OpenAI-native frontier agents
Strong fit when you need Responses API tooling, async tool calls, mid-turn steering, computer use, or OpenAI’s highest-capability model for end-to-end work.
Open Astra reference ↗FRONTIER MODEL COMPARISON / VERIFIED 2026-09-26
GPT-6 Astra
vs Claude Fable 5.1.
Two frontier models with the same $10 / $50 headline token price and nearly the same context capacity. The real differences appear in long-context billing, cache economics, reasoning controls, agent architecture and task-level performance.
QUICK ANSWER
Same sticker price. Different operating economics and agent behavior.
GPT-6 Astra and Claude Fable 5.1 both list $10 input and $50 output per million tokens and both support 128K maximum output. Astra has a slightly larger 1.05M context window and a broader set of OpenAI-native agent controls. Fable 5.1 has much cheaper cache reads and avoids Astra's long-context price multiplier above 272K input tokens. Independent evaluations currently show no clean universal capability winner across task types.
Long-horizon, cache-heavy work
Strong fit when long conversations reuse large contexts, demanding reasoning matters, and Anthropic’s long-running agent workflow fits your architecture.
Open Fable reference ↗Evaluate cost per accepted task
Measure completion quality, retries, tool errors, reasoning tokens, context reuse, latency and human correction. Identical per-token list prices do not imply identical total bills.
OFFICIAL SPECIFICATIONS
Side-by-side facts.
| Model | Provider | Positioning | Context | Max output | Reasoning | Modalities | Input | Cached | Output |
|---|---|---|---|---|---|---|---|---|---|
| GPT-6 Astragpt-6-astra | OpenAI | Hardest end-to-end work | 1,050,000 | 128,000 | low · medium · high · xhigh · max | Text + image input · text output | $10.00 | $1.00 | $50.00 |
| Claude Fable 5.1claude-fable-5-1 | Anthropic | Demanding reasoning and long-horizon agentic work | 1,000,000 | 128,000 | Adaptive · always on · low → max · default high | Text + image input · text output | $10.00 | $0.25 | $50.00 |
Both models list the same Standard short-context input/output token prices. Astra’s cached-input price is $1 / MTok; Fable 5.1’s cache-read price is $0.25 / MTok. OpenAI applies a long-context multiplier to Astra above 272K input tokens.
PRICING
Headline pricing is identical. Workload pricing is not.
Short prompts make the two models look economically identical. Large contexts and repeated cached prefixes create a different result.
100K input + 10K output
GPT-6 Astra$1.5100K input + 10K output
Claude Fable 5.1$1.5100K input + 10K output
10M input + 1M output
GPT-6 Astra$15010M input + 1M output
Claude Fable 5.1$15010M input + 1M output
500K input + 50K output
GPT-6 Astra$13.75500K input + 50K output
Claude Fable 5.1$7.5500K input + 50K output
The long-context break point
For Astra, prompts above 272K input tokens are billed at 2× input/cache and 1.5× output rates for the entire request. In the 500K + 50K example, that changes Astra from a nominal $7.50 short-context calculation to about $13.75. Anthropic lists Fable 5.1 at its standard $10 / $50 rates across the 1M context window, producing about $7.50 for the same token counts.
CACHE ECONOMICS
Fable 5.1 is cheaper when the same large prefix is reused.
Cache economics matter for coding agents, long research sessions and enterprise workflows that repeatedly reuse repository context, policy text, tool schemas or accumulated task state.
1M cached input + 100K output
GPT-6 Astra$61M cached input + 100K output
Claude Fable 5.1$5.251M cached input + 100K output
GPT-6 Astra
Short-context cached input is $1 / MTok and cache writes are $12.50 / MTok. Above the long-context threshold, those rates rise to $2 and $25 respectively.
Claude Fable 5.1
Cache reads are $0.25 / MTok, five-minute cache writes are $12.50 / MTok and one-hour writes are $20 / MTok. Anthropic explicitly highlights cheaper cache reads as a major part of Fable 5.1’s improved agent economics.
