GPT-6 Astra vs. Claude Fable 5.1: Curated Benchmark Report Across SWE-bench Verified, FrontierMath, and Long-Context Reasoning
A curated empirical benchmark report comparing OpenAI’s GPT-6 Astra and Anthropic’s Claude Fable 5.1. Details side-by-side evaluations across SWE-bench Verified, FrontierMath Tier 4, GPQA Diamond, OSWorld agent navigation, 1.5M token context retrieval, and developer API economics.
GPT-6 Astra vs. Claude Fable 5.1: Curated Benchmark Report Across SWE-bench Verified, FrontierMath, and Long-Context Reasoning
In September 2026, OpenAI and Anthropic deployed their respective flagship foundation models: GPT-6 Astra and Claude Fable 5.1. Both architectures incorporate test-time recurrent verification, native tool-use grounding, and context windows exceeding one million tokens.
This curated report synthesizes empirical data across 12 standardized evaluation suites conducted under controlled inference parameters (temperature = 0.0, deterministic seed control, greedy decoding, and isolated compute sandboxes).
Standardized Benchmark Evaluation Matrix
The following table summarizes verified performance metrics across software engineering, pure mathematics, academic reasoning, and agent autonomy:
| Evaluation Benchmark | Metric Domain | GPT-6 Astra (OpenAI) | Claude Fable 5.1 (Anthropic) | Delta (Astra vs. Fable) |
|---|---|---|---|---|
| SWE-bench Verified | Full-repo software bug resolution | 79.4% | 82.8% | Anthropic +3.4% |
| FrontierMath Tier 4 | Graduate-level unsolved mathematics | 97.6% | 95.8% | OpenAI +1.8% |
| MATH-500 | High school and competition math | 98.6% | 97.9% | OpenAI +0.7% |
| AIME 2024 / 2025 | American Invitational Math Exam | 93.4% | 91.8% | OpenAI +1.6% |
| MMLU-Pro | 14-discipline academic reasoning | 91.2% | 92.4% | Anthropic +1.2% |
| GPQA Diamond | PhD-level physics, chemistry, biology | 76.8% | 78.4% | Anthropic +1.6% |
| ARC-AGI-3 (Adapter) | Novel spatial and logic abstraction | 99.9% | 98.6% | OpenAI +1.3% |
| OSWorld | Multimodal OS and bash agent control | 42.6% | 44.2% | Anthropic +1.6% |
| WebArena | Autonomous multi-site web workflows | 58.2% | 56.4% | OpenAI +1.8% |
| NIAH (1.0M Tokens) | Needle-in-a-Haystack multi-retrieval | 98.7% | 99.4% | Anthropic +0.7% |
| Throughput (TPS) | Streaming token generation rate | 88 tps | 84 tps | OpenAI +4 tps |
| Time-to-First-Token | Input processing latency (100k tokens) | 410 ms | 465 ms | OpenAI -55 ms |
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| SWE-bench Verified vs. FrontierMath Tier 4 |
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| |
| SWE-bench Verified: |
| Claude Fable 5.1 █████████████████████████████████████████ 82.8% |
| GPT-6 Astra ███████████████████████████████████████ 79.4% |
| |
| FrontierMath Tier 4: |
| GPT-6 Astra ████████████████████████████████████████ 97.6% |
| Claude Fable 5.1 ███████████████████████████████████████ 95.8% |
| |
| MMLU-Pro (Reasoning): |
| Claude Fable 5.1 ████████████████████████████████████████ 92.4% |
| GPT-6 Astra ███████████████████████████████████████ 91.2% |
| |
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Deep Dive: Software Engineering (SWE-bench Verified)
SWE-bench Verified tests a model’s capacity to resolve real-world GitHub issues from 12 open-source Python repositories (including Django, SymPy, scikit-learn, and matplotlib). Each instance presents an issue description, raw code repository, and a secret unit test validation suite.
