The artificial intelligence landscape has reached a fascinating inflection point. Anthropic launched Claude Fable 5.1 on September 1, 2026, and OpenAI followed just two days later with GPT-6 Astra on September 3, 2026. Both flagship models occupy the highest-priced $10 per million input tokens and $50 per million output tokens tier, yet they represent fundamentally different engineering philosophies. That split is exactly why this GPT-6 vs Claude Fable 5.1 comparison matters. There is no single universal winner in this head-to-head showdown. Claude Fable 5.1 leads the Finance and Accounting Index and most neutral-harness reasoning components, while GPT-6 Astra dominates LLM Stats composites, token efficiency, output speed, and computer use execution.
To help you navigate this massive generational leap, this article breaks down every shared benchmark, output speed, context window, tokens per task, and cost per task using live data from Artificial Analysis, llm-stats compare page, Epoch-tracked FrontierMath, and the BenchLM taxonomy. If you are shopping in the budget tier instead, see our Gemini 3.8 Flash vs GLM 5.3 Flash comparison and our GPT-6 Sol and Luna benchmarks breakdown, covering OpenAI’s half-price models. For deeper context on OpenAI’s flagship architecture, explore our GPT-6 Astra benchmarks overview, or review Anthropic’s side in our Claude Fable 5.1 deep dive. For the cheapest end of Anthropic’s range, see our breakdown of Claude Haiku 5.5.
At a glance: specs compared
Before diving into complex reasoning evaluations, it helps to examine the baseline hardware and API specifications of both models side by side. Both maintain proprietary architectures, identical output limits, and symmetric tier pricing.
| Specification | OpenAI GPT-6 Astra | Anthropic Claude Fable 5.1 |
|---|---|---|
| Release Date | September 3, 2026 | September 1, 2026 |
| Context Window | 1.05 Million tokens | 1.0 Million tokens |
| Max Output Tokens | 128,000 tokens | 128,000 tokens |
| Knowledge Cutoff | April 30, 2026 | June 2026 |
| Input Price (per 1M) | $10.00 | $10.00 |
| Output Price (per 1M) | $50.00 | $50.00 |
| Prompt Cache Read Price | $1.00 | $0.25 |
| Supported Modalities | Text, Image, Audio, Video | Text, Image, Audio, Video |
| Architecture | Proprietary | Proprietary |
Independent intelligence: who ranks #1 where
When evaluating raw intelligence, rankings depend on the testing harness and the index version. The v4.1.1 Intelligence Index put Fable at 66 and Astra at 61, v4.2 rebased that to 57 for Fable and 55 for Astra, and the current v4.3 Index has the two tied at 53 apiece. Fable still takes the Finance and Accounting Index 59 to 54 and the Coding Agent Index 70 to 67, though Astra costs roughly half as much per coding task ($4.72 versus $9.18).
However, the narrative flips completely when examining LLM Stats composites. LLM Stats places GPT-6 Astra at #1 overall with a composite score of 60.7, edging out Fable at 56.8. A breakdown of subcategories reveals Astra leading in Reasoning (58.7 vs 53.9), Coding (49.0 vs 36.0), Agents (46.4 vs 41.9), and Tool Use (36.6 vs 30.7), while Vision ties at 41.5. This divergence explains why both companies can legitimately claim top-tier dominance: Fable excels in independent academic reasoning composites, whereas Astra excels in pragmatic developer workflows and multi-step tool execution.
