GPT-6 Astra and Claude Fable 5.1 launched just two days apart: Astra on September 3, 2026, and Fable 5.1 on September 1, 2026.
They overlap in capability, but they are optimized differently. Astra has the stronger published results across coding, scientific tooling, computer use, and professional workflows. Fable 5.1 leads on Humanity’s Last Exam and offers much cheaper cache reads, which matters for long-running agents with large repeated prompts.
My short version:
- Choose GPT-6 Astra first for execution-heavy agents, coding, computer use, and tool-driven scientific tasks.
- Choose Claude Fable 5.1 for broad difficult reasoning, asynchronous long-horizon work, and workloads that repeatedly reuse very large contexts.
- For mixed workloads, measure cost per successful task, not just benchmark scores or token prices.
Feature and pricing snapshot
| Specification | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Developer | OpenAI | Anthropic |
| Release date | Sep 3, 2026 | Sep 1, 2026 |
| API model ID | gpt-6-astra | claude-fable-5-1 |
| Context window | 1,050,000 tokens | 1,000,000 tokens |
| Maximum output | 128,000 tokens | 128,000 tokens |
| Input modalities | Text, images | Text, images |
| Output modality | Text | Text |
| Knowledge cutoff | Apr 30, 2026 | Jun 2026 |
| Reasoning | low / medium / high / xhigh / max | Adaptive, always on |
| Default effort | — | high |
| Official input / output | $10 / $50 per MTok | $10 / $50 per MTok |
| Official cache read | $1 / MTok | $0.25 / MTok |
| Long-context pricing | Higher tier above 272K input | Standard model rate across 1M context |
| Primary positioning | End-to-end execution, coding, computer use | Demanding reasoning, long-horizon agents |
Information and pricing verified on September 7, 2026. Provider specifications and prices may change after publication.
Where GPT-6 Astra fits
OpenAI introduced GPT-6 Astra on September 3, 2026 as a model for difficult, end-to-end professional work. Its intended workflows combine reasoning, coding, research, computer interaction, and document creation rather than stopping at question answering.
Astra can work through multiple steps, use supplied tools and context, and adapt when requirements change. OpenAI specifically highlights documents, spreadsheets, and presentations that must follow particular instructions or templates.
The model supports a 1,050,000-token context window and up to 128,000 output tokens. Its reasoning effort is configurable:
low
medium
high
xhigh
max
That gives developers a direct way to trade response speed against deeper computation.
Where Claude Fable 5.1 fits
Anthropic released Claude Fable 5.1 on September 1, 2026 as its highest-end model for demanding reasoning and long-horizon agent work. It builds on Claude Fable 5 with improvements in extended coding sessions, multistep research, and document, spreadsheet, and presentation workflows.
Anthropic positions it for cases where sustained reasoning and dependable execution matter more than raw response speed, especially when Claude Opus 5 at higher effort settings is not enough.
Fable 5.1 has a 1-million-token context window and a 128,000-token maximum output. It accepts text and image inputs, produces text, and has a June 2026 knowledge cutoff. Its reasoning is adaptive and always enabled, with effort used to control depth.
Coding: Astra has the clearer edge
Coding is Astra’s strongest comparative category in the published results.
| Coding benchmark | GPT-6 Astra | Claude Fable 5.1 | Result |
|---|---|---|---|
| Terminal-Bench 4.0 | 57.9% | 55.8% | Astra +2.1 pts |
| DeepSWE v1.1 | 74.1% | 67.4% | Astra +6.7 pts |
| FrontierCode 1.1 Extended | 64.5% | 63.6% | Astra +0.9 pts |
| FrontierCode 1.1 Main | 53.3% | 50.9% | Astra +2.4 pts |
| Database migration, internal | 63.9% | 57.8% | Astra +6.1 pts |
Astra leads Fable 5.1 on Terminal-Bench 4.0, DeepSWE v1.1, and the internal database-migration evaluation. Those tests are more relevant to an agent that has to navigate a repository, run commands, test changes, and verify its own work than to a model that only writes code in a response.
That does not make Fable 5.1 poor at software engineering. Anthropic reports a 73.4% result on its own CursorBench 3.2.0 evaluation and describes Fable 5.1 as its most capable model for ambitious coding projects.
The distinction is workload shape. Astra looks better when coding is tightly coupled to shell execution, repository navigation, testing, and tool use.
My coding pick: GPT-6 Astra, particularly for execution-heavy software agents.
