astra charges $20 / $75 per million tokens once input goes past 272K tokens. Opus 5.5 costs 20% less per token than Opus 5, and 60% less than Fable 5.1 or Astra.

Coding benchmarks

These are Anthropic's published scores. Opus 5.5 was run at xhigh effort. Anthropic lists no CursorBench 4.0 score for Astra.

Opus 5.5 has the top score on Terminal-Bench 4.0, FrontierCode and CursorBench. Astra is ahead on AutomationBench by 1.4 points and on Terminal-Bench-Science by 5.9 points. On FrontierCode the gap between Opus 5.5 and Astra is 1.1 points.

Artificial Analysis ran Terminal-Bench 4.0 separately and measured Opus 5.5 at 59.6%, level with Astra at xhigh effort. That is a smaller lead than Anthropic reports.

Independent index

The Artificial Analysis Intelligence Index combines ten evaluations across reasoning, knowledge, maths and coding. Opus 5.5 at max effort scores 58. Astra and Fable 5.1 both score 53.

Opus 5.5 at high effort already scores above Astra and Fable 5.1. At medium effort, which is now the default, it scores 51.

Tokens per task

Price per token is only half of the bill. Artificial Analysis counted output tokens per index task at max effort:

• Opus 5.5: about 119k
• Fable 5.1: about 78k
• Opus 5: about 73k
• GPT-6 Astra: about 27k

Astra writes far fewer tokens, so its higher token price does not translate into a higher bill per task. Artificial Analysis measured $3.26 per task for Astra against $7.63 for Fable 5.1, and found Opus 5.5 level with Opus 5 on cost per task.

Lower effort settings cut this further. Anthropic reports that Opus 5.5 at its lowest effort caught 72% of known bugs in code review, compared with 56% for Opus 5 at high effort.

Breaking changes

Moving from Opus 5 to Opus 5.5 is more than a model ID swap. The migration guide lists four changes that return errors:

• Thinking can't be disabled. Requests with thinking: {"type": "disabled"} or a manual budget_tokens return a 400. Remove the field and set an effort level instead.
• Forced tool use is gone. tool_choice types "any" and "tool" return a 400. Use "auto" with strict tool use or structured outputs, and say in the prompt when the tool applies.
• Thinking blocks are tied to the model. Only Fable 5.1 and Mythos 5.1 can read Opus 5.5 thinking blocks. A router that moves a conversation to any other model runs without them.
• The old computer use tool is rejected. On the Claude API and Google Cloud, computer_20251124 returns a 400. Declare computer_toolset_20260801 instead. Amazon Bedrock still accepts the old tool.

Two more changes return no error. The default effort is now medium, where it was high on Opus 5. Text the model writes between tool calls now comes back inside thinking blocks, and those are empty by default, so a UI that shows progress updates will go quiet until you set a display mode.

Which model for which job

• Opus 5.5 for agentic coding and code review. It has the top scores on the terminal and IDE benchmarks and the lowest price per token of the three.
• GPT-6 Astra for scientific computing and workflow automation, where it scores higher. It also uses the fewest tokens per task.
• Fable 5.1 as a fallback inside Claude conversations. Anthropic says Opus 5.5 matches it on most work at 40% of the price, but Fable 5.1 is the only other model that can read Opus 5.5 thinking blocks.

Switching models in production

Benchmarks do not tell you how a model performs on your prompts. Put the model ID behind a feature flag and move a share of traffic across while you compare results.

import Featureflow from 'featureflow-node-sdk';

const featureflow = new Featureflow.Client({
  apiKey: process.env.FEATUREFLOW_SERVER_KEY,
});

const MODELS = {
  current: 'claude-opus-5',
  next: 'claude-opus-5-5',
};

// Bucket by account so a conversation never changes model halfway through
const user = new Featureflow.UserBuilder(accountId)
  .withAttribute('plan', account.plan)
  .build();

const variant = featureflow.evaluate('coding-model', user).value();
const model = MODELS[variant] ?? MODELS.current;

// Record the outcome you care about for each variant
featureflow.track('task-completed', user);

Start with 5% of accounts and watch error rates, cost per task and completion. If something breaks, turn the flag off and requests go back to the old model without a deploy. Bucketing by account matters here because thinking blocks are tied to the model that wrote them. The LLM rollouts post and the Featureflow docs cover the setup in more detail.

#ClaudeOpus#GPT6#AICoding#LLMOps#FeatureFlags