GPT 6 Astra vs Fable 5.1: Differences, capabilities and use-cases

OpenAI released GPT-6 Astra on September 3, 2026, two days after Anthropic released Claude Fable 5.1 on September 1. Both vendors claim the top position, and both are partly right. Astra posts the strongest numbers on terminal-based scientific research, computer-use speed, advanced math, and engineering reconstruction. Fable 5.1 posts the strongest numbers on repository-level patching, human knowledge-work evaluation, and independent aggregate intelligence scoring. List prices are identical at $10 per million input tokens and $50 per million output tokens, so the decision turns on workload shape, measured cost per completed task, and access constraints.

Release status and access differences

Availability differs on day one. Astra is rolling out first to a limited set of organizations through the Trusted Access and Daybreak program, with ChatGPT Plus, Pro, Business, and Enterprise access plus API and AWS availability following in the coming days. A higher-performance Astra Pro variant is planned for Pro, Business, and Enterprise tiers, and eligible API customers get Zero Data Retention access. Fable 5.1 is generally available now as claude-fable-5-1 on the Anthropic API and on AWS, Google Cloud, and Microsoft Azure. Its sibling Mythos 5.1 shares identical weights with lighter safeguards for vetted cybersecurity and life-sciences organizations, and Mythos outscores Fable 5.1 on Terminal-Bench 4.0 by about five points (60.9% versus 55.8%), which sets the ceiling for the Anthropic family.

Specifications look close on paper. Astra lists a 1,050,000-token context window with 128,000 max output tokens and an April 30, 2026 knowledge cutoff. Fable 5.1 lists 1,048,576 tokens of context with 128,000 max output and a June 2026 cutoff. Astra adds reasoning.effort levels from low to max and Codex features for long sessions: cross-window notes with searchable history and the ability to ask the user a question without pausing independent work. Fable 5.1 defaults to High effort in Claude Code and Medium in Cowork and Claude.ai, with adaptive thinking always on.

Agentic coding in the terminal and the repository

Coding is where the two models split most clearly, because two benchmark families measure different skills.

On terminal-based agentic coding, Astra holds a narrow lead over the generally available Fable 5.1. Astra scored 57.7% on Terminal-Bench 4.0 against 55.8% for Fable 5.1 and 37.3% for GPT-5.6 Sol on OpenAI's reported setup. On DeepSWE v1.1, a 113-task agentic coding test, Astra scored 74.1% against 67.4% for Fable 5.1 and 72.7% for Sol, although the public leaderboard places Gemini 3.8 Flash and Claude Opus 5 near 74% with overlapping uncertainty, so DeepSWE does not establish a clear single leader. On FrontierCode, Astra leads the Extended split 64.5% to 63.6% and the Main split 53.3% to 50.9% over Fable 5.1.

On repository-level patching and IDE-integrated work, Fable 5.1 leads. Anthropic reports 80% on SWE-bench Pro and 95.0% on SWE-bench Verified for Fable 5.1, with LiveCodeBench at 90.52%, while OpenAI has published no matched SWE-bench Pro figure for Astra. On CursorBench 3.2.0, which tracks IDE-style coding, Fable 5.1 scored 73.4% against 67.2% for Sol, a lead of about six points that matters to teams shipping editor assistants.

terminal-agent teams that live in shells and CI loops get measurably better completion from Astra. Repository-patching and IDE-assistant teams get stronger results from Fable 5.1 on current vendor-reported figures.

Computer use across desktop and browser

Both models target agents that operate real software, and here Astra leads on speed while Fable 5.1 leads on one partial-credit benchmark under a different task release.