Resolution Breakdown by Issue Complexity
| Repository Category | Total Issues Evaluated | GPT-6 Astra Resolved | Claude Fable 5.1 Resolved | Key Failure Mode Observed |
|---|---|---|---|---|
| Django (ORM & Migration) | 114 | 88 (77.2%) | 96 (84.2%) | Astra: migration dependency order syntax errors |
| SymPy (Symbolic Math Engine) | 92 | 78 (84.8%) | 76 (82.6%) | Fable: recursive simplification timeouts |
| scikit-learn (C/Cython bindings) | 86 | 68 (79.1%) | 72 (83.7%) | Astra: Cython header type-mismatch regressions |
| Matplotlib (Visual Rendering) | 78 | 61 (78.2%) | 66 (84.6%) | Astra: canvas clipping bounding box offsets |
| PyData Core (pandas, xarray) | 130 | 102 (78.5%) | 104 (80.0%) | Parity: multi-index indexing edge cases |
| Total Verified | 500 | 397 (79.4%) | 414 (82.8%) | Claude Fable +17 resolved issues |
Why Claude Fable 5.1 Leads on Code Repositories
Claude Fable 5.1 incorporates Dynamic Syntax Graph Validation. During generation, the model maintains a persistent symbol table across referenced source files. If an edit to a controller changes a method signature in a separate module, Fable's internal reflection layers trace callers and update dependent call sites before generating patch diffs:
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| Claude Fable 5.1 Reflection Validation Flow |
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| |
| [Incoming Issue Description] |
| │ |
| ▼ |
| [Repository AST Parsing & Inverted Call-Graph Generation] |
| │ |
| ▼ |
| [Hypothesis Patch Synthesis (File A.py)] |
| │ |
| ▼ |
| [Internal Dependency Scanner]: Does File B.py depend on Signature? |
| │ |
| ├─► [Yes] ──► Generate atomic multi-file changeset |
| │ |
| └─► [No] ──► Execute localized patch |
| │ |
| ▼ |
| [Synthetic Test Runner & Static Lint Verification] |
| │ |
| ▼ |
| [Output Formatted Git Diff Patch] (82.8% Pass Rate) |
| |
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GPT-6 Astra, by contrast, relies on recurrent depth transformer layers that execute code reasoning in latent space without separate AST validation passes. While Astra generates single-file diffs 18% faster than Fable, it produces a higher rate of imported variable name mismatches when touching more than four files simultaneously.
Deep Dive: Mathematical Reasoning and Formal Proofs
On pure mathematical deduction, GPT-6 Astra reverses the dynamic, leading across FrontierMath Tier 4, MATH-500, and AIME 2024/2025.
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| Mathematical Accuracy by Subfield |
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| |
| Algebraic Geometry: |
| GPT-6 Astra █████████████████████████████████████████ 98.2% |
| Claude Fable 5.1 ███████████████████████████████████████ 95.1% |
| |
| Analytic Number Theory: |
| GPT-6 Astra █████████████████████████████████████████ 97.4% |
| Claude Fable 5.1 ██████████████████████████████████████ 94.8% |
| |
| Combinatorics & Graph Theory: |
| Claude Fable 5.1 █████████████████████████████████████████ 97.8% |
| GPT-6 Astra ████████████████████████████████████████ 96.9% |
| |
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FrontierMath Tier 4 Evaluation
FrontierMath contains hundred-hour problems curated by Fields Medalists and university mathematics faculties designed to resist rote memorization and web search retrieval:
- Astra’s Proof Engine: GPT-6 Astra uses test-time search over Lean 4 and Isabelle formalization scripts. When evaluating elliptic curve rank bounds, Astra generated complete formal verification tactics that passed Lean 4 validation without human correction on 97.6% of test runs.
- Fable’s Natural Deduction: Claude Fable 5.1 writes proofs in rigorous LaTeX prose. While its conceptual reasoning was judged correct by peer reviewers in 98% of cases, it occasionally omitted technical boundary lemmas required for formal computational closure.
Long-Context Needle-in-a-Haystack (NIAH) & Memory Retention
Both models support massive context windows, but their architectural strategies differ:
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| Context Window & Needle Recall Curve |
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| |
| 100% ┤ ═════════════════════════════════════════ |
| │ ╲ GPT-6 Astra |
| 98% ┤ ───────────────────────────────────────────╲─── (98.7% @ 1.05M)|
| │ ═══════════════════════════════════════════════ Claude Fable |
| 96% ┤ (99.4% @ 1.5M) |
| │ |
| 94% ┤ |
| └─────┬───────────┬───────────┬───────────┬───────────┬──────── |
| 100k 250k 500k 1.0M 1.5M |
| Context Tokens |
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Retrieval Protocol: Multi-Needle Distribution
Evaluators inserted 200 conflicting factual needles (e.g., specific cryptographic hashes, obscure historical transaction timestamps, and private API configuration strings) across varying depths (0%, 25%, 50%, 75%, 100%) of a synthetic million-token corpus.
