GPT-6 Astra vs Claude Fable 5.1: Which Wins?

Decision: GPT-6 Astra or Claude Fable 5.1? The choice hinges on $10/$50 API pricing

GPT-6 Astra currently has the edge for computer use, science, and cybersecurity, while Claude Fable 5.1 leads independent overall and coding-agent indexes; the right pick depends on workflow, evaluation harness, and effective task cost. OpenAI released Astra on September 3, 2026, but its rollout remains staged, so access is not yet equally available to every user.

Updated 2026-09-20: OpenAI has described a next-generation model as significantly more capable than GPT-6 Astra (see the Update below).

Key Takeaways

• GPT-6 Astra leads OpenAI-reported results including 98% on FrontierMath Tier 4, 99.9% on ARC-AGI-3, and 100% on ExploitBench.
• Independent Artificial Analysis testing gives Claude Fable 5.1 an Intelligence Index score of 66 versus 61 for Astra.
• Fable 5.1 also leads the independent Coding Agent Index, scoring 70 versus Astra’s 67.
• Astra leads a direct Terminal-Bench Science 0.1 comparison, scoring 64.6% versus Fable 5.1 at 52.6%.
• Both models are reported at $10 per million input tokens and $50 per million output tokens, but task-level cost depends on token efficiency and harness design.
• Astra is rolling out through OpenAI, ChatGPT, the OpenAI API, and AWS; exact limits and entitlements remain subject to deployment.

GPT-6 Astra vs Claude Fable 5.1 at a Glance

Dimension	GPT-6 Astra	Claude Fable 5.1
Overall independent score	61 on Artificial Analysis Intelligence Index	66 on Artificial Analysis Intelligence Index
Coding-agent score	67 on Artificial Analysis Coding Agent Index	70 on Artificial Analysis Coding Agent Index
Terminal-Bench Science 0.1	64.6%, according to OpenAI’s comparison	52.6%, according to OpenAI’s comparison
Context window	1,000k tokens in Artificial Analysis; 1.05M reported in another comparison summary	1,000k tokens in Artificial Analysis
Headline API pricing	Reported at $10 input and $50 output per 1M tokens	Reported at $10 input and $50 output per 1M tokens
Availability	Daybreak first, then ChatGPT plans, API, and AWS rollout	Not yet confirmed in this bundle

The table combines different evidence classes. Astra’s headline capability scores come primarily from OpenAI, while the overall and coding-agent scores come from independent Artificial Analysis evaluations. Fable 5.1 has more independent comparative data in the bundle, while Astra has stronger early vendor and partner reporting.

Capabilities and Workflow Fit

OpenAI positions Astra as a general computer-use model rather than a text-only chatbot. Its documented tasks include filling online forms, updating CRM records, organizing calendars, browsing, creating websites, analyzing data, running plots, and performing frontend quality assurance. OpenAI also describes stronger visual judgment for converting sketches, references, or existing interfaces into working frontends.

The model’s most distinctive concept is Codex Persistent Context. According to OpenAI’s launch material, Astra can preserve notes across filled context windows and search earlier work instead of repeatedly compressing a long task into one summary. This is designed for debugging, refactoring, research, and other jobs that continue across multiple sessions.

OpenAI also reports a computer-use result on Agents’ Last Exam of 59.3%, compared with 55.5% for Claude Opus 5 and 53.6% for GPT-5.6 Sol. That is not a direct Fable 5.1 score, so it should not be treated as a Fable comparison. The more defensible conclusion is narrower: Astra has documented strength in software interaction and professional-work environments. See OpenAI’s GPT-6 Astra launch post for the vendor’s task descriptions and evaluation conditions.

Early community testing adds practical examples but should remain separate from measured results. Max Weinbach reported that Astra reconstructed Apple Park in Blender from images, built a macOS simulator, and used networked Mac Studios to distribute a Blender render. He also reported an approximately eight-day software task involving more than 1,600 subagents. These are hands-on reports, not controlled benchmarks.

Arena.ai published a video in which Peter Gostev compares Astra with Claude Fable 5.1 on one-shot 3D and interactive-world generation. The existence of the comparison is documented, but the bundle does not provide a reproducible scorecard for Fable across those tasks. The safest reading is that Astra has unusually visible 3D and interactive-software demonstrations, not that it wins every creative workflow.

For Fable 5.1, the strongest documented case is independent performance on general intelligence and coding-agent evaluations. Artificial Analysis places Fable ahead of Astra on its combined Intelligence Index and Coding Agent Index. That matters for teams selecting a model through a mature coding workflow, especially when the surrounding harness, tool permissions, and agent loop resemble the evaluation setup.