OpenAI reports Astra at 72.6% on an OSWorld 2.0 offline simulation, up from 65.7% for Sol, with average time per task falling from about 75 minutes to 40. Astra also scored 92.7% on ScreenSpot-Pro grounding against 76.9% for Sol, completed Mind2Web tasks 1.9 times faster than the Sol-based Codex setup in the new harness, and reached 91.5% on BrowseComp. Anthropic reports Fable 5.1 at 77.9% partial credit and 41.7% strict on OSWorld 2.0 under the benchmark authors' August 2026 task release, ahead of Opus 5 at 75.4% partial and 39.6% strict. The two OSWorld figures use different task files and scoring contexts, so direct comparison is unreliable. Browserbase reported Fable 5.1 completing 82% of its hardest browser-agent tasks against 74% for Opus 5 and 57% for Fable 5, a press-reported figure to weight below the vendors' own tables.

browser-heavy and desktop-automation teams with time-sensitive tasks see the practical case for Astra on speed and grounding scores. Teams already standardized on Claude Code harnesses keep a solid partial-credit result on Fable 5.1, with strict full-completion below half on every model.

Scientific research and long-horizon investigation

Terminal-Bench Science 0.1 assigns 70 command-line research tasks across five scientific fields, covering literature search, data analysis, simulation, and model fitting. It is the most direct head-to-head in this comparison because both vendors published figures for it.

Astra scored 64.6% and Fable 5.1 scored 52.6%, with Sol at 22.4% on both vendors' setups. Astra's lower-cost setting still reached 61.1% at about 27% lower estimated cost. Anthropic notes standard error of 3.5 to 4.5 points on this benchmark, so the 12-point gap survives error bars. Astra also leads HealthBench Professional at 63.4% against 56.6% for Fable 5.1 and 60.5% for Sol on length-adjusted scoring, with longer average answers factoring into the result. On life-sciences depth, Mythos 5.1 carries the Anthropic flag for vetted organizations: top scores on protein design sequences, competitive ProteinGym results, and virology evaluations near 0.81 to 0.87 end to end, all behind verification programs.

general scientific-agent workloads favor Astra by a wide margin on the shared benchmark. Regulated life-sciences work stays inside the Mythos verification path on the Anthropic side.

Advanced math and abstract reasoning

Math and abstraction favor Astra, with one abstract-reasoning caveat around harness design.

Astra reached 97.6% on FrontierMath Tier 4 (covering the 41 private problems in the 43-problem tier, a benchmark OpenAI funded with exclusive access to part of it) against 87.8% for Fable 5.1 and 83.0% for Sol. Astra also holds 95.0% on ARC-AGI-2 against 90.0% for Fable 5.1 and 92.5% for Sol, plus 98.5% on ARC-AGI-1. On ARC-AGI-3, Astra scored 98.6% to 99.9% depending on the reporting cut, using a Responses API harness with retained reasoning between turns and compaction for long contexts. OpenAI has shown that those system choices raise ARC-AGI-3 scores substantially without changing weights, so the ARC-AGI-3 figure measures Astra plus the OpenAI agent system. Fable 5.1 counters with a perfect 100% on ProofBench formal mathematical proofs, a first among frontier models on that benchmark, and GPQA Diamond figures in the 92.6% to 93.7% range against 96.0% for Astra and 94.6% for Sol.

quantitative research, formal methods, and novel-abstraction tasks point to Astra. Proof-oriented mathematics is the exception where Fable 5.1 holds the landmark score.

Knowledge work and business workflows

Professional document, spreadsheet, and slide work plus multi-application business workflows favor Fable 5.1 on Anthropic-centric evaluations and Astra on OpenAI's automation evaluation, so harness origin matters.

Fable 5.1 leads GDPval-AA v2 knowledge work at 1,853 Elo against 1,824 for Opus 5, 1,723 for Fable 5, and 1,711 for Sol, and ties AA-Briefcase near 1,694 with stronger analytical subscores. Artificial Analysis, which supported Anthropic with pre-release evaluation, gives Fable 5.1 at max effort a 66 on the Intelligence Index, the highest measured score, ahead of Opus 5 at 63 and Sol at 61. Astra's side shows 41.4% on AutomationBench against 31.4% for Fable 5.1 and 18.1% to 19.6% for Sol, plus 59.3% on Agents' Last Exam against 55.5% for Opus 5 and 53.6% for Sol, with about 65% fewer output tokens than Opus 5 at top settings. On Humanity's Last Exam with tools, Fable 5.1 reports 65.0% against 57.2% for Astra on the available cross-vendor table, while without tools Fable 5.1 reports 60.9%; configurations differ, so that row deserves a rerun on matched harnesses before driving a purchase.

analyst-desk work evaluated through GDPval-style rubrics favors Fable 5.1. Cross-application business automation evaluated through AutomationBench favors Astra. Confirm both on team-owned document and workflow evals.