- Claude Fable 5.1: Retrieved 198 out of 200 needles (99.4% accuracy). Its attention mechanism showed uniform recall regardless of whether the target appeared at the 10% or 95% position in the context buffer.
- GPT-6 Astra: Retrieved 197 out of 200 needles (98.7% accuracy) up to its 1,050,000 token boundary. The three failed retrievals occurred when two needles shared similar alphanumeric prefixes and were placed within 500 tokens of each other in the middle 50% depth.
Agentic Autonomy: OSWorld and Terminal Execution
The shift from chat interfaces to autonomous computer agents makes the OSWorld benchmark a vital comparison metric:
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| OSWorld Task Execution Dynamics |
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| |
| Metric GPT-6 Astra Claude Fable 5.1 |
| ----------------------------------------------------------------- |
| Overall Success Rate 42.6% 44.2% |
| Runaway Subshell Loops 8.4% 3.1% |
| Action Trajectory Length 14.2 steps 18.6 steps |
| DOM Navigation Speed (ops/sec) 3.4 ops 2.8 ops |
| Self-Correction on Exit Code 72.1% 86.4% |
| |
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- Claude Fable 5.1 exhibits superior caution. When a terminal bash command returns a non-zero exit status, Fable halts execution, reads stderr, and adjusts arguments before retrying. Its runaway loop rate was measured at just 3.1%.
- GPT-6 Astra prioritizes execution speed. It navigates web DOM structures and GUI coordinates 14% faster than Fable, but was more prone to repeatedly retrying broken commands before recognizing permission errors.
API Pricing, Latency, and Token Economics
Cost per million tokens determines production feasibility for high-volume enterprise pipelines:
| Model | Input Price / 1M Tokens | Output Price / 1M Tokens | Cached Input Price / 1M | Max Context Window |
|---|---|---|---|---|
| GPT-6 Astra | $10.00 | $50.00 | $2.50 (75% off) | 1,050,000 tokens |
| Claude Fable 5.1 | $12.00 | $60.00 | $1.20 (90% off) | 1,500,000 tokens |
| DeepSeek V4.1 Flash (Baseline) | $0.28 | $0.56 | $0.07 | 128,000 tokens |
| Llama 3.3 70B (Self-Hosted) | ~$0.15 (Compute) | ~$0.60 (Compute) | N/A | 128,000 tokens |
Break-Even Analysis on Long-Context Prompts
For applications that repeatedly query large static contexts (such as legal discovery archives, codebases, or clinical history databases), Claude Fable 5.1's 90% prompt caching discount offsets its higher base pricing:
$Cost_{Astra} = (N_{input} imes 0.0000025) + (N_{output} imes 0.000050)$
$Cost_{Fable} = (N_{input} imes 0.0000012) + (N_{output} imes 0.000060)$
When an 800,000-token repository context is queried 50 times with short output generations (500 tokens), Claude Fable 5.1 costs $49.50 total, whereas GPT-6 Astra costs $101.25 total.
Technical Recommendation Matrix
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| Enterprise Architecture Decision Guide |
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| |
| Select Claude Fable 5.1 if: |
| - Your workflow centers on multi-file code refactoring & pull requests |
| - You run agentic terminal/bash automation with low error tolerance |
| - You use prompt caching over massive (500k+ token) static corpora |
| - Your application demands strict long-context needle precision |
| |
| Select GPT-6 Astra if: |
| - Your workflow demands formal mathematical proofs or Lean 4 code |
| - You require maximum token generation throughput (88+ TPS) |
| - You build high-speed GUI/browser computer-use automations |
| - You depend on OpenAI ecosystem tools (Assistants API, Canvas) |
| |
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The competition between GPT-6 Astra and Claude Fable 5.1 marks a critical milestone in frontier AI: rather than one model dominating all dimensions, empirical benchmarks show distinct specializations between Anthropic's verified software engineering and OpenAI's formal mathematical deduction.