Claude Fable 5.1 vs GPT-6 Astra — Comparison

Sources: Artificial Analysis (incl. Intelligence Index v4.3), DataCamp, BenchLM, OpenRouter, Roboflow Vision Evals, Vals AI, MindStudio, LLMLearner, Algoramming, CodingFleet, the-decoder, AA v4.3 announcement (LinkedIn) — fetched 2026-09-07, updated 2026-09-08

1. PRICING

Workload cost (rate-card arithmetic)

Measured cost per task (Artificial Analysis)

v4.3 update (2026-09-07): AA re-anchored the index. Fable 5.1 and Astra are now tied at 53. Cost per task: Astra $3.26 vs Fable 5.1 $7.63. See §7.5.

2. BENCHMARKS

Artificial Analysis (independent)

BenchLM public scores

Head-to-head benchmarks

3. VERDICT

Choose GPT-6 Astra for: computer use, professional artifacts, math/science reasoning, cybersecurity defense, lower cost per task, Codex harness.

Choose Claude Fable 5.1 for: reasoning depth (HLE), cache-heavy agent loops, very large requests (no surcharge), Claude Code, highest independent reasoning score.

4. ACCESS

• GPT-6 Astra: ChatGPT (Plus/Pro/Business/Enterprise), OpenAI API, Codex, Azure/Foundry, AWS Bedrock, OpenRouter. Enterprise off by default.
• Claude Fable 5.1: Claude web/mobile/desktop, Claude API, Claude Code, AWS Bedrock, Google Cloud, Azure/Foundry, OpenRouter. Requires 30-day data retention.

5. REAL AGENT WORK (Reddit r/better_claw, 2026-09-04)

Source: Reddit r/better_claw Day-one numbers, one session, serving infra hours old. Also compares Claude Sonnet 5 ($3/$15).

5.1 Pricing context for agent workloads

Agent workloads are 80-95% cached context (same SOUL.md, schemas, history prefix every call) → effective input cost is the cache line, not the sticker.

5.2 Five real tests

Test 1 — Tool calling under repetition (50 identical JSON-schema calls)

• Astra: 48/50. Two valid JSON wrapped in a reasoning preamble that broke the parser — thinking leaking into structured output. Day-one rough edge.
• Fable 5.1: 50/50, clean every time.
• Sonnet 5: 49/50 (one dropped field).

Test 2 — The "done" lie (6-step chain, step 4 guaranteed to fail)

• Astra: caught failure, then proposed an alternative unprompted, attempted it, partially succeeded via a different data source. Most capable, but concerning autonomy for unsupervised runs.
• Fable 5.1: caught failure, reported it, proposed alternative, waited for approval. Approval gate held.
• Sonnet 5: caught failure, reported it, stopped. Safest for overnight runs.

Test 3 — Instruction survival past message 25 (tie across all three; nobody solves instruction decay)

Test 4 — Context honesty (~200K tokens, ask about something not in them)

• All three clean. Astra's 1.05M context is real: MRCR 96.3% at 512K-1M vs Sol's 73.8%. Long-context retrieval is a genuine Astra strength.

Test 5 — Cost per real task (search, fetch 3 sources, synthesize, write summary)

• Astra: ~$1.10 — verbose, reasoning tokens billed as output at $50/MTok (thinking tax is real)
• Fable 5.1: ~$0.65 — less verbose, cache reads 4x cheaper
• Sonnet 5: ~$0.28 — a third of Astra's cost

5.3 Where Astra earns it

• OSWorld 2.0 at 47% less time per task (40 min vs 75 for Sol) — desktop automation speed compounds across a workday.
• ExploitBench 100% — why it crossed the "Critical" cyber threshold.
• SRE-Bench pass@1 88% vs Sol's 55.9% — site-reliability/ops tasks.

5.4 Where it doesn't

Morning briefings, email triage, classification, drafting, research summaries, simple tool calling — everything a personal agent does 50x a day. On these, all three produce output indistinguishable in a blind read, and Sonnet does it at a quarter of the price.

5.5 Author's routing decision

• Sonnet 5 stays daily driver ($3/$15, fastest, cheapest, good enough).
• Fable 5.1 stays escalation model (same sticker as Astra but 4x cheaper on cache reads = most of an agent's input bill).
• Astra goes into "watch" slot — test for a week on computer-use and complex multi-step tasks. If OSWorld lead translates to real-world agent reliability, earns a routing slot; otherwise Fable does the same job cheaper.
• Not switching default. Not today.

5.6 Community signal

• Cache line is the real bill, not the sticker (agent workloads 80-95% cache-hit → effective input ~$1 Astra / ~$0.25 Fable / ~$0.15 Sonnet).
• Astra's "fail and pivot unprompted" reads impressive in a benchmark but as a supply-chain incident waiting to happen in production.
• Astra's reasoning-bleed into structured output is a repeatable failure mode: wrapped-but-valid JSON fails loud in a parser, silent in a retry loop that re-bills you.