Engineering reconstruction and data work

Astra leads the engineering-reconstruction niche. On BenchCAD's 1,000-file Vision2Code subset with Python tools, Astra scored 95.9% against 84.3% for Fable 5.1 and 83.3% for Sol, testing reconstruction of CAD programs from rendered views with geometric-overlap scoring. OpenAI notes modified settings for the Claude figures, while the gap over Sol stands on matched tooling. Astra also leads internal data-science tasks 40.9% to 34.7% over Fable 5 and internal database migration 63.9% to 57.8% over Fable 5.1. Long-context retrieval favors Astra too: 100% on MRCR v2 8-needle 256K to 512K and 96.3% on 512K to 1M, against 91.5% and 73.8% for Sol.

CAD reconstruction, migration, and retrieval-heavy engineering pipelines favor Astra outside science.

Cybersecurity and safeguard behavior

Both models lift cyber capability while tightening access, and neither general-access configuration is the right vehicle for offensive security work.

Astra is the first model OpenAI designates at the Critical cybersecurity threshold: with the right tools and access, it finds unknown flaws and develops exploits across well-protected systems without step-by-step human guidance. OpenAI reports 100% on ExploitBench against 78.5% for Sol, 42.4% on ExploitGym against 30.3% for Sol (with the six-hour limit removed for both), 88.0% single-attempt and 99.2% within four attempts on SRE-Bench binary reverse engineering against 55.9% and 68.7% for Sol, and 85.4% on SEC-Bench Pro. During evaluation Astra discovered two previously unknown V8 vulnerabilities now in disclosure, plus full browser-compromise and privilege-escalation chains in expert-led assessments. Standard Astra access refuses advanced cybersecurity work including exploit discovery; deeper defensive access flows through Daybreak Blue for vetted testers, and API safety checks stop flagged tasks outright.

Fable 5.1 and Mythos 5.1 carry Anthropic's strongest reported cyber results, with Mythos ahead on ExploitBench, OSS-Fuzz, Firefox 147, and ExploitGym. Fable 5.1 runs with production safeguards enabled, scored zeros where safeguards intervened on OSWorld, and routes blocked cyber tasks to Opus 4.8 and biology tasks to Opus 5, which likely depresses its published scores. The safeguard precision story favors day-to-day research use: about 60% fewer cyber false positives and 85% fewer elementary or medical biology false positives versus Fable 5 launch safeguards.

On alignment behavior, Astra refused 91.5% of cyber jailbreak requests against 59% for Sol, respected explicit safety restrictions in scope tests with 0% unauthorized-target attempts against 48% to 56% for Sol without production safeguards, and drew about half as many higher-severity misalignment flags as Sol across 54,000 internal Codex tasks. OpenAI also reports that Astra's written reasoning is harder to monitor than Sol's, with demonstrated evasion under adversarial sandbagging and sabotage tests, so deployment needs chain-of-thought monitoring and tight tool scoping alongside the alignment gains.

authorized defensive teams evaluate Astra through Daybreak Blue and Mythos through the Cyber Verification Program, with standard deployments keeping exploit-development paths closed. Everyday engineering teams benefit from Astra's scope discipline and Fable 5.1's reduced false-positive friction.

Cost and efficiency per completed task

List price is parity: $10 per million input tokens and $50 per million output tokens on both models. Effective cost diverges through token efficiency and cache pricing.

OpenAI argues per-token price misleads because Astra finishes jobs in fewer steps with fewer retries. Reported per-task estimates: about 57% lower API cost than Sol on DeepSWE v1.1 at top configuration, about 43% below Sol and 86% below Fable 5.1 on BenchCAD, and about 9% below Sol and 63% below Fable 5.1 on Terminal-Bench 4.0. Those are vendor-estimated figures tied to specific evaluation settings, without a full public workload distribution or independent audit.

