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BenchLM

Claude Fable 5 vs GPT-5.6 Sol

Updated September 24, 2026. Rank says Claude Fable 5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Claude Fable 5 has the higher public score estimate, 79.07 versus 78.49, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 14 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Anthropic logo

Anthropic

79.07/100

Supported · Public rank #6

90% interval 76.0–82.1

Model B
OpenAI logo

OpenAI

78.49/100

Supported · Public rank #7

90% interval 75.6–81.4

Shared results
14
Claude Fable 5 only
7
GPT-5.6 Sol only
23
Like-for-like categories
3 / 8
Supported: Claude Fable 5 and GPT-5.6 SolHow the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Claude Fable 5

    Claude Fable 5 leads on the public coding lane, 74.5 to 71.6, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited
  • Agentic work

    Tool use, computer use, and multi-step task completion

    Claude Fable 5

    Claude Fable 5 leads on the public agentic lane, 73.7 to 69.6, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    GPT-5.6 Sol

    GPT-5.6 Sol has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls
  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-5.6 Sol

    GPT-5.6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    GPT-5.6 Sol

    GPT-5.6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Repository review cost

    50K fresh input + 3K output tokens

    GPT-5.6 Sol

    GPT-5.6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

74.5Claude Fable 571.6GPT-5.6 Sol

Like-for-like · BenchAlign v5.7

Claude Fable 5 leads the like-for-like coding row, although the 90% intervals overlap.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Like-for-like
Claude Fable 5
73.7
Supported · #4/105
GPT-5.6 Sol
69.6
Supported · #7/105
Basis
BenchAlign v5.7 lane · 5 vs 9 public rows
Reading
Claude Fable 5 leads · intervals overlap

Coding

Like-for-like
Claude Fable 5
74.5
Supported · #4/135
GPT-5.6 Sol
71.6
Supported · #6/135
Basis
BenchAlign v5.7 lane · 10 vs 12 public rows
Reading
Claude Fable 5 leads · intervals overlap

Knowledge

Like-for-like
Claude Fable 5
81.8
Supported · #4/158
GPT-5.6 Sol
78.8
Supported · #7/158
Basis
BenchAlign v5.7 lane · 2 vs 8 public rows
Reading
Claude Fable 5 leads · intervals overlap

Instruction following

Directional only
Claude Fable 5
75.7
#58/124
GPT-5.6 Sol
87.7
#28/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
Claude Fable 5
80.6
Unranked · 4 rankable rows
GPT-5.6 Sol
72.1
#8/19
Basis
Provisional lane · 1 vs 2 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5
62.6
Unranked · 2 rankable rows
GPT-5.6 Sol
87.6
#5/50
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5
Not ranked
GPT-5.6 Sol
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5
Not ranked
GPT-5.6 Sol
96.8
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Claude Fable 5
$0.035
Fits in one request
GPT-5.6 Sol
$0.014
Fits in one request

GPT-5.6 Sol has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Fable 5
$0.65
Fits in one request
GPT-5.6 Sol
$0.26
Fits in one request

GPT-5.6 Sol has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Fable 5
$0.9
Fits in one request
GPT-5.6 Sol
$0.36
Fits in one request

GPT-5.6 Sol has the lower modeled cost

Costs use the listed standard API rates.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Fable 5

$1 per 1M cached input tokens

Claude API pricing

GPT-5.6 Sol

$0.4 per 1M cached input tokens

OpenAI pricing

Reasoning profile

Claude Fable 5

Reasoning

GPT-5.6 Sol

Reasoning

Weight access

Claude Fable 5

Proprietary

GPT-5.6 Sol

Proprietary

License

Claude Fable 5

Proprietary

GPT-5.6 Sol

Proprietary

Release date

Claude Fable 5

2026-06-09

GPT-5.6 Sol

2026-07-09

If you already use one of these models

Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
Claude Fable 5 has the higher public score estimate, 79.07 versus 78.49, but the 90% score intervals overlap.
Workload cost
Repository review: $0.65 vs $0.26. Cache-heavy agent loop: $0.9 vs $0.36.
Context tradeoff
GPT-5.6 Sol has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Fable 5 or GPT-5.6 Sol?

Claude Fable 5 has the higher public score estimate, 79.07 versus 78.49, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Fable 5 or GPT-5.6 Sol?

Claude Fable 5 leads the public coding lane, 74.5 to 71.6, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Claude Fable 5 or GPT-5.6 Sol?

Claude Fable 5 leads the public agentic tasks lane, 73.7 to 69.6, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Claude Fable 5 or GPT-5.6 Sol?

For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.014 on GPT-5.6 Sol; repository review costs $0.65 and $0.26; the cache-heavy agent loop costs $0.9 and $0.36. Costs use the listed standard API rates.

Which has the larger context window, Claude Fable 5 or GPT-5.6 Sol?

GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 1M.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence44 rows

Agentic

  • Terminal-Bench 3.0

    Shared source
    Claude Fable 534.0%
    GPT-5.6 Sol34.6%

    GPT-5.6 Sol leads this result

  • Terminal-Bench 2.1

    Claude Fable 584.3%
    Source
    GPT-5.6 Sol91.9%
    Source

    GPT-5.6 Sol leads this result

  • OSWorld-Verified

    Claude Fable 585%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Fable 580.5%
    Source
    GPT-5.6 Sol85.8%
    Source

    GPT-5.6 Sol leads this result

  • ApprenticeBench

    Shared source
    Claude Fable 534%
    GPT-5.6 Sol26%

    Claude Fable 5 leads this result

  • BrowseComp

    Claude Fable 5—
    GPT-5.6 Sol92.2%
    Source

    Not directly comparable

  • OSWorld 2.0

    Claude Fable 5—
    GPT-5.6 Sol62.6%
    Source

    Not directly comparable

  • CyberGym

    Claude Fable 5—
    GPT-5.6 Sol84.5%
    Source

    Not directly comparable

  • ExploitGym

    Claude Fable 5—
    GPT-5.6 Sol33.7%
    Source

    Not directly comparable

  • Toolathlon

    Claude Fable 5—
    GPT-5.6 Sol58%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Fable 595%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • SWE-bench Pro

    Claude Fable 580%
    Source
    GPT-5.6 Sol64.6%
    Source

    Claude Fable 5 leads this result

  • FrontierSWE v2

    Shared source
    Claude Fable 547.0%
    GPT-5.6 Sol32.2%

    Claude Fable 5 leads this result

  • FrontierCode 1.1 Main

    Claude Fable 553.5%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Fable 584.3%
    Source
    GPT-5.6 Sol91.9%
    Source

    GPT-5.6 Sol leads this result

  • cursorBench31

    Claude Fable 570.6%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • cursorBench32

    Shared source
    Claude Fable 570.5%
    GPT-5.6 Sol67.2%

    Claude Fable 5 leads this result

  • VulcanBench v3

    Shared source
    Claude Fable 589.5%
    GPT-5.6 Sol87.0%

    Claude Fable 5 leads this result

  • LiveCodeBench (Vals)

    Claude Fable 589.8%
    Source
    GPT-5.6 Sol82.6%
    Source

    Claude Fable 5 leads this result

  • SWE-bench (Vals)

    Claude Fable 595.0%
    Source
    GPT-5.6 Sol96.2%
    Source

    GPT-5.6 Sol leads this result

  • Bug Hunt Bench

    Claude Fable 5—
    GPT-5.6 Sol42 fixes
    Source

    Not directly comparable

  • DeepSWE

    Claude Fable 5—
    GPT-5.6 Sol72.7%
    Source

    Not directly comparable

  • FrontierCode 1.1 Extended

    Claude Fable 5—
    GPT-5.6 Sol60.6%
    Source

    Not directly comparable

  • VulcanBench CII v1

    Claude Fable 5—
    GPT-5.6 Sol86.5%
    Source

    Not directly comparable

  • cursorBench40

    Claude Fable 5—
    GPT-5.6 Sol41.7%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-1

    Claude Fable 598.50%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • ARC-AGI-2

    Claude Fable 589.2%
    Source
    GPT-5.6 Sol92.5%
    Source

    GPT-5.6 Sol leads this result

  • ARC-AGI-3

    Claude Fable 5—
    GPT-5.6 Sol7.8%
    Source

    Not directly comparable

  • GeneBench-Pro

    Claude Fable 5—
    GPT-5.6 Sol28.7%
    Source

    Not directly comparable

Multimodal

  • Blueprint-Bench 2

    Claude Fable 538.6%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • OfficeQA Pro

    Claude Fable 557.9%
    Source
    GPT-5.6 Sol—

    Not directly comparable

  • MMMU-Pro

    Claude Fable 5—
    GPT-5.6 Sol83%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Claude Fable 5—
    GPT-5.6 Sol84.6%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Claude Fable 593.2%
    Source
    GPT-5.6 Sol95.2%
    Source

    GPT-5.6 Sol leads this result

  • MMLU-Pro (Vals)

    Claude Fable 591.5%
    Source
    GPT-5.6 Sol89.1%
    Source

    Claude Fable 5 leads this result

  • GPQA

    Claude Fable 5—
    GPT-5.6 Sol94.6%
    Source

    Not directly comparable

  • GPQA-D

    Claude Fable 5—
    GPT-5.6 Sol94.6%
    Source

    Not directly comparable

  • HLE-Verified

    Claude Fable 5—
    GPT-5.6 Sol54.5%
    Source

    Not directly comparable

  • LABBench2

    Claude Fable 5—
    GