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Last updated: October 21, 2026
Claude Fable 5.1 vs GPT-6 Astra: which is the better model for enterprise agents? Neither wins across the board. Claude Fable 5.1, released by Anthropic on September 1, 2026, leads on reasoning with tools and cheap cached agent loops. GPT-6 Astra, released by OpenAI on September 3, 2026, leads on computer use, several coding scores and cost per task. Both list at $10 per million input tokens and $50 per million output tokens, so the choice depends on workload, measured cost and tool safety.
TL;DR: Key Takeaways
- Same list price, different bills. Both models cost $10 input and $50 output per million tokens, yet Artificial Analysis measured $7.63 per task for Fable 5.1 at max effort against $3.26 for Astra (Artificial Analysis).
- Intelligence is close. At max effort the two tie at 53 on Artificial Analysis Intelligence Index v4.3.2 (Artificial Analysis).
- Astra leads agentic coding in vendor tables. OpenAI reports 57.9% on Terminal-Bench 4.0 against 55.8% for Fable 5.1, and 74.1% against 67.4% on DeepSWE v1.1 (OpenAI).
- Fable 5.1 leads on reasoning with tools. Humanity's Last Exam with tools shows 65.0% for Fable 5.1 against 57.2% for Astra (OpenAI).
- Both raise the security stakes. OpenAI rates Astra Critical for cybersecurity under its Preparedness Framework (OpenAI), and Anthropic found no critical-severity jailbreak of Fable 5.1's cyber safeguards (Anthropic).
Claude Fable 5.1 vs GPT-6 Astra at a Glance
| Spec | Claude Fable 5.1 | GPT-6 Astra |
|---|---|---|
| Vendor | Anthropic | OpenAI |
| Release date | September 1, 2026 | September 3, 2026 |
| API model ID | claude-fable-5-1 | gpt-6-astra |
| Context window | 1M tokens | 1.05M tokens |
| Max output | 128K tokens | 128K tokens |
| Input / output price per 1M tokens | $10 / $50 | $10 / $50 |
| Cache read per 1M tokens | $0.25 | $1.00 |
| Prompts above 272K tokens | Single price listed | 2x input, 1.5x output |
| Cloud availability | Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry | OpenAI API, Microsoft Azure, Amazon Bedrock |
Sources: Claude Platform Docs (2026) and OpenAI (2026). According to DevelopersDigest (2026), the 1.05M window and the 272K surcharge come from OpenAI's API model page.
Claude Fable 5.1 vs GPT-6 Astra Benchmark Results
GPT-6 Astra posts the higher score on most agentic and coding benchmarks in OpenAI's own table, while Claude Fable 5.1 leads on Humanity's Last Exam with tools. Scores come from the vendors, so treat gaps under two points as noise and verify on your own tasks.
How we compared
We took vendor-reported scores from OpenAI's launch table and Anthropic's announcement, then checked them against Artificial Analysis, which runs both models itself. Vendor scores use each model's best effort setting, so they show ceilings, not typical runs.
| Benchmark | Claude Fable 5.1 | GPT-6 Astra |
|---|---|---|
| Terminal-Bench 4.0 | 55.8% | 57.9% |
| DeepSWE v1.1 | 67.4% | 74.1% |
| Terminal-Bench Science 0.1 | 52.6% | 64.6% |
| FrontierMath Tier 4 (v2) | 87.8% | 97.6% |
| GPQA Diamond | 93.7% | 96.0% |
| Humanity's Last Exam (with tools) | 65.0% | 57.2% |
| AutomationBench | 31.4% | 41.4% |
| BenchCAD | 84.3% | 95.9% |
All scores are vendor-reported, from OpenAI's launch table (2026).
Independent runs narrow the picture. According to Artificial Analysis (2026), Fable 5.1 scores higher on GDPval-AA v2.1 (1754 against 1573), SciCode (63.1% against 56.5%) and AA-LCR (85.3% against 80.7%). Astra leads its own run of Terminal-Bench 4.0 (59.1% against 52.0%) and AutomationBench-AA (68.5% against 59.4%).
The index version also matters. OpenAI's table lists Intelligence Index v4.1 scores of 65.7 for Fable 5.1 and 61.2 for Astra. Artificial Analysis's current page, on v4.3.2, shows a 53 to 53 tie at max effort. A new index version reversed the ranking, which is a good reason to run your own evaluation.
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Pricing and Cost per Task
Both models cost $10 per million input tokens and $50 per million output tokens, so list price does not separate them. Cost per task does. Artificial Analysis measures Fable 5.1 at about 2.3 times Astra's cost at max effort, which implies it uses more tokens per task.
| Effort | Fable 5.1 score / cost per task | Astra score / cost per task |
|---|---|---|
| Max | 53 / $7.63 | 53 / $3.26 |
| xhigh | 53 / $5.98 | 52 / $2.31 |
| High | 51 / $3.91 | 51 / $1.73 |
| Medium | 49 / $2.98 | 50 / $1.54 |
| Low | 47 / $2.37 | 46 / $0.82 |
Scores are Artificial Analysis Intelligence Index v4.3.2; costs are Artificial Analysis (2026).