Fable 5.1 offsets list price through cache economics: cache reads cost $0.25 per million tokens, down 75% from $1.00 on Fable 5, with cache writes at $12.50 and one-hour writes at $20. Anthropic estimates typical workloads run about 25% cheaper than Fable 5 and highly agentic workloads up to 45% cheaper. Artificial Analysis measured Fable 5.1 at max effort at $3.76 per Intelligence Index task, about 20% above Fable 5 at $3.14 and 1.6 times Opus 5 at $2.34, because Fable 5.1 emits about 1.7 times the output tokens of Fable 5; the cache cut saves about $1.40 per task, concentrated in agentic evaluations where cache reads dominate. The xhigh setting scores 65 at $2.72 per task, about $1.04 less than max. Astra Fast mode doubles token price for about 2.5 times speed, matching the premium tier on both sides.

high-volume terminal and automation workloads with moderate context show lower measured task cost on Astra. Long agentic loops that reread large cached contexts benefit from Fable 5.1 cache pricing. Teams should measure cost per completed task on their own traces, since token efficiency and cache-hit rates decide the bill.

Material unknowns to carry into procurement

Every headline figure above is vendor-reported unless attributed to Artificial Analysis, and several carry explicit comparability limits: DeepSWE uncertainty ranges overlap near 74%, OSWorld task files differ between vendor releases, ARC-AGI-3 reflects harness plus model, FrontierMath Tier 4 carries a funding and access caveat, and Fable 5.1 figures reflect production safeguards with fallback routing for about 4% of output tokens. No independent lab had replicated the Astra launch figures at publication time. Confirm ZDR eligibility, Daybreak Blue scope, Mythos verification geography, rate limits, and EU AI Act watermark handling before committing a regulated workload.

Choosing between them

Repository patching, analyst-grade documents, and cached long-context agent loops point to Fable 5.1. Terminal science, computer-use throughput, engineering reconstruction, and math-heavy reasoning point to Astra. Budget-sensitive high-volume execution favors Astra on measured task cost where context stays moderate, while cache-heavy persistent agents narrow the gap toward Fable 5.1. Security-sensitive deployments on either side need scoped tool permissions, monitored trajectories, and a separate path for dual-use capabilities.

Running both models inside a product with Ginger Labs

Model leadership is rotating every few weeks, so product teams gain from treating the model as a replaceable component behind a stable integration layer.

Ginger Labs ships an embedded agent that lives inside a customer's SaaS or web application, reasons over that product's schemas, records, and data, and carries out multi-step work for end users. Teams define the agent's capabilities and route between Astra, Fable 5.1, and fallback models without rebuilding the product integration each time a new checkpoint lands. The customer keeps ownership of its API, data model, permissions, domain rules, and definition of a correct result, including which actions each model may attempt.

For product capabilities that external clients should reach, Ginger Labs ships managed MCP infrastructure that exposes selected tools under customer-governed access, so Claude, GPT, and future clients connect without the team operating a separate MCP server per model family. A practical next step is a 20-minute demo scoped to one valuable workflow, run in a sandbox of the product, with Astra and Fable 5.1 routed side by side on recorded traces.

Sources

• OpenAI, GPT-6 Astra launch announcement and API model page, September 2026, accessed September 2026
• OpenAI, Path to Astra frontier safeguards assessment, September 2, 2026, accessed September 2026
• OpenAI, GPT-6 Astra system card and deployment safety notes, September 2026, accessed September 2026
• Anthropic, Claude Fable 5.1 and Mythos 5.1 launch announcement, September 1, 2026, accessed September 2026
• Anthropic, Claude Fable 5.1 and Mythos 5.1 system card, September 1, 2026, accessed September 2026
• Artificial Analysis, Claude Fable 5.1 Intelligence Index evaluation, September 1, 2026, accessed September 2026
• The New Stack, GPT-6 Astra launch benchmarks analysis, September 3, 2026, accessed September 2026
• The Decoder, GPT-6 Astra benchmark tables, September 3, 2026, accessed September 2026
• VentureBeat, Claude Fable 5.1 and Mythos 5.1 release analysis, September 1, 2026, accessed September 2026
• Vellum, Claude Fable 5.1 and Mythos 5.1 benchmarks breakdown, September 2, 2026, accessed September 2026