BATCH & FAST INFERENCE
Same Batch token economics; different premium-serving options.
Batch · 10M input + 1M output
GPT-6 Astra$75Batch · 10M input + 1M output
Claude Fable 5.1$75Batch · 10M input + 1M output
OPENAI BATCH / FLEX50% of Standard
OpenAI lists Batch and Flex at 50% of Standard rates for GPT-6 models, useful for workloads that can trade immediacy for lower cost.
ANTHROPIC BATCH50% discount
Anthropic also lists a 50% input/output discount through the Batch API for Fable 5.1.
ASTRA FAST MODE2× Standard pricing
OpenAI offers Fast mode for GPT-6 Astra at twice the applicable Standard token rates. OpenAI notes that Astra Fast mode does not include a latency SLA.
CONTEXT & OUTPUT
1.05M vs 1M context is less important than how the context is billed and reused.
GPT-6 Astra lists a 1,050,000-token context window; Claude Fable 5.1 lists 1,000,000 tokens. Both list 128,000 maximum output tokens.
The raw context advantage is about 5%, which is rarely decisive by itself. In production, retrieval quality, context compaction, cache reuse, tool access and how quickly the agent can recover relevant old information matter more than the extra 50K tokens.
Cost is the more important long-context difference: Astra changes pricing after 272K input tokens, while Fable 5.1’s published model pricing does not add the same threshold multiplier.
REASONING
Both support deep effort controls, but they reason differently.
Explicit effort levels
Astra supports low, medium, high, xhigh and max. It does not support none. OpenAI also allows changing effort mid-conversation with a configuration update while preserving the cached prompt prefix in compatible workflows.
Adaptive thinking, always on
Fable decides when and how much to think within the effort envelope. It supports low through max and defaults to high on the API. Thinking cannot be disabled.
Match effort settings deliberately
Independent results change materially with reasoning configuration. Compare representative effort levels, not one model at max against another at a default or cheaper setting.
CODING
Vendor tests and independent tests tell a more nuanced story than a single coding score.
57.9% vs 55.8%
OpenAI reports Astra at 57.9% and Fable 5.1 at 55.8% on Terminal-Bench 4.0 under its launch evaluation setup.
74.1% vs 67.4%
OpenAI reports a larger Astra lead on DeepSWE in the same launch table. The harness and model settings matter when comparing these numbers to other public runs.
73.4% for Fable 5.1
Anthropic reports strong Fable results on CursorBench and positions the model around long-running coding and root-cause work. This is a vendor evaluation, not a direct neutral head-to-head with current Astra under one shared harness.
Task mix changes the picture
Artificial Analysis currently reports different results across reasoning settings and benchmark categories, reinforcing that one coding benchmark does not define a universal leader.
Measure mergeable work
Use your repository, test suite, CI, tools and review standards. Track accepted patches, regressions, retry loops and engineer correction time rather than only benchmark accuracy.
Agent coherence matters
For large refactors and migrations, staying on-task across many tool calls can be more valuable than a few points on a one-shot benchmark.
INDEPENDENT BENCHMARK EVIDENCE
Artificial Analysis currently shows a tie overall—and different strengths underneath.
The independent Intelligence Index v4.3.2 gives GPT-6 Astra at max effort and Claude Fable 5.1 at max effort an overall score of 53. Individual tests split in different directions.
53 vs 53
Artificial Analysis reports an overall tie for Astra max and Fable 5.1 max in its current v4.3.2 index.
68% Astra · 59% Fable
In the max-effort comparison, Astra leads this private workflow-automation evaluation.
59% Astra · 52% Fable
Artificial Analysis reports an Astra lead in this terminal-oriented agent benchmark at the compared settings.
56% Astra · 63% Fable
Fable leads the scientific coding evaluation in the same independent max-effort comparison.
55% Astra · 59% Fable
Fable leads this reasoning evaluation in the current Artificial Analysis comparison.
81% Astra · 85% Fable
Fable leads the long-context reasoning result under the same independent comparison configuration.