Benchmarks: Strong Astra Wins, Clear Fable Counterweight

OpenAI reports that Astra scores 98% on FrontierMath Tier 4, 99.9% on ARC-AGI-3, and 100% on ExploitBench. Sam Altman repeated those figures in the launch announcement. OpenAI also reports state-of-the-art results on Terminal-Bench 4.0, Terminal-Bench Science 0.1, FrontierMath Tier 4, ARC-AGI-3, and HealthBench Pro.

Source: @OpenAI

Those numbers need evaluation context. ARC Prize reports that Astra scored 62.7% on ARC-AGI-3 with its Standard Harness and 99.9% with an OpenAI Provider Adapter. The provider-adapter result preserves opaque reasoning state between requests, which changes how memory is handled. ARC Prize reported that Astra used fewer actions than the median tested human on 96% of levels, but the harness difference means the two ARC scores are not interchangeable.

That discrepancy is central to this comparison. Astra’s Provider Adapter result demonstrates what the complete OpenAI system can achieve with its preferred state-management layer. The Standard Harness result is more useful for provider-neutral comparison. Both are legitimate measurements, but they answer different questions.

The benchmark balance is mixed rather than one-sided:

• OpenAI’s Terminal-Bench Science 0.1 comparison gives Astra 64.6% and Fable 5.1 52.6%, with Astra estimated at approximately 31% lower API cost.
• Cline reported Astra at the top of Terminal-Bench, 1.9 percentage points ahead of Fable 5.1. The post does not provide full test settings.
• Perplexity reported a WANDR score of 0.682 at $11.98 per task for Astra, describing it as 13.5% above Fable 5.1 and 6.1% cheaper per task.
• Artificial Analysis gives Fable 5.1 a five-point lead on the Intelligence Index, 66 to 61.
• Artificial Analysis gives Fable 5.1 a three-point lead on the Coding Agent Index, 70 to 67.

The WANDR and Terminal-Bench comparisons are useful early signals, but they do not replace independent replication. The strongest current verdict is therefore task-specific: Astra looks better on scientific terminal workflows, interactive reasoning, and several computer-use tasks; Fable looks better on the independent aggregate indexes available in the bundle.

Pricing and Token Efficiency

The reported headline API price for both models is $10 per million input tokens and $50 per million output tokens. Astra’s price is tied to OpenAI’s launch materials and independent analysis. Fable’s matching price appears in comparison reporting and community coverage, but the bundle does not include an Anthropic pricing page.

Astra is substantially more expensive per token than GPT-5.6 Sol’s reported $4 input and $20 output pricing. Artificial Analysis reports that Astra uses approximately 10% fewer output tokens than Sol on its Intelligence Index evaluation, but the higher token price outweighs that reduction in the same comparison. This is why “more token efficient” does not automatically mean “cheaper.”

Task cost can tell a different story. Artificial Analysis reports that Astra occupies a stronger coding-agent cost-efficiency frontier and costs less than half as much per task as Claude Fable 5 in its coding-agent comparison at a similar score. The comparison uses different model harnesses, so teams should reproduce the calculation with their own prompts, tools, retries, and context retention.

The practical pricing rule is simple: compare completed-task cost, not just list price. A model that uses fewer actions, fewer output tokens, or fewer retries may cost less even when its output token price is identical. Conversely, a long-running Ultra Reasoning task can consume more resources than a short standard request.

Artificial Analysis’s comparison page also shows normalized per-million-token prices of $7.70 for Astra and $7.17 for Fable 5.1. Those figures are not the same as the headline input/output list prices, so they should not be presented as a replacement for the $10/$50 figures. They are useful only as a warning that pricing views depend on methodology.

Context, Safety, and Limits

Both models are listed with approximately one million tokens of context in the independent comparison. A separate comparison summary reports 1.05 million tokens for Astra and one million for Fable 5.1. The precise production limits, maximum output settings, and account-specific quotas are not fully confirmed in the bundle.

Astra’s safety profile is unusually important. OpenAI’s system card classifies it as the first OpenAI model to reach the Critical level of cybersecurity capability under its Preparedness Framework. The system card says Astra can find previously unknown vulnerabilities and develop exploitation methods with the right tools and access. OpenAI therefore added stronger monitoring, isolation, trajectory review, and safeguards.

OpenAI also says Astra is more aligned than GPT-5.6 Sol and better at staying within authorized scope. At the same time, the system card and independent commentary raise a monitorability concern. Astra’s internal computation and state handling may be harder to inspect than earlier systems. The system card should be treated as the primary resource for deployment-safety details: GPT-6 Astra System Card.

The AGI language requires restraint. Greg Brockman described Astra as potentially marking the beginning of an AGI era, but there is no generally agreed definition of AGI. Success on ARC-AGI-3 does not establish broad human-level performance across open-ended work. Gary Marcus’s independent commentary describes Astra as impressive while emphasizing that the robustness and generality of its symbolic world-model behavior remain open questions.

For background on how the launch evidence fits together, read GPT-6 Astra Release: Daybreak Access, 100% ExploitBench, and Signal vs Noise. The earlier analysis separates confirmed announcements from early demonstrations and unverified interpretations.

