GPT-6 Astra vs Fable 5.1: The Ultimate Comparison

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GPT-6 Astra arrived in September 2026, days after Anthropic shipped Claude Fable 5.1. Both list at $10 per million input tokens and $50 per million output, and that shared sticker price is where the similarity ends. Compared benchmark by benchmark, the two frontier models split cleanly: Fable 5.1 owns neutral intelligence and long agentic work, while Astra owns computer use, math, and cost efficiency.

This guide walks each category with the numbers, labels which come from neutral testing and which come from each vendor, and ends with a clear read on which model fits which job.

GPT-6 Astra vs Fable 5.1 at a glance

Fable 5.1 wins more of the headline intelligence benchmarks, while Astra wins computer use and cost per task. The table below sets the two side by side across every dimension that matters, with each row labeled by source so you can tell neutral testing from vendor claims.

For the deeper Anthropic-side detail, see our Fable 5.1 benchmarks breakdown.

Table 1 - GPT-6 Astra vs Fable 5.1 comparison, September 2026

How to read this comparison

One source measures both models the same way, and most do not. Artificial Analysis runs GPT-6 Astra and Fable 5.1 through a single neutral harness, so its Intelligence Index and cost figures are the closest thing to an apples-to-apples comparison. Treat those rows as the anchor.

The vendor tables need more care. OpenAI's FrontierMath score for Astra is measured against GPT-5.6 Sol, not against Fable 5.1, and Anthropic's computer-use numbers use a different task release than OpenAI's. When each company picks the comparison that flatters its own model, the gaps in those tables tell you as much as the numbers do. Wherever a row below comes from one vendor, read it as directional rather than a matched result.

Intelligence and reasoning

Fable 5.1 is the more intelligent model on neutral testing. On the Artificial Analysis Intelligence Index, which aggregates nine reasoning, knowledge, and coding evaluations under one harness, Fable 5.1 scores 66 at max effort against Astra's 61. That five-point gap is the single most reliable signal in the whole comparison, because it is the one place both models are measured identically.

The vendor tables point the same way on general reasoning. On Humanity's Last Exam with tools, a broad test spanning math, science, and humanities, OpenAI's own table shows Astra at 57.2% trailing Fable 5.1's 65.0%. When a model loses a benchmark on its maker's own chart, the result carries extra weight.

Astra is not far behind on raw knowledge, and for many everyday reasoning tasks the difference is small. But if your work leans on hard, sustained reasoning, Fable 5.1 has the measurable edge.

Coding and agentic tasks

Coding is a split decision that depends on the kind of coding you do. On the narrow benchmark rows, Astra edges ahead; on long agentic work and neutral coding-agent testing, the two are level or Fable 5.1 leads.

1. Neutral coding-agent testing is a tie

On the Artificial Analysis Coding Agent Index, the two models are effectively tied: Astra scores 67.0 against Fable 5.1's 67.2. Neither model has a meaningful edge on the neutral measure of agentic coding, so this row comes down to cost and workflow rather than raw capability.

2. Astra leads the vendor coding rows

OpenAI's table gives Astra the win on several coding benchmarks. On DeepSWE v1.1, an agentic coding test, Astra scores 74.1% against Fable 5.1's 67.4%. On Terminal-Bench 4.0, which tests terminal-based software tasks, Astra reaches 57.9% to Fable 5.1's 55.8%, a narrow lead that both vendors report at similar levels.

3. Fable 5.1 leads long agentic and scientific work

Anthropic's strength shows in sustained, tool-using tasks. On Terminal-Bench-Science, an agentic scientific research benchmark, Anthropic reports Fable 5.1 at 52.6%, more than double its predecessor and well ahead of the field. For agents that run long, read context repeatedly, and need to stay readable across many steps, Fable 5.1 is the stronger fit, and its cache pricing reinforces that, as the pricing section shows.

Math and science

GPT-6 Astra is the stronger math model by a wide margin. On FrontierMath Tier 4, a set of research-level problems built to resist AI, OpenAI reports Astra at 97.6% against Fable 5.1's 87.8%. Astra effectively saturates a test designed to stay ahead of models, which is a genuine achievement.