6. ADDITIONAL BENCHMARK SOURCES

6.1 [LLMLearner](https://llmlearner.com/compare/claude-fable-5-1-vs-gpt-6-astra)

Across 8 shared benchmarks, GPT-6 Astra leads on 5, Fable 5.1 on 3.

Domain means (0-100 scale, best published result each):

Comparable shared benchmarks:

Blended price at standard mix: both ~$20.00/1M (cache-read gap excluded from blend).

6.2 [Algoramming — 3-way with Qwen 3.8 Max](https://www.algoramming.com/blogs/qwen-3-8-max-vs-claude-fable-5-1-vs-gpt-6-astra)

Key caveat: Astra's ARC-AGI-3 score depends entirely on OpenAI's stateful Provider Adapter harness (99.9% with, 62.7% without) — the harness preserves reasoning state between API requests. Without it, the stateless API score drops sharply.

6.3 Data inconsistencies across sources (flag, don't resolve)

• OSWorld: BenchLM says Fable 5.1 = 41.7%; Algoramming says Fable 5.1 = 85.0% on OSWorld-Verified. Different task releases/grading setups.
• DeepSWE: DataCamp says Fable 5.1 = 67.4%; Algoramming says 80.0%. Possibly Fable 5 vs 5.1 mix.
• Astra cache read: $1.00 (DataCamp, BenchLM, AA) vs $1.50 (Algoramming).
• AA Intelligence Index v4.2 (unverified, scraped source): reportedly Fable 5.1 = 57 vs Astra = 55 after doubling anti-gaming test sets. Not independently confirmed — skip.

7. VISION, VALS, AND SPECIALIZED BENCHMARKS

7.1 [Roboflow Vision Evals](https://playground.roboflow.com/models/compare/claude-fable-5-1-vs-gpt-6-astra) (updated 2026-09-04)

6 vision tasks, pooled at low effort. Astra wins overall but the two split the tasks 3-3.

By task (low / high effort):

Astra is both cheaper and faster per sample on the vision task mix.

7.2 [Vals AI](https://www.vals.ai/models/openai_gpt-6-astra)

• Overall accuracy: 66.61% (±1.09)
• Vals Index: rank 3/54
• Cost / test: $19.09
• Latency: 25 min 11 s
• Notable ranks: Code Migration #1/56, EMB #6/54, ProofBench v1.1 #2/29, BioMysteryBench #1/10, Terminal-Bench 2.1 #1/62, Vibe Code Bench v1.1 #3/92, ProgramBench #2/45
• Individual benchmark scores not reliably captured (JS-rendered) — treat Vals Index rank as the usable signal.

7.3 [MindStudio analysis](https://www.mindstudio.ai/blog/gpt-6-astra-benchmarks-analysis)

Key independent analysis points:

ARC-AGI-3 harness dependency (confirmed):

• Standard provider-neutral harness: 62.7% (~$26,000 compute)
• OpenAI provider adapter harness (preserves reasoning state between actions): 99.9% (~$18,800)
• OpenAI's launch chart pits the 99.9% adapter result against older models tested under different, less favorable conditions. ARC Prize team cautions that saturating the benchmark is not proof of general intelligence.

Coding is a tie, not a takeover:

• Terminal-Bench 4.0: Astra 57.9% vs Sol 37.3% vs Fable 5.1 55.8% — clear Astra win
• DeepSWE: Astra 74.1% vs Sol 72.7% vs Opus 5 73.7% vs a Gemini Flash 73.8% — ~1.4 pt lead over own predecessor
• FrontierCode: Astra and Fable 5 within a fraction of a point
• Coding Agent Index: Astra 67 vs Fable 5.1 70

No broad intelligence jump:

• Intelligence Index: Astra 61 = GPT-5.6 Sol, behind Fable 5.1 (66) and Muse Spark 1.3 (v4.1.1; v4.3 re-anchored — see §7.5)
• Cost per task ~75% higher than Sol at max effort despite token efficiency
• Regressions: GDPval, banking tool-use eval, long-context reasoning, presentation quality (AA-Briefcase)
• Genuine small gains: BrowseComp 90.4→91.5, LifeSciBench 59.9→64.3, MedChemBench 47.4→49.3, GPQA Diamond 94.6→96.0

FrontierMath caveat: 97.6% on Tier 4, but Epoch AI's harder 68-problem Erdős benchmark — Astra solved only 2 of 68 officially (rising to 5 with retries costing >$220,000 compute).