| Artificial Analysis v4.3 Component (max effort) | GPT-6 Astra | Claude Fable 5.1 |
|---|---|---|
| Intelligence Index v4.3 | 53 | 53 |
| Finance and Accounting Index | 54 | 59 |
| Intelligence cost per task | $3.26 | $7.63 |
| Time per Intelligence Index task | 4.0 min | 9.5 min |
| Coding Agent Index | 67 | 70 |
| Coding cost per task | $4.72 | $9.18 |
| AA-Briefcase Elo | 1562 | 1662 |
| AA-Briefcase Rubric Score | 52.0% | 61.5% |
| GDPval-AA v2 Elo | 1580 | 1763 |
| AutomationBench-AA score | 68.5% | 59.4% |
| Terminal-Bench v4.0 score | 59.6% | 55.1% |
| Tau3-Banking score | 41.4% | 47.2% |
| SciCode score | 56.5% | 63.1% |
| Humanity’s Last Exam score | 54.7% | 59.1% |
| GDP.pdf all-pass | 32.2% | 28.0% (26.2% at max) |
| CritPt score | 31.7% | 31.1% |
| AA-Omniscience Index score | 44 | 43 |
| AA-Omniscience Accuracy | 63% | 67% |
| AA-Omniscience Hallucination Rate (lower is better) | 51% | 73% |
| AA-LCR v1.1 score | 80.7% | 85.3% |
| GPQA Diamond score | 96.3% | 93.7% |
| AA-AnalystAgent score | 51.2% | 57.5% |
| MMMU-Pro score | 87% | – |
| Harvey LAB-AA criterion pass rate | – | 93.3% |
| EnterpriseOps-Gym-AA | – | 51.1% |
On the new v4.3 Intelligence Index the two models tie at 53 apiece, while Fable takes the Finance and Accounting Index 59 to 54. Across the directly comparable v4.3 components, Fable leads on Briefcase Elo and rubric, GDPval-AA v2, Tau3-Banking, SciCode, HLE, Omniscience Accuracy, LCR v1.1, AnalystAgent, and the Coding Agent Index. Astra’s outright intelligence wins are AutomationBench-AA (68.5% versus 59.4%), Terminal-Bench v4.0 (59.6% versus 55.1%), GDP.pdf (32.2% versus 28.0%), CritPt (31.7% versus 31.1%), the Omniscience Index via a much lower hallucination rate (51% versus 73%), GPQA Diamond (96.3% versus 93.7%), and MMMU-Pro (87%, no Fable score published). On cost and time Astra sweeps: $3.26 versus $7.63 per intelligence task, $4.72 versus $9.18 per coding task, and 4.0 versus 9.5 minutes per task. Harvey LAB-AA (93.3%) and EnterpriseOps-Gym-AA (51.1%) have published Fable scores with no GPT equivalent yet. That ranking was unsettled again on September 22, when Anthropic shipped Claude Opus 5.5, which took first place on the Intelligence Index and beat Fable 5.1 on seven of nine shared benchmarks while costing about 40% less to run than Opus 5, and undercutting Fable 5.1 by 60% on list price per token.
Every shared benchmark, head-to-head
A granular look at all 32 reported benchmarks highlights distinct operational specializations. Where only one vendor published a score, the other column shows a dash, meaning unpublished rather than zero. Shared rows where both vendors agree are the fairest head-to-head signal, and Astra leads most of them, while Fable’s lonely rows (HLE without tools, CursorBench) and Astra’s lonely rows (cyber, BrowseComp, ScreenSpot-Pro) show where each lab chose to compete. The five Artificial Analysis rows at the foot of the table carry the current v4.3 scores, and there Fable takes HLE, SciCode and GPQA goes to Astra, Terminal-Bench v4.0 and AutomationBench-AA go to Astra on the newest runs, while MMMU-Pro has an Astra score only.