One caveat matters. OpenAI measured Astra's FrontierMath result against GPT-5.6 Sol in its own materials, and the Fable 5.1 figure comes from separate reporting rather than a single matched run. The gap is real and large, but read it as two vendor numbers placed side by side rather than one head-to-head test.

On graduate-level science, the two are closer. Both post strong GPQA Diamond scores in the mid-90s, so the science gap is far narrower than the math gap. If your work is math-heavy, Astra is the clear choice; if it is general science, either model serves.

Computer use

Computer use is Astra's most decisive win. OpenAI calls it the best computer-use model it has built, and the evaluation results back a clear lead over Fable 5.1 on tasks like navigating desktop applications, filling forms, and completing multi-step office work.

Astra also brings a speed advantage here. OpenAI reports completing real desktop tasks in roughly 47% less time per task than GPT-5.6 Sol, which translates into faster agentic automation in practice. For workflows that drive a browser or an operating system, this is the category that should weigh most.

Anthropic reports its own computer-use figures for Fable 5.1, but on a different task release than OpenAI uses, so the two sets of numbers are not directly comparable. What is clear from both the vendor claims and independent coverage is that Astra sets the pace on computer use, and Fable 5.1 follows.

Pricing and cost efficiency

Astra and Fable 5.1 share a sticker price but tell opposite cost stories underneath it. Both list at $10 per million input tokens and $50 per million output. Which one is cheaper for you depends entirely on how you use it.

Astra wins on cost per completed task. Artificial Analysis measured its cost per Intelligence Index task at $1.67 at max effort against roughly $3.70 for Fable 5.1, because Fable 5.1 spends far more output tokens to reach an answer. For single, token-heavy tasks, Astra finishes for less than half the cost.

Fable 5.1 wins on cache economics and blended price. Its cache reads cost $0.25 per million tokens against Astra's $1.00, a fourfold difference that compounds in any workflow replaying cached context, such as long agents or retrieval pipelines. On Artificial Analysis's blended per-token measure, Fable 5.1 comes out at $7.17 against Astra's $7.70. For the full Anthropic-side detail, see the Fable 5.1 pricing breakdown.

Table 2 - GPT-6 Astra vs Fable 5.1 pricing as of September 2026

The practical rule is simple. If your workload sends heavy one-off prompts, Astra's token efficiency makes it cheaper. If it replays cached context across many turns, Fable 5.1's cheaper cache reads win.

Which should you choose?

Choose the model that matches your dominant workload, because neither wins across the board. The benchmark split maps cleanly onto real use cases, so the decision is less about which model is better and more about what you are building.

1. Choose Fable 5.1 for agents, retrieval, and research

Fable 5.1 fits work that centers on long-running coding agents, retrieval and document pipelines, scientific research, or any task where readable, sustained reasoning matters more than a quick answer. It leads the neutral intelligence index, wins agentic science, and its cheap cache reads pay off in loops that reuse context. Anthropic itself positions Fable 5.1 for the most demanding, lengthy tasks. For a related Anthropic matchup, see Fable 5.1 versus Opus 5.

2. Choose Astra for computer use, math, and security

GPT-6 Astra fits work that leans on computer-use automation, math-heavy or security-focused problems, or token-efficient single tasks. It is the stronger computer-use model, saturates hard math benchmarks, and costs less per completed task. If your agent's main job is driving a browser or a desktop application, Astra sets the pace.

3. Treat chat and everyday coding as a toss-up

For general chat and everyday coding, the two models are close enough that benchmarks should not decide it. They tie on the neutral coding-agent index and sit near each other on general reasoning, so price mix, ecosystem, and the tools you already use should break the tie. If neither feels like an obvious fit, it is worth weighing the wider field of Fable 5.1 alternatives before you commit.

Build production apps on Emergent

The honest read on GPT-6 Astra vs Fable 5.1 is that the better model is the one that fits the job in front of you. Fable 5.1 wins neutral intelligence, agentic science, and cache economics; Astra wins computer use, math, and cost per task. The sticker price is identical, so the decision rests on your workload, not the rate card.

Whichever way that decision goes, Emergent gives you room to build with leading models rather than locking you to one. It supports GPT, Claude, and other frontier-lab models through a single Universal LLM Key, so you work with a supported model family without setting up separate provider accounts. Describe the software you want, and let Emergent build the full-stack app that runs your business.

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