At medium effort Astra scores 50 against 49 for half the cost per task, which is the clearest case for Astra on price.
Long Context and Caching Economics
Both models offer a context window of about one million tokens. The difference is price: Anthropic lists one price and a $0.25 cache read, while OpenAI charges more above 272K tokens and $1.00 for cached reads. Long agent loops that reuse context favor cheaper cache reads.
According to DevelopersDigest (2026), prompts above 272K tokens on Astra are billed at 2x the input and cache rates and 1.5x the output rate. Anthropic estimates that Fable 5.1's cheaper cache reads cut cost by about 25% for typical workloads and up to about 45% for highly agentic work, compared with Fable 5 (Anthropic).
OpenAI reports Astra at 100.0% on MRCR v2 from 256K to 512K tokens and 96.3% from 512K to 1M, against 91.5% and 73.8% for GPT-5.6 Sol. No Fable 5.1 figure appears in that table.
Speed and Latency
Fable 5.1 generates more tokens per second than Astra at every effort level Artificial Analysis tested. It also reaches the first token sooner at every level except medium. The gap is widest at high effort, where time to first token is 20.9 seconds for Fable 5.1 and 64.4 seconds for Astra.
According to Artificial Analysis (2026), output speed at max effort is 69 tokens per second for Fable 5.1 and 64 for Astra.
Coding, Computer Use and Research Agents
For terminal and coding agents, Astra holds the higher vendor-reported scores. For computer use, the two vendors grade OSWorld 2.0 differently, so their numbers cannot be compared directly. For research agents with tools, Fable 5.1 holds the stronger published score.
Anthropic reports Fable 5.1 at 73.4% on CursorBench 3.2.0, ahead of GPT-5.6 Sol at 67.2%. No Astra CursorBench score appears in the sources we reviewed. OpenAI reports Astra at 72.6% on OSWorld 2.0 (offline, partial credit), while Anthropic reports 77.9% for Fable 5.1 on partial credit and 41.7% on strict grading, with scores of zero where safeguards intervened.
Availability and Data Controls
Both models are available through their vendors' APIs and major clouds. Fable 5.1 reaches Amazon Bedrock, Google Cloud and Microsoft Foundry, and Astra reaches Microsoft Azure and Amazon Bedrock.
According to OpenAI (2026), Astra rolled out first to a limited set of organizations, and Enterprise admins must enable it, because it is off by default. Anthropic offers Fable 5.1 with zero data retention for eligible customers and is rolling out Enterprise Frontier Safeguards from fall 2026 (Anthropic). Mythos 5.1, the same model with more permissive safeguards, is limited to trusted access programs for US organizations.
Enterprise Security and Governance for Tool-Using Agents
Neither model removes the need for runtime controls. Both vendors publish strong safeguards and disclose residual risk in their own documents. Prompt injection remains the top risk for tool-using agents: according to OWASP (2025), it ranks first on its Top 10 for LLM Applications.
| Risk signal | Claude Fable 5.1 | GPT-6 Astra |
|---|---|---|
| Cyber capability | Penetration testing, exploit generation and binary scanning are redirected to Opus models; Mythos 5.1 limited to verified programs | First OpenAI model at Critical cyber level; advanced exploit tasks refused at launch |
| Prompt injection | No score in the sources reviewed | "Significantly more resistant" than GPT-5.6 Sol, no rate published |
| Agent misbehavior | Permission-hook workarounds in under 0.01% of monitored completions | Computer-use safety benchmark 2.4% against 22.0% for GPT-5.6 Sol (lower is better) |
| Disclosed gap | Less honest under pressure than prior models | Written reasoning harder to monitor than GPT-5.6 Sol's |
Sources: Anthropic system card (September 1, 2026) and OpenAI safety overview (September 3, 2026). The Anthropic card covers Fable 5.1 and Mythos 5.1 together, so some agentic findings may reflect Mythos 5.1.
These are vendor tests, and none covers your tools, data or MCP servers. A model that follows instructions better also follows planted instructions better, so enterprises enforce policy outside the model. NeuralTrust's Agent Gateway (TrustGate) sits between agents, models and MCP servers, ships 200+ pre-built MCP servers and applies access policy per request. Agent Runtime Security (TrustGuard) inspects prompts, tool results and responses for prompt injection and data leakage. NeuralTrust holds four Gartner Hype Cycle 2026 recognitions in AI Runtime Defense, and runs self-hosted or as a managed service in the EU or US. For model-specific detail, read our Claude Opus 5.5 enterprise security and GPT-6 Astra CISO security analyses.
Which Should You Choose?
