GPT-6 Astra and Claude Fable 5.1 at a glance
These are both frontier reasoning models released in September 2026. OpenAI describes Astra as its model for difficult end-to-end work across reasoning, coding, computer use, research and document creation. Anthropic positions Fable 5.1 for demanding reasoning and long-horizon agentic work, with particular emphasis on coding, research and projects that continue for hours or days.
| Category | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Primary positioning | Complex reasoning, coding, computer use, research and polished work products. | Demanding reasoning, long-running agents, ambitious coding and knowledge work. |
| API context window | 1.05 million tokens. | 1 million tokens. |
| Maximum output | 128,000 tokens. | 128,000 tokens. |
| Standard API price | $10 per million input tokens and $50 per million output tokens for short-context requests. | $10 per million input tokens and $50 per million output tokens. |
| Cached input | $1 per million cached input tokens. | $0.25 per million cache reads. |
| Long-input treatment | Requests above 272,000 input tokens carry higher input, cache and output rates. | The full one-million-token context is documented at standard per-token rates. |
Sources: OpenAI’s GPT-6 Astra model page, Anthropic’s model overview, and Anthropic’s pricing documentation. Subscription access is separate from API pricing.
The benchmarks do not produce one universal winner
OpenAI’s launch table gives Astra an advantage on several published comparisons: 41.4% versus 31.4% on AutomationBench, 57.9% versus 55.8% on Terminal-Bench 4.0, 64.6% versus 52.6% on Terminal-Bench Science 0.1, and 97.6% versus 87.8% on FrontierMath Tier 4 v2. The same table also shows Fable 5.1 ahead on Humanity’s Last Exam with tools, 65.0% versus 57.2%, and on the then-listed Artificial Analysis Intelligence Index, 65.7 versus 61.2.
A more recent independent comparison from Artificial Analysis reports an overall Intelligence Index tie at 53. Its individual evaluations still split: Astra leads AutomationBench-AA and Terminal-Bench 4.0, while Fable 5.1 leads SciCode, Humanity’s Last Exam, AA-Briefcase and the long-context reasoning evaluation. This is exactly why a single benchmark screenshot should not decide your purchase or workflow.
How to interpret the numbers: vendor launch tables and independent leaderboards may use different model settings, harnesses, dates and scoring methods. Treat each score as evidence about a defined test, not proof that one model is universally more intelligent.
Sources: OpenAI’s Astra announcement and evaluation notes and the Artificial Analysis head-to-head comparison and methodology links.
Reasoning and research
Best fit for tool-grounded analytical work
Astra is a strong choice when reasoning must turn into action across software, data, files or specialist tools. OpenAI highlights scientific reasoning, browsing, computer use and professional artifact creation as connected parts of the model rather than isolated chat abilities.
Best fit for sustained investigation
Fable 5.1 is designed for demanding reasoning and long-horizon work. Anthropic emphasizes research, multi-stage knowledge work, root-cause analysis and the ability to continue difficult projects with less supervision.
If your research process involves operating tools, building the resulting document or spreadsheet and checking the finished artifact, Astra has the more integrated product story. If the work is a prolonged investigation through a very large body of evidence, Fable 5.1 deserves the first trial. For high-stakes research, use either model to locate and organize evidence, but verify the conclusion against the underlying sources.
Sources: OpenAI’s capability announcement and Anthropic’s Fable 5.1 use-case overview.
Coding, agents and long-running execution
Astra leads several coding and automation rows published by OpenAI, including Terminal-Bench 4.0 and DeepSWE v1.1. It also supports computer use, a hosted shell, code interpretation, patch application, web and file search, Skills, MCP and tool search through the Responses API. This combination makes Astra attractive when a software task crosses from source code into a browser, document, terminal or deployed interface.
Fable 5.1 is built specifically around ambitious codebase work and long-running agents. Anthropic describes it as suitable for features spanning an entire codebase, code review, performance work and multi-day autonomous sessions. Its documentation also emphasizes planning, recovery after failures and progress updates during unattended work.