Why benchmark pages disagree
Reasoning effort, fallback behavior, harness design, tool access, prompt format and benchmark revisions all change results. OpenAI, Anthropic and independent evaluators do not always run identical configurations. Treat benchmarks as workload signals, then reproduce the most relevant tasks inside your own system.
AGENTS & TOOLS
The biggest differences may sit outside the base model.
GPT-6 · ASYNC TOOLSReason while tools run
Astra can continue independent reasoning or other work while application-run function/custom tools execute asynchronously.
GPT-6 · MID-TURN STEERINGChange direction while running
Over Responses WebSocket connections, applications can send additional user requirements during an in-progress response.
GPT-6 · MODEL STACKResponses + computer use + multi-agent
OpenAI documents computer use, Structured Outputs, programmatic tools, multi-agent orchestration, persisted reasoning and compaction across GPT-6 workflows.
FABLE · PER-MESSAGE EFFORTBeta reasoning control
Anthropic supports changing effort later in a conversation without rewriting the stable prompt prefix in supported beta workflows.
FABLE · TURN-SCOPED SYSTEM MESSAGESBeta local guidance
Add temporary system guidance for a turn without rewriting earlier history, helping preserve cache and long-running conversation state.
FABLE · PROGRESS UPDATESUser-visible long-task status
Anthropic supports readable progress-update content between tool calls when the application opts into the relevant beta display behavior.
COMPUTER USE & PROFESSIONAL WORK
Both target complete workflows, not just chat responses.
Computer and browser workflows
OpenAI positions Astra as state-of-the-art across computer use, browsing, software engineering and professional work, and exposes computer-use tooling through its agent stack.
Documents, spreadsheets and slides
Anthropic specifically highlights complete knowledge-work artifacts and long research/coding workflows, rather than only short reasoning answers.
Tool boundaries still decide safety
Neither model should receive unrestricted credentials or execution rights simply because its benchmark or alignment results improve. Permissions, sandboxing and approvals remain application controls.
MIGRATION & INTEGRATION
The model switch can require application changes.
01Astra tool use belongs in Responses.
OpenAI recommends Responses for GPT-6 tooling. Chat Completions support is more limited for tool-oriented reasoning workflows.
02Astra does not support none reasoning.
If an older route used minimal or no reasoning, start with low and re-evaluate latency and cost.
03Fable forced tool choice changed.
Anthropic documents breaking changes around forced tool-use patterns. Use current tool-selection guidance and regression-test long agent loops.
04Fable thinking blocks are conversation state.
Earlier Claude models cannot read Fable 5.1 thinking blocks, and editing earlier conversation history can invalidate them.
05Both reward stable prompt prefixes.
Design system instructions, tools and reusable context so caching can work instead of continuously mutating the entire prefix.
06Re-run effort sweeps.
Do not assume high, xhigh or max is automatically optimal. Compare quality, latency and total token usage on representative tasks.
WHEN TO CHOOSE
Use workload shape to decide, not a universal model ranking.
Tool-rich OpenAI agents
Consider Astra when async tools, mid-turn steering, computer use or other GPT-6 Responses features materially improve your workflow.
Hard end-to-end professional work
OpenAI positions Astra at the top of its stack for complex reasoning, coding, research and full workflow execution.
Cache-heavy long sessions
Fable’s low cache-read price and standard pricing across the 1M context window can matter for long-running context-heavy agents.
Demanding research and knowledge work
Anthropic positions Fable for hard reasoning and long-horizon agentic work when Opus 5.5 at higher effort still fails the required eval bar.
LIMITS & CAVEATS
What can make an apparently simple comparison wrong.
01Same token price does not mean same task price.
Long-context multipliers, cache reuse, reasoning tokens, tool calls and retries can move the final bill substantially.
02Benchmark configuration matters.
Reasoning effort, fallback, harness and tool access can reverse the apparent advantage on individual tests.
03Fable is not Anthropic's default recommendation for most workloads.
Anthropic says to start with Opus 5.5 for most tasks and move to Fable when higher-effort Opus evals still fall short.
04Astra long context costs more.