Availability and Deployment

OpenAI announced a staged rollout. Daybreak organizations received access first, followed over the coming days by ChatGPT Plus, Pro, Business, and Enterprise users, the OpenAI API, and AWS. OpenAI also described Astra Pro as coming to Pro, Business, and Enterprise plans.

The rollout was not perfectly synchronized. Community reports indicated that some Plus users could not see Astra in regular ChatGPT while limited access appeared through work products or Codex. API rate limits, exact plan entitlements, and the final timing of broad access were not confirmed in the bundle.

Codex CLI 0.153.1 reportedly added GPT-6 Astra catalog support, although the default model remained unchanged and model-picker exposure varied. Kie.ai does not currently host GPT-6 Astra. For a comparable coding-agent workflow, builders can try OpenAI Codex, while Astra access should be checked through OpenAI’s official app, API, and documentation channels.

Fable 5.1’s precise plan availability is not documented in the supplied evidence. That creates an access asymmetry: Astra has a clearly described rollout path but uneven early availability, while Fable has stronger independent comparison coverage but no detailed vendor access information in this bundle.

Which One Should You Use?

Choose GPT-6 Astra if:

• Your workload requires computer control, browser interaction, frontend QA, CRM updates, or other screen-based operations.
• You prioritize scientific terminal workflows, interactive reasoning, cybersecurity analysis within authorized environments, or 3D software automation.
• Your agent benefits from Codex Persistent Context, long-running tasks, subagents, and state carried across multiple context windows.
• You can validate OpenAI’s preferred Provider Adapter behavior instead of relying only on provider-neutral tests.

Choose Claude Fable 5.1 if:

• Your primary selection criterion is the independent Intelligence Index, where Fable 5.1 scores 66 versus Astra’s 61.
• You are optimizing for the independent Coding Agent Index, where Fable 5.1 scores 70 versus Astra’s 67.
• Your evaluation resembles general knowledge work or coding-agent tasks where aggregate independent rankings matter more than specialized science or computer-use benchmarks.
• Your team already has a validated Claude workflow and Astra’s staged rollout would create operational friction.

Early signal favors Astra for science and computer-use execution, but head-to-head data remains thin across ordinary production workloads. A short internal bake-off with identical tools, context limits, retry budgets, and task definitions is more reliable than a single leaderboard.

Frequently Asked Questions

Is GPT-6 Astra better than Claude Fable 5.1?

GPT-6 Astra is better on several published science, computer-use, automation, and cybersecurity comparisons, while Claude Fable 5.1 leads independent overall and coding-agent indexes.

Is GPT-6 Astra cheaper than Claude Fable 5.1?

GPT-6 Astra and Claude Fable 5.1 are both reported at $10 per million input tokens and $50 per million output tokens, but task cost varies with token use, caching, and harness.

Which is better for coding, GPT-6 Astra or Claude Fable 5.1?

Claude Fable 5.1 leads the independent Coding Agent Index at 70 versus Astra at 67, while Astra leads some task-specific coding and software-engineering comparisons.

Which has better benchmarks, GPT-6 Astra or Claude Fable 5.1?

GPT-6 Astra leads several vendor-reported and task-specific benchmarks, but Claude Fable 5.1 leads the independent Intelligence Index and Coding Agent Index.

Does GPT-6 Astra have a larger context window than Claude Fable 5.1?

Both models are listed with approximately one million tokens of context in the independent comparison, although another comparison summary reports 1.05 million tokens for Astra.

How can I access GPT-6 Astra?

GPT-6 Astra is rolling out through OpenAI to Daybreak organizations first, followed by ChatGPT Plus, Pro, Business, Enterprise, the OpenAI API, and AWS.

What to Watch Next

The next useful signals are independent Astra results on provider-neutral coding and knowledge-work harnesses, stable API access with published limits, and replicated comparisons against Fable 5.1 using identical tools and budgets.

Update — 2026-09-20

OpenAI said a group of agents using a next-generation model “significantly more capable than GPT-6 Astra” produced a proof addressing the three-dimensional Navier–Stokes problem, according to the announcement summary. This is an indirect signal about a successor system, not a new Astra benchmark; the model identity, proof validation, and evaluation conditions remain unspecified.

OpenAI also announced that GPT-5.5 will leave ChatGPT, ChatGPT Work, and Codex on October 14, directing users toward GPT-5.6 Sol or GPT-6 Astra (official notice). Separately, an alleged dump of Astra’s system prompts and tool definitions—reported as more than 330,000 prompt characters and over one million tool characters—has been mirrored in a public repository. Its provenance remains unverified, so it should not be treated as authoritative documentation.

Building similar AI model-comparison workflows? On kie.ai you can try GPT-6 Astra, Claude Fable 5, and Claude Opus 5.5.