Practical verdict: start with Astra for mixed software-and-computer workflows or when you want the agent to produce and visually inspect the finished result. Start with Fable 5.1 for a long codebase investigation, difficult root-cause analysis or an agent expected to keep working for hours. For production engineering, test both inside the same harness because the surrounding tools can change the outcome as much as the base model.
Sources: Astra’s supported tools and specifications, OpenAI’s coding evaluation notes, and Anthropic’s coding and agent descriptions.
Computer use, documents and everyday professional work
This is the clearest area in which Astra’s product positioning is broader. OpenAI presents it as a computer-using model that can work across forms, browsers, professional software and files, then produce documents, spreadsheets, presentations, websites and other finished artifacts. It is also trained to follow existing templates and preserve business context while completing the work.
Fable 5.1 is still capable of vision, tool use, research and multi-stage deliverables. Anthropic particularly emphasizes understanding complex PDFs, charts, diagrams and tables, along with inspecting its own coding output visually. Its advantage is not that it handles only text; rather, it is framed around deep, sustained work more than a general cross-application production surface.
Practical verdict: Astra is the stronger default for people who want one model to research, act inside applications and deliver polished files. Fable 5.1 remains a serious alternative for document-heavy analysis or knowledge work that continues for a long time without constant direction.
Context, speed and API cost
The headline API price is the same: both companies list $10 per million input tokens and $50 per million output tokens. The cost changes once the workload shape is considered. Astra charges $1 per million cached input tokens and applies higher rates when the input exceeds 272,000 tokens. Fable 5.1 lists cache reads at $0.25 per million tokens and documents standard per-token pricing across its full one-million-token window.
Artificial Analysis currently measures Fable 5.1 at a faster output rate, about 68 tokens per second compared with about 54 for Astra in the configurations tested. However, it also records Astra using far fewer output and reasoning tokens per task and completing the measured task in less total generation time. “Faster” can therefore mean a quicker first response, a faster stream, fewer generated tokens, or less total time to a correct result.
Practical verdict: Fable 5.1 has the clearer cost advantage for cache-heavy agents that repeatedly reuse large prompts. Astra may be more efficient when it reaches the answer or completes the workflow with fewer generated tokens. Estimate the whole task, including tool calls, long-context multipliers, retries and human correction time.
Sources: OpenAI model pricing, Anthropic API pricing, and Artificial Analysis speed, token-use and cost measurements.
Which AI model should you choose?
The winner depends on the workload
- Choose GPT-6 Astra first
- When the job combines research, computer use, coding, browsers, professional applications and polished documents or presentations.
- Choose Claude Fable 5.1 first
- When the job is a prolonged coding, research or knowledge-work assignment that must stay coherent over many steps with limited supervision.
- Choose Astra for broad execution
- When you want one model to move from analysis into visible actions and inspect the resulting website, file or interface.
- Choose Fable for cached agents
- When a repeated API workflow reuses a large context and the lower cache-read price materially changes operating cost.
- Test both for coding
- When the task affects production software. Astra leads several published coding benchmarks, while Fable is purpose-built for long-running codebase work. Your repository and harness should decide.
Choose your main workload
Select the closest match for a concise starting recommendation. This is a workflow guide, not a substitute for testing with your own material.
Choose a workload above to see which model deserves your first test.
How to compare both models fairly
A useful evaluation should resemble the work you will actually pay for. Avoid asking one casual question and treating the most confident answer as proof of intelligence.
- Choose three representative tasks. Include one ordinary task, one difficult task and one task that previously failed.
- Use the same inputs and acceptance criteria. Give both models the same source files, permissions, deadline and definition of success.
- Match the effort setting as closely as possible. Record model version, reasoning level, tools and harness because those settings affect results.
- Score the finished work. Measure factual accuracy, completion, usability, corrections required, elapsed time and total cost.
- Repeat before deciding. One run may be unusually good or bad. Use multiple trials and keep the model that performs reliably on your work.
The clearest conclusion is conditional: GPT-6 Astra is the better general choice for broad end-to-end execution, while Claude Fable 5.1 is often the better first test for sustained agentic coding, long investigations and cache-heavy workflows. If your work crosses both categories, the strongest setup may be to assign each model the jobs that match its strengths rather than forcing one model to handle everything.