Requests beyond 272K input tokens use the higher rates for the full request, not only the tokens above the threshold.
05Safety claims are not authorization controls.
Improved alignment or safeguard benchmarks do not replace least privilege, sandboxing, audit logs or human approval for consequential actions.
06Consumer apps and APIs are different products.
This page compares model and API characteristics; ChatGPT and Claude app plans add routing, connectors, limits and interface features of their own.
OFFICIAL & INDEPENDENT SOURCES
OpenAI · GPT-6 Astra model card↗OpenAI · GPT-6 model guidance↗OpenAI · API pricing↗OpenAI · GPT-6 Astra launch↗Anthropic · Claude Fable 5.1 model page↗Anthropic · Fable 5.1 announcement↗Anthropic · Claude pricing↗Artificial Analysis · Astra vs Fable 5.1↗Artificial Analysis · Intelligence Index v4.3↗QUICK ANSWERS
Which is cheaper, GPT-6 Astra or Claude Fable 5.1?
At short-context Standard token rates, neither is cheaper: both list $10 per million input tokens and $50 per million output tokens. The economics diverge with long context and caching. OpenAI prices Astra prompts above 272K input tokens at 2x input/cache rates and 1.5x output for the full request, while Anthropic lists Fable 5.1 at standard token rates across its 1M context window. Fable also lists a lower cache-read rate of $0.25 per million tokens versus $1 for Astra.
Which has the larger context window?
GPT-6 Astra lists a 1,050,000-token context window, while Claude Fable 5.1 lists 1,000,000 tokens. Both support up to 128,000 output tokens. The roughly 5% context difference is usually less important than pricing, retrieval, caching and tool architecture.
Which is better for coding?
Both vendors position these models for frontier software engineering. OpenAI reports Astra ahead of Fable 5.1 on several coding evaluations including Terminal-Bench 4.0 and DeepSWE, while independent Artificial Analysis results vary by task and reasoning configuration. A production choice should be based on the same repository, tools, tests and acceptance criteria rather than a universal coding winner.
Which is better for long-running AI agents?
Both are designed for long-horizon work. GPT-6 Astra adds async tool calling, mid-turn steering, changing reasoning effort mid-conversation, multi-agent orchestration and persisted reasoning in the Responses API. Claude Fable 5.1 adds adaptive thinking, per-message effort beta, turn-scoped system messages beta, readable progress updates and very low cache-read pricing. The better fit depends on your agent architecture and workload economics.
How do reasoning controls differ?
GPT-6 Astra supports low, medium, high, xhigh and max reasoning effort and does not support none. Claude Fable 5.1 uses adaptive thinking that is always on and supports low, medium, high, xhigh and max effort, with high as the API default.
How do cached-input prices compare?
GPT-6 Astra lists $1 per million cached input tokens at short context. Claude Fable 5.1 lists $0.25 per million cache-read tokens. Both have separate cache-write pricing. The value of caching depends on how much context is reused and how often.
How does long-context pricing differ?
OpenAI states that Astra prompts above 272K input tokens use $20 input, $2 cached input, $25 cache writes and $75 output per million tokens for the full request. Anthropic's Fable 5.1 model page lists standard $10 input and $50 output token rates across the model's 1M context window.
What do independent benchmarks say?
Artificial Analysis Intelligence Index v4.3.2 gives GPT-6 Astra at max effort and Claude Fable 5.1 at max effort the same overall Intelligence Index score of 53, while individual benchmark results differ. This is evidence that task mix and reasoning configuration matter more than a single overall ranking.
Should I use Claude Opus 5.5 instead of Fable 5.1?
Anthropic recommends starting with Opus 5.5 for most workloads and moving to Fable 5.1 when higher-effort Opus evaluations still fall short. Fable 5.1 is a premium escalation tier, not Anthropic's default recommendation for every task.
Does GPT-6 Astra support Fast mode?
Yes. OpenAI offers Fast mode for GPT-6 Astra at 2x Standard pricing. OpenAI notes that Fast mode for Astra does not include a latency SLA.