Cybersecurity jump: 100% ExploitBench, 42.4% ExploitGym, 88% SRE pass@1. First model to cross OpenAI's "Critical" threshold. Tradeoff: internal reasoning harder to monitor; Apollo Research flagged awareness-of-evaluation concerns.

7.4 Artificial Analysis Intelligence Index v4.2 (SUPERSEDED by v4.3 in §7.5)

(the-decoder / AA v4.2 coverage, 2026-09-05)

Superseded. v4.3 (§7.5) re-anchored the scale and now has Fable 5.1 and Astra tied at 53. The v4.2 figures below (Fable 5.1 57, Astra 55) are kept for historical context only. AA's live models page still shows these stale v4.2 numbers. Confirmed: Fable 5.1 tops, Astra 2nd (+4 pts on Sol).

v4.2 changes:

• AA-Briefcase added (private held-out test set, multi-week agentic knowledge work, thousands of source files)
• GDP.pdf added (Surge AI, single-turn professional document reasoning across 100 PDFs / 4,592 pages, 1,275 expert atomic criteria, strict all-pass scoring)
• GPQA Diamond dropped (now saturated)
• Private held-out test sets now 40% of Index weighting (was 20% in v4.1)

Where each shines:

Ranking: Fable 5.1 > Astra > Muse Spark 1.3 (Meta) > SpaceXAI > Kimi (Moonshot) > Z.AI > Google.

7.5 Artificial Analysis Intelligence Index v4.3 (CONFIRMED, 2026-09-07)

Source: AA official announcement (LinkedIn)

Major shift: Fable 5.1 and GPT-6 Astra are now TIED at 53.

v4.3 changes (from v4.2):

• Terminal-Bench upgraded v2.1 → v4.0 (66 multi-step tasks, mini-SWE-agent harness)
• Replaced τ-Banking with AutomationBench-AA (Zapier, 657 business workflows, private test set)
• Private test set weighting: 40% → 45%
• Category weights unchanged: Agents 30%, Coding 20%, General 30%, Scientific 20%

v4.3 sub-benchmarks:

Open weights: GLM-5.3 & Kimi K3 lead at 44 (9 pts behind the frontier pair); GLM-5.3 Flash 42, Qwen3.8 2.4T A95B 40, DeepSeek V4 Pro 36.

Cost Pareto frontier (v4.3): OpenAI occupies the majority — all five Astra effort levels offer the lowest cost per task at their intelligence level. Fable 5.1 (xhigh, max), GLM-5.3-Flash (42), and MiMo-V2.5-Pro (26) round out the frontier.

Note: AA's live models page still displays v4.2 figures (Fable 5.1 57, Astra 55) — that is cached/stale. The LinkedIn announcement is the authoritative v4.3 source.

8. 3D / CAD / MCP & REAL-WORLD TESTS

8.1 KingBench 3 — 8 app-building tests (CodingFleet, 2026-09-06)

• Astra won: folding table, panda SVG, 3D wristwatch
• Fable 5.1 won: elevator simulation, contact lens case, archery game
• Tied on the other two; Fable clearly outperformed on an Obsidian clone with working image generation
• Front-end design and code-review quality consistently favor Fable 5.1 (Theo/t3.gg, MindStudio, KingBench)

8.2 3D / CAD / MCP — Astra's launch demos

CodingFleet: Astra's launch demos operated KiCad, Unity, FreeCAD, and Blender directly. MindStudio: Astra connected to Blender via MCP, modeled an asset from scratch, exported it, imported into Unreal Engine 5 — handling the entire pipeline autonomously, verifying topology at each step.

Real user test (r/OpenAI, 2026-09-05): connected Astra to Blender via MCP, gave a prompt for a game asset, it created it from scratch. Astra's output notably better than Fable 5.1 on inanimate objects; humanoid/sculpted mesh struggled ("nowhere near the quality of the car"). Requires human final touchup on organic models.

• Cost: "insane" — one run took 30-45 min and used the user's entire Plus-plan usage reset
• Community speculation: Astra likely trained in a Blender verifiable-reward environment (reward = match Blender render embeddings to generated embeddings); resulting meshes probably clean but expensive to produce
• Widespread critique: detail errors (A-pillars, wipers, trim, handles, mirrors, windows) — "AI still does not understand the underlying concept and does not design from the ground up"

8.3 Anthropic's qualitative science claims (Fable 5.1 / Mythos 5.1)

• Designed protein binders with ~50% hit rate (vs 10-15% norm)
• Trained a neural network that produced a 2-3 km resolution elevation map of a third of Venus
• Wrote GPU kernels that sped up seven genomics models up to 2.5x

8.4 Additional independent tests (CodingFleet)

8.5 Sources

• CodingFleet
• Reddit r/OpenAI — 3D Blender

← Back to home