Model comparison
Claude Fable 5.1 vs GPT-6 Astra
Anthropic against OpenAI, compared on context, price, and attributed benchmark results.
Recorded profile verification October 1, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below
Anthropic
Claude Fable 5.1
Frontier
OpenAI
GPT-6 Astra
Frontier
| Specification | Claude Fable 5.1 | GPT-6 Astra |
|---|---|---|
| Provider | ||
| Provider | Anthropic | OpenAI |
| Tier | ||
| Tier | Frontier | Frontier |
| Context window | ||
| Context window | 1M | Winner: 1.05M |
| Max output | ||
| Max output | 128K | 128K |
| Input / 1M tokens | ||
| Input / 1M tokens | $10 | $10 |
| Output / 1M tokens | ||
| Output / 1M tokens | $50 | $50 |
| Weights | ||
| Weights | Closed | Closed |
| Parameters | ||
| Parameters | Not verifiedUnverified | Not verifiedUnverified |
| Reasoning levels | ||
| Reasoning levels | low, medium, high, xhigh, max | low, medium, high, xhigh, max |
| Modalities | ||
| Modalities | text, image | text, image |
| API model id | ||
| API model id | claude-fable-5-1 | gpt-6-astra |
| Released | ||
| Released | September 1, 2026 | September 3, 2026 |
| Artificial Analysis Intelligence Index [max] · source date Claude Fable 5.1: 2026-10-01 | ||
| Artificial Analysis Intelligence Index [max] · source date Claude Fable 5.1: 2026-10-01 | 53 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index [max] · source date GPT-6 Astra: 2026-10-01 | ||
| Artificial Analysis Intelligence Index [max] · source date GPT-6 Astra: 2026-10-01 | Not verifiedUnverified | 53 |
| Humanity's Last Exam · source date Claude Fable 5.1: 2026-09-01, GPT-6 Astra: 2026-09-03 (comparability not verified) | ||
| Humanity's Last Exam · source date Claude Fable 5.1: 2026-09-01, GPT-6 Astra: 2026-09-03 (comparability not verified) | 65 | 57.2 |
| Terminal-Bench 2.1 · source date Claude Fable 5.1: 2026-09-26 | ||
| Terminal-Bench 2.1 · source date Claude Fable 5.1: 2026-09-26 | 91.4 | Not verifiedUnverified |
| DeepSWE 1.1 · source date Claude Fable 5.1: 2026-09-03 | ||
| DeepSWE 1.1 · source date Claude Fable 5.1: 2026-09-03 | 67.4 | Not verifiedUnverified |
| DeepSWE 1.1 · source date GPT-6 Astra: 2026-09-03 | ||
| DeepSWE 1.1 · source date GPT-6 Astra: 2026-09-03 | Not verifiedUnverified | 74.1 |
| OSWorld 2.0 (offline) · source date GPT-6 Astra: 2026-09-03 | ||
| OSWorld 2.0 (offline) · source date GPT-6 Astra: 2026-09-03 | Not verifiedUnverified | 72.6 |
| GPQA Diamond · source date GPT-6 Astra: 2026-09-03 | ||
| GPQA Diamond · source date GPT-6 Astra: 2026-09-03 | Not verifiedUnverified | 96 |
Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
Where each score comes from, and how far it can be compared across models.
- Source date2026-10-01Artificial Analysis (GPT-6 Astra (max))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
- Source date2026-10-01Artificial Analysis (Claude Fable 5.1 (max with fallback))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
- Source date2026-09-26Artificial Analysis Terminal-Bench 2.1 (independent, Terminus 2, max effort)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.
- Source date2026-09-03OpenAI GPT-6 Astra evaluation table (vendor-reported, with tools; maximum across evaluated efforts)
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
- Source date2026-09-03OpenAI GPT-6 Astra evaluation table (vendor-reported; maximum across evaluated efforts)
Graduate-level science reasoning: multiple-choice questions written by domain PhDs.
Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.
- Source date2026-09-03OpenAI (vendor-reported) (v2026.08.08 offline set, partial reward; vendor research/API evaluation, maximum across evaluated efforts)
Computer-use: completing real OS tasks (files, apps, settings) in a sandboxed desktop.
Comparable with caveatOffline vs live interaction modes are NOT comparable; step budgets and screen resolution must match.
- Source date2026-09-03OpenAI (xhigh, mini-swe-agent; matches Datacurve's independent board (74.1% at $4.43/task, rank 1; board updated 2026-09-22))
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
- Source date2026-09-03OpenAI GPT-6 Astra launch table quoting a cross-provider result; not a direct Datacurve row (Cross-provider quotation; Fable harness, effort and experiment date unverified)
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
- Source date2026-09-01Anthropic Fable 5.1 announcement (vendor, with tools; 60.9 no tools) — Anthropic's later Opus 5.5 table lists 65.6; conflict preserved
Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.
Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.
Pricing tiers
Claude Fable 5.1: Standard $10/$50 per MTok with no long-context surcharge. Cache reads $0.25, versus Fable 5's $1; 5-minute writes $12.50 and 1-hour writes $20. Batch $5/$25. US-only inference adds 10% where supported. Adaptive thinking is always on, default effort high; knowledge cutoff Jun 2026. Retirement not sooner than September 1, 2027.
GPT-6 Astra: Standard $10/$50 per MTok (≤272K input); above 272K the full request bills $20/$75. Cached input $1/$2 and cache writes $12.50/$25 for short/long context. Batch/Flex are half Standard; Fast is 2x. Ultrafast is 6x: $60/$300 short, $120/$450 long, through Responses with global/US processing; no EU or other regional inference residency. Regional/FedRAMP processing adds 10% where eligible. Knowledge cutoff Apr 30 2026. Enterprise model access is disabled by default.
Claude Fable 5.1
Claude Fable 5.1 is Anthropic's model for the most demanding reasoning and long-horizon agentic work at $10/$50 — Anthropic now recommends Opus 5.5 as the default for most workloads.
Best for
- Frontier agents
- Hardest reasoning work
- Fable 5 workloads at the same price
Watch out
Standard token rates are 2.5x Opus 5.5's. Forced tool use errors; thinking blocks are model/conversation-bound, so editing earlier turns or switching models can invalidate them. Display updates and per-message effort are beta. Match benchmark settings and evaluate pinned workloads before migrating.
GPT-6 Astra
OpenAI describes GPT-6 Astra as its most capable model for complex reasoning, coding and computer use, and the first it gates at the Preparedness Framework's 'Critical' cybersecurity threshold.
Best for
- Computer-use agents
- Agentic coding
- Hard reasoning
Watch out
OpenAI places Astra at its Critical cybersecurity threshold and disables it by default for Enterprise. Tool calling requires Responses; none reasoning and custom temperature/top_p/logprobs are unsupported. Long prompts above 272K increase the full-request rate. GPT-6.1 Sol has lower base token prices; compare results on your own workload.
Benchmark
DeepSWE 1.1 in context
Only GPT-6 Astra has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.
Local leader
GPT-6 Astra [xhigh]
74.1%
Rows shown
28
Best source-matched Pass@1 per model
Snapshot date
2026-10-01
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost, output tokens and steps. Legacy v1 transcriptions are excluded here until an exact primary receipt is retained.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra [xhigh] | 74.1% | $4.43 | 30k | 29 |
| 2 | Gemini 3.8 Flash [high] | 73.8% | $2.36 | 143k | 166 |
| 3 | Claude Opus 5 [max] | 73.6% | $11.84 | 118k | 99 |
| 4 | GPT-5.6 Sol [max] | 72.7% | $8.39 | 60k | 61 |
| 5 | Claude Fable 5 [xhigh] | 69.9% | $13.41 | 80k | 68 |
| 6 | GPT-5.6 Terra [max] | 69.6% | $4.95 | 72k | 76 |
| 7 | GLM 5.3 [max] | 69.0% | $3.99 | 80k | 124 |
| 8 | Kimi K3 [max] | 68.5% | $4.65 | 82k | 98 |
| 9 | Grok 4.6 [medium] | 67.5% | $3.45 | 50k | 70 |
| 10 | GPT-5.6 Luna [max] | 67.2% | $3.03 | 73k | 102 |
| 11 | GPT-5.5 [xhigh] | 67.0% | $7.23 | 46k | 82 |
| 12 | Gemini 3.7 Flash [medium] | 65.5% | $2.03 | 94k | 117 |
| 13 | GLM 5.3 Flash [max] | 63.4% | $0.48 | 73k | 123 |
| 14 | DeepSeek V4-Pro [max] | 62.8% | $0.24 | 106k | 155 |
| 15 | Claude Opus 4.8 [max] | 59.0% | $13.22 | 135k | 120 |
| 16 | Qwen3.8-Max [xhigh] | 57.5% | $3.73 | 95k | 111 |
| 17 | Muse Spark 1.2 [xhigh] | 54.9% | $3.70 | 99k | 101 |
| 18 | Claude Sonnet 5 [max] | 53.8% | $26.40 | 214k | 268 |
| 19 | Grok 4.5 [high] | 53.8% | $2.42 | 36k | 61 |
| 20 | DeepSeek V4-Flash [max] | 53.3% | $0.10 | 108k | 153 |
| 21 | Muse Spark 1.1 [xhigh] | 53.3% | $2.36 | 74k | 96 |