Choose Fable 5.1 for long, cache-heavy reasoning and research agents where accuracy per run matters more than cost. Choose Astra for terminal, computer-use and automation agents where cost per task is the constraint.
| Use case | Better fit | Why |
|---|---|---|
| Long research agent with heavy caching | Fable 5.1 | 65.0% on Humanity's Last Exam with tools; $0.25 cache reads |
| Terminal and DevOps automation | Astra | 57.9% on Terminal-Bench 4.0; 41.4% on AutomationBench |
| High-volume workflows | Astra | $0.82 to $3.26 per task against $2.37 to $7.63 |
| Prompts above 272K tokens | Fable 5.1 | No surcharge listed |
| Math and science reasoning | Astra | 97.6% on FrontierMath Tier 4; 96.0% on GPQA Diamond |
| Multi-cloud procurement | Either | Fable 5.1 on three clouds, Astra on two |
Conclusion
The Claude Fable 5.1 vs GPT-6 Astra decision is a workload decision, not a leaderboard decision. Fable 5.1 ties Astra on the independent intelligence index, leads on reasoning with tools and has cheaper cache reads. Astra leads agentic and coding scores in vendor tables and costs less per task at every effort level tested. Run both on your own tasks, measure cost per completed task, and keep access policy and prompt-injection controls outside the model.
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Related Comparisons
- Claude vs ChatGPT: Benchmark and Models Comparison
- GPT-6 Sol vs Luna: Security, Cost and Performance
FAQs about Claude Fable 5.1 vs GPT-6 Astra
1. Is Claude Fable 5.1 better than GPT-6 Astra?
Neither is better on every task. They tie at 53 on Artificial Analysis Intelligence Index v4.3.2 at max effort. Fable 5.1 leads on Humanity's Last Exam with tools, while Astra leads most coding and automation scores in OpenAI's table. Test both on your own workload before you commit.
2. Which is cheaper, Fable 5.1 or GPT-6 Astra?
List prices are identical at $10 input and $50 output per million tokens. Astra costs less per task in Artificial Analysis testing, from $0.82 at low effort to $3.26 at max effort, against $2.37 to $7.63 for Fable 5.1. Fable 5.1 has cheaper cache reads at $0.25 against $1.00.
3. Which model has the larger context window?
Astra reports a 1.05M-token window and Fable 5.1 a 1M-token window, so the sizes are effectively equal. The pricing differs: Astra prompts above 272K tokens cost more, according to DevelopersDigest, while Anthropic lists a single price for Fable 5.1.
4. Which model is better for coding agents?
Astra holds the higher vendor-reported scores on Terminal-Bench 4.0 (57.9% against 55.8%) and DeepSWE v1.1 (74.1% against 67.4%). Anthropic reports Fable 5.1 at 73.4% on CursorBench 3.2.0, with no Astra score published. Run both inside your own repositories and toolchain.
5. Is it safe to give these models tool and MCP access?
Only with controls outside the model. Both vendors publish safeguards and disclose gaps: Astra's reasoning is harder to monitor, and Fable 5.1 shows rare permission-hook workarounds. A runtime layer that enforces access policy and screens tool results for prompt injection reduces the risk either model leaves open.
6. Can I use both models through one gateway?
Yes. An AI gateway can expose both models behind one endpoint, apply the same access policy and logging to each, and route requests by task or cost. That also lets you switch models without rewriting agents.
About the Author
Roger Howroyd is Head of Global SEO and AI at NeuralTrust, where he leads the company's search strategy across SEO, AEO, GEO, and LLM optimization. He specializes in AI-powered search, content strategy, and SEM. Connect on LinkedIn.
NeuralTrust is the leading platform for securing and scaling AI agents. Named a Pioneer in the Gartner Emerging Market Quadrant for AI Application Security 2026, recognized across four Gartner Hype Cycle reports in 2026, and featured in the Gartner Market Guide for Guardian Agents 2026, the Gartner Market Guide for AI Gateways 2025 and the KuppingerCole Leadership Compass for Generative AI Defense 2025. Headquartered in Barcelona with offices in London and New York. ISO 27001 certified.
Sources
- Anthropic, Introducing Claude Fable 5.1 and Claude Mythos 5.1 (September 2026)
- Claude Platform Docs, Claude Fable 5.1 overview (2026)
- Anthropic, System Card: Claude Fable 5.1 and Claude Mythos 5.1 (September 1, 2026)
- OpenAI, GPT-6 Astra: A new generation of intelligence (September 2026, updated September 29)
- OpenAI, Safety overview: GPT-6 Astra (September 3, 2026)
- Artificial Analysis, GPT-6 Astra vs. Claude Fable 5.1: Release Comparison (2026)
- DevelopersDigest, GPT-6 Astra Release Guide (September 28, 2026)
- OWASP, LLM01:2025 Prompt Injection (2025)
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