| Benchmark | GPT-6 Astra | Claude Fable 5.1 | Primary Source |
|---|---|---|---|
| Terminal-Bench 4.0 (agentic coding) | 57.7% | 55.8% | Both vendors agree (AA v4.3 now 59.6% vs 55.1%) |
| Terminal-Bench Science 0.1 (agentic research) | 64.6% | 52.6% | Both vendors agree |
| AutomationBench (business workflows) | 41.4% | 31.4% | Both vendors agree (AA v4.3 now 68.5% vs 59.4%) |
| DeepSWE v1.1 (agentic coding) | 74.1% | 67.4% | OpenAI table |
| FrontierMath Tier 4 v2 (research math) | 97.6% | 87.8% | OpenAI table |
| GPQA Diamond (science) | 96.0% | 93.7% | OpenAI table |
| Humanity’s Last Exam with tools | 57.2% | 65.0% | Both vendors agree |
| Humanity’s Last Exam, no tools | – | 60.9% | Anthropic |
| ARC-AGI-1 | 98.5% | 97.5% | Both vendors agree |
| ARC-AGI-2 | 95.0% | 90.0% | Both vendors agree |
| ARC-AGI-3, standardized harness | 62.7% | – | OpenAI / ARC Prize |
| ARC-AGI-3, Provider Adapter harness | 99.9% | – | OpenAI |
| BenchCAD Vision2Code | 95.9% | 84.3% | OpenAI table |
| CursorBench 3.2.0 (agentic coding) | – | 73.4% | Anthropic |
| OSWorld 2.0 (computer use) | 72.6% offline, 40 min/task | 41.7% strict | Harness differs, not directly comparable |
| GDPval-AA v2 (knowledge work) | 1629 (AA run) | 1853 (Anthropic run) | Harness differs |
| FrontierCode v1.1 Main | 53.3% | – | OpenAI |
| FrontierCode v1.1 Extended | 64.5% | – | OpenAI |
| BrowseComp (web research) | 91.5% | – | OpenAI |
| Agents’ Last Exam | 59.3% | – | OpenAI |
| ScreenSpot-Pro, no tools (UI grounding) | 92.7% | – | OpenAI |
| SRE-Bench public | 88% pass@1 | – | OpenAI |
| ExploitBench (cybersecurity) | 100% | – | OpenAI |
| ExploitGym | 42.4% | – | OpenAI |
| SEC-Bench Pro | 85.4% | – | OpenAI |
| GeneBench Pro | 37.8% | – | OpenAI |
| LifeSciBench | 60.3% | – | OpenAI |
| Terminal-Bench v2.1 (Artificial Analysis, superseded by v4.0 runs above) | 89.9% high, 88.4% max | 91.4% max | Artificial Analysis |
| SciCode (Artificial Analysis, see v4.3 table above for current 56.5% vs 63.1%) | 54.1% max | 62.0% max | Artificial Analysis (earlier run) |
| MMMU-Pro (Artificial Analysis: 87% Astra, no Fable score published) | 87% max | – | Artificial Analysis (Vals.ai tracked 90.6% Fable in a separate run) |
| Humanity’s Last Exam (Artificial Analysis v4.3: 54.7% vs 59.1%) | 54.7% max | 59.1% max | Artificial Analysis |
| GPQA Diamond (Artificial Analysis v4.3: 96.3% vs 93.7%) | 96.3% max | 93.7% max | Artificial Analysis |
Researchers must also account for harness discrepancies across standardized suites. For example, in ARC-AGI-3 evaluations, provider adapter configurations achieve up to 99.9% effectiveness, whereas standardized evaluations record 62.7%. On OSWorld the vendors ran different subsets (Astra 72.6% offline versus Fable 41.7% strict), and llm-stats tracks a separate OSWorld 2.0 lane where Fable posts 77.9%, proving that benchmark victories often depend heavily on execution harness design. Similarly, GDPval-AA v2 shows 1629 on the Artificial Analysis run for Astra against 1853 on Anthropic’s own run for Fable, so the table keeps both figures with their harnesses labeled. The same snapshot problem is now reshaping tabular AI, where the LimiX-2 vs TabPFN-3.5 leaderboard battle saw both models claim first place on TabArena within days of each other, using different evaluation runs.
Speed, tokens per task, and cost per task
Raw model intelligence is only half the equation when deploying production workloads. Operational velocity and token economy dictate real-world infrastructure expenses. Artificial Analysis live comparisons demonstrate that GPT-6 Astra generates output at 87.5 tokens per second, outpacing Claude Fable 5.1 at 68.7 tokens per second.
Furthermore, Astra completes standardized index tasks in an average of 4.0 minutes, compared to 9.5 minutes for Fable.
Token consumption per task reveals an even starker efficiency gap. GPT-6 Astra utilizes approximately 20,985 total output tokens per task (comprising 8,372 answer tokens and 12,614 reasoning tokens), whereas Claude Fable 5.1 consumes roughly 64,072 output tokens (25,478 answer tokens and 38,594 reasoning tokens). This means Astra achieves top-tier results using roughly one-third of the reasoning overhead. Consequently, the cost per index task sits at $3.26 for Astra versus $7.63 for Fable on standard uncached runs, making Astra cheaper per workflow execution at list prices despite identical $10 and $50 per-token rates. That lead narrows wherever cached context dominates, since Fable holds an advantage with its $0.25 prompt cache read price compared to Astra’s $1.00.