| 22 | GPT-5.4 [xhigh] | 51.8% | $5.65 | 71k | 70 |
| 23 | Gemini 3.6 Flash [high] | 46.7% | $4.42 | 96k | 117 |
| 24 | GLM 5.2 [max] | 43.8% | $3.92 | 78k | 129 |
| 25 | Gemini 3.5 Flash [high] | 36.1% | $3.45 | 76k | 105 |
| 26 | Kimi K2.7 Code | 30.5% | $2.82 | 59k | 149 |
| 27 | Claude Sonnet 4.6 [high] | 29.9% | $5.52 | 76k | 134 |
| 28 | Gemini 3.1 Pro [high] | 11.7% | $2.14 | 28k | 76 |
“Best per model” shows each model’s highest source-matched Pass@1 in this capture; switch to all effort levels to see every retained configuration. Efforts differ across models, so this view is not a comparison at matched effort. The source was generated September 22 and captured October 1. Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
When the cheaper one wins
Both charge $50 per million output tokens at standard rates, so output price is not the split. Prefer the row whose “best for” line matches the job, then confirm the live vendor rate.
Run the model pickerEvidence confidence: Medium
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 4/5 core specs verified on both sides — Not published for at least one side: parameter count.
- No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
- Verified within the last 90 days — Older of the two catalog checks was 25 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- Claude Fable 5.1: Claude model overview — whole-profile source check October 1 (accessed 2026-10-01)
- Claude Fable 5.1: Claude current API pricing, caching and inference geography (accessed 2026-10-01)
- Claude Fable 5.1: Anthropic pricing — Fable 5.1/Mythos 5.1 $10/$50 (accessed 2026-09-03)
- Claude Fable 5.1: Anthropic models overview — Fable 5.1 (1M context, 128K output) (accessed 2026-09-03)
- Claude Fable 5.1: Anthropic — Claude Fable and Mythos 5.1 announcement (accessed 2026-09-26)
- Claude Fable 5.1: Anthropic models overview — Opus 5.5 default; Fable 5.1 for demanding reasoning (accessed 2026-09-26)
- Claude Fable 5.1: Anthropic effort documentation (Fable 5.1 selectable effort) (accessed 2026-10-01)
- GPT-6 Astra: OpenAI model card — whole-profile source check October 1 (accessed 2026-10-01)
- GPT-6 Astra: OpenAI current API pricing and processing tiers (accessed 2026-10-01)
- GPT-6 Astra: OpenAI API changelog — September 3 release; checked October 1 (accessed 2026-10-01)
- GPT-6 Astra: OpenAI — Introducing GPT-6 Astra (accessed 2026-09-03)
- GPT-6 Astra: OpenAI API pricing (gpt-6-astra $10/$50) (accessed 2026-09-03)
- GPT-6 Astra: CNBC — OpenAI launches GPT-6 Astra (accessed 2026-09-03)
- GPT-6 Astra: OpenAI model docs — gpt-6-astra (128K max output, effort levels) (accessed 2026-09-05)
- GPT-6 Astra: OpenAI API pricing (gpt-6-astra ≤272K $10/$50, >272K $20/$75) (accessed 2026-09-26)
Related comparisons
- Claude Fable 5.1 vs Claude Haiku 4.5
- Claude Fable 5.1 vs Claude Mythos 5.1
- Claude Fable 5.1 vs Claude Opus 5
- Claude Fable 5.1 vs Claude Opus 5.5
- Claude Fable 5.1 vs Claude Sonnet 5
- Claude Fable 5.1 vs Claude Sonnet 5.5
Diving deeper on one model? Claude Fable 5.1 · GPT-6 Astra
Common questions
Claude Fable 5.1 vs GPT-6 Astra
Answered from the verified figures on this page rather than general guidance.
Is Claude Fable 5.1 or GPT-6 Astra cheaper for input?
Is Claude Fable 5.1 or GPT-6 Astra cheaper for output?
Which has the larger context window, Claude Fable 5.1 or GPT-6 Astra?
Do Claude Fable 5.1 and GPT-6 Astra support the same reasoning levels?
Should I use Claude Fable 5.1 or GPT-6 Astra?
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Context: GPT-6 Astra takes 1.05M against 1M for Claude Fable 5.1 — only decisive if your prompts approach the smaller window.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.