Reasoning and science are not a clean sweep
The reasoning results depend heavily on the evaluation.
| Reasoning / science benchmark | GPT-6 Astra | Claude Fable 5.1 | Winner |
|---|---|---|---|
| FrontierMath Tier 4 v2 | 97.6% | 87.8% | Astra |
| GPQA Diamond | 96.0% | 93.7% | Astra |
| Terminal-Bench Science 0.1 | 64.6% | 52.6% | Astra |
| Humanity's Last Exam, with tools | 57.2% | 65.0% | Fable 5.1 |
| Artificial Analysis Intelligence Index | 61.2 | 65.7 | Fable 5.1 |
Astra has substantial leads on FrontierMath Tier 4, GPQA Diamond, and Terminal-Bench Science. Those evaluations emphasize highly technical reasoning, mathematics, science, and interaction with tools or external environments.
Fable 5.1 wins Humanity’s Last Exam with tools, 65.0% to Astra’s 57.2%. It also leads the Artificial Analysis Intelligence Index, 65.7 to 61.2.
My interpretation is that Astra is stronger at technical reasoning that must be executed through tools, while Fable 5.1 remains very competitive—and sometimes better—on broader multidisciplinary reasoning.
Computer use and professional workflows
The OSWorld numbers need careful handling. Anthropic’s benchmark note says Fable 5.1 uses the authors’ August 2026 task release. Its chart reports 77.9% partial and 41.7% strict, so those figures should not be compared directly with OpenAI’s 72.6% result.
Other published professional-work comparisons are easier to read:
| Professional benchmark | GPT-6 Astra | Claude Fable 5.1 | Result |
|---|---|---|---|
| AutomationBench | 41.4% | 31.4% | Astra +10.0 pts |
| BenchCAD | 95.9% | 84.3% | Astra +11.6 pts |
| Terminal-Bench Science | 64.6% | 52.6% | Astra +12.0 pts |
OpenAI also designed Astra around producing documents, spreadsheets, and presentations while completing multistep workflows. Fable 5.1 supports browser operation, research, coding, professional documents, and long-running agents, but Astra currently has stronger published evidence for computer-mediated execution and artifact production.
For computer-use agents and professional automation, I would start with Astra.
Context size matters less than context pricing
Astra’s context window is technically larger: 1.05 million tokens versus 1 million for Fable 5.1. In practice, that 5% difference probably will not determine an architecture.
The pricing rules may.
For Astra, requests containing more than 272K input tokens use higher rates for the entire request:
- Input: $20 per MTok
- Cached input: $2 per MTok
- Output: $75 per MTok
Fable 5.1 keeps the standard model rate across its 1-million-token context:
- Input: $10 per MTok
- Cache read: $0.25 per MTok
- Output: $50 per MTok
If an application regularly sends 300K, 500K, or 900K tokens, the nominal context-window size is only part of the decision. Fable 5.1 has the cleaner economics for very large requests, especially when most of the prompt can be reused from cache.
Token prices are equal, cache prices are not
At the official Standard rate, uncached input and output cost the same for both models.
| Price component | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Input / MTok | $10.00 | $10.00 |
| Output / MTok | $50.00 | $50.00 |
| 5-minute cache write / MTok | $12.50 | $12.50 |
| Cache read / MTok | $1.00 | $0.25 |
| Batch input/output discount | 50% | 50% |
| Long-context surcharge | Above 272K input | None across standard 1M context |
The important difference is Fable 5.1’s $0.25 cache-read price. That is one quarter of Astra’s standard $1 cached-input rate and one eighth of Astra’s $2 long-context cache rate.
Consider an agent that repeatedly sends the same system prompt, tool definitions, repository context, and reference documents over dozens of turns. Cache reads accumulate across that loop, while a one-shot benchmark does not show the effect.
Anthropic estimates that the lower cache price can reduce typical Fable 5.1 workload costs by about 25% and highly agentic workload costs by up to approximately 45%. Those are vendor estimates, not guarantees, but they explain why cache behavior deserves its own test.
Is Fable 5.1 always cheaper?
No.
A model with a higher token bill can still cost less per completed task if it needs fewer retries, makes fewer failed tool calls, emits fewer tokens, or finishes faster. I prefer measuring:
total API spend ÷ accepted completed tasks
Fable 5.1 wins the rate-card comparison for cache-heavy and very-long-context workloads. Astra can still be cheaper per successful task if its execution advantage reduces retries or runtime enough.
Tool design and agent behavior
Both models target agentic applications, but their interfaces suggest different design priorities.
GPT-6 Astra works in a tool-rich Responses API environment. Its supported tools include:
- Web search
- File search
- Image generation
- Code interpreter
- Hosted shell
- Apply Patch
- Computer use
- MCP
- Tool search
Its configurable reasoning effort also lets an application decide how much compute to spend on a particular request.
Fable 5.1 takes a different approach. Adaptive thinking is always enabled, and Anthropic added per-message effort, turn-scoped system messages, and readable progress updates between tool calls. Those features fit long autonomous sessions where the agent needs to maintain continuity and expose what it is doing.
In practical terms:
- Astra feels optimized for tool breadth and end-to-end environment control.
- Fable 5.1 feels optimized for persistent reasoning and long-horizon continuity.
The orchestration layer still matters as much as the model. Retrieval quality, tool permissions, state management, retry logic, and acceptance tests can easily dominate a benchmark difference.
Safety and deployment behavior
OpenAI describes Astra as its first model to reach the company’s Critical cybersecurity threshold. It also describes extra safeguards for high-capability workflows, production monitoring, and task interruption when an agent may be exceeding its authorized scope.
Fable 5.1 uses a different safety design. Anthropic says that some cybersecurity and biology requests identified by its safeguards can be routed to less capable models.
That means observed production behavior is not only a property of the base model. It also reflects routing, refusals, fallbacks, interruptions, audit requirements, data retention, and privacy controls.
I would include all of those in an enterprise evaluation instead of checking them only after selecting a model.
Which model should I test first?
| Workload | Recommended model | Reason |
|---|---|---|
| Autonomous computer-use agent | Astra | Stronger published computer-use and professional execution evidence |
| Agentic terminal / software engineering | Astra | Leads Terminal-Bench, DeepSWE, and database-migration results |
| Scientific tool workflows | Astra | Strong FrontierMath, GPQA, and Terminal-Bench Science results |
| Broad frontier reasoning | Fable 5.1 | Leads HLE with tools and the Intelligence Index |
| Long-running asynchronous agent | Fable 5.1 | Built around long-horizon agentic work and progress updates |
| 300K–1M-token requests | Fable 5.1 | Avoids Astra’s 272K long-context surcharge in standard pricing |
| Heavy prompt-cache reuse | Fable 5.1 | $0.25/MTok official cache reads |
| Document / spreadsheet / presentation automation | Astra | Strong professional-work and artifact-generation positioning |
| Large repository with repeated cached context | Fable 5.1 | Cache economics can dominate repeated loops |
| Mixed production workloads | Test both | Their strengths are complementary |
A useful rule of thumb:
- If the model needs to act, start with Astra.
- If it needs to think for a long time over a huge, repeatedly reused context, start with Fable 5.1.
Overall verdict
Astra wins more directly comparable published benchmarks, especially in coding execution, scientific tooling, computer use, and professional automation. For an agent that must operate software rather than explain what a person should do, that is meaningful.
Fable 5.1’s advantages are narrower but important. Its 65.0% Humanity’s Last Exam result and higher Intelligence Index score show that Astra is not universally better at general reasoning. Fable 5.1 also has a structural advantage for cache-heavy agents and large requests that use much of the million-token context.
The practical question has shifted from “Which chatbot gives the smartest answer?” to “Which system completes this job correctly at the lowest total cost?”
How I would evaluate both models
I would build a fixed test set from real production tasks, then run identical inputs through both systems with equivalent tool permissions.
For agentic workloads, I would record:
- Task success rate
- Human acceptance rate
- Tool-call failures
- Retry count
- Time to completion
- Uncached input tokens
- Cache-read tokens
- Output tokens
- Total API spend
The key metric is:
total API spend ÷ accepted completed tasks
That calculation can overturn a benchmark-based choice. Astra may win if it avoids retries. Fable 5.1 may win dramatically across a 50-turn loop because of its cache-read price.
I would keep the model configurable and route different workloads independently rather than forcing one model to handle every request.
FAQs
Is GPT-6 Astra better than Claude Fable 5.1?
Astra is stronger across several comparable coding, scientific, and professional-work benchmarks, including Terminal-Bench, DeepSWE, FrontierMath, and AutomationBench. Fable 5.1 leads on Humanity’s Last Exam with tools and the Artificial Analysis Intelligence Index. Neither model wins every category.
Which model is better for coding?
GPT-6 Astra is the better first choice for coding agents and execution-heavy engineering. The published comparison reports 57.9% for Astra versus 55.8% for Fable 5.1 on Terminal-Bench 4.0, with larger Astra leads on DeepSWE and database migration.
Fable 5.1 remains competitive for long-running repository work, but Astra has the stronger overall coding evidence.
Which model is better for long-context applications?
Claude Fable 5.1 is the better choice when requests are very large or repeatedly reuse cached prompts. Both models provide roughly one million tokens of context, but Astra applies higher rates above 272K input tokens, while Fable 5.1 keeps standard pricing across its 1-million-token context and charges only $0.25 per MTok for cache reads.
Which one is cheaper?
At the official base rate, neither is cheaper: both cost $10 per million input tokens and $50 per million output tokens.
Fable 5.1 is cheaper for cache-heavy and very-long-context workloads because of its $0.25 per MTok cache-read rate and lack of a standard long-context surcharge. Both models are listed at $8 per million input tokens and $40 per million output tokens through CometAPI, a unified multi-model API.
Should I commit to one model?
Not without testing your actual workload. Their strengths are complementary, so model routing may outperform a single-model deployment. Compare successful task completion, retries, latency, token usage, and total spend using your own acceptance criteria.
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