Notes on Epoch.ai and BenchLM
Epoch.ai tracks FrontierMath, including the Tier 4 research-level suite where Astra posts 97.6%. BenchLM.ai maintains a directory of over 400 benchmark definitions, useful for checking harness details behind names like Terminal-Bench 4.0 or OSWorld 2.0. Neither source changes the head to head numbers above, which is why this article keeps the focus on the vendor and Artificial Analysis results.
Which should you pick?
Benchmark names alone do not buy software. What matters is speed, coding ability, and what a finished task actually costs. On those three, the split is clean.
- Speed: Astra generates 87.5 tokens per second against Fable’s 68.7, and finishes index tasks in 4.0 minutes against 9.5. For interactive coding sessions and latency-sensitive agents, Astra is the faster model.
- Coding ability is mixed, so match the sub-skill to the job: Astra leads agentic terminal coding on the newest v4.3 runs (Terminal-Bench v4.0 59.6% versus 55.1%), scientific terminal work (64.6% versus 52.6%), business workflow automation on the AA run (68.5% versus 59.4%), and pull-request quality (DeepSWE 74.1% versus 67.4%, BenchCAD 95.9% versus 84.3%). Fable leads research-grade coding science (SciCode 63.1% versus 56.5%), CursorBench 3.2.0 (73.4%, no Astra score published), and the Coding Agent Index overall (70 versus 67). Short version: Astra for terminal-driven agentic coding, Fable for research-grade coding science.
- Cost per task versus cost per token: Astra is cheaper per finished task on both indexes ($3.26 versus $7.63 intelligence, $4.72 versus $9.18 coding) because it uses roughly one third of the tokens. That advantage shrinks in workloads with heavy context reuse, because Fable’s cache reads cost $0.25 per million against Astra’s $1.00. Long-running agents that re-read large cached contexts narrow or erase Astra’s per-task lead, so check the cache math before assuming cheaper per task means a cheaper bill.
Pick Fable 5.1 for neutral-harness reasoning strength, the Finance and Accounting Index lead, long agentic research, and cached-context-heavy workloads. Pick Astra for faster task completion, terminal-driven coding, computer use, math, and lower cost per finished task where caching is light. That value case now has a direct challenger: our Gemini 4 Argon vs GPT-6 Astra comparison shows Google matching Astra’s headline intelligence score at a fifth of the per-token price.
Frequently Asked Questions
When were GPT-6 Astra and Claude Fable 5.1 released?
Anthropic released Claude Fable 5.1 on September 1, 2026, closely followed by OpenAI launching GPT-6 Astra on September 3, 2026. Both models arrived within 48 hours of each other, initiating a fierce new generation of flagship AI capabilities.
Why do Artificial Analysis and LLM Stats disagree on the #1 spot?
The disagreement stems from weighting methodology. Artificial Analysis weights neutral-harness reasoning where Fable still leads most components even as the headline v4.3 Index ties at 53 apiece, whereas LLM Stats composites focus heavily on pragmatic developer workflows, multi-step tool use, and agentic speed, where Astra dominates.
How do their pricing models and cache read costs compare?
Both models charge $10.00 per million input tokens and $50.00 per million output tokens for standard calls. However, prompt cache read pricing differs significantly: Claude Fable 5.1 charges $0.25 per million tokens, while GPT-6 Astra charges $1.00.
Which model is better for coding and agentic workflows?
For raw multi-step coding execution and task efficiency, GPT-6 Astra uses approximately one-third of the reasoning tokens per task and completes benchmarks faster. For deep academic code reasoning and complex agentic research, Claude Fable 5.1 remains exceptionally competitive.
Bottom Line
Anthropic’s Fable 5.1 is an intelligence powerhouse that ties the v4.3 Index at 53 apiece while taking the Finance and Accounting Index and most neutral-harness reasoning components. OpenAI’s GPT-6 Astra is an efficiency juggernaut that delivers blistering output speeds, advanced computer use, and a token economy that cuts per-task cost roughly in half on uncached runs, with the caveat that heavy cache reuse favors Fable’s cheaper cache reads. By matching your operational priorities to their respective strengths, developers and enterprise leaders can harness the optimal frontier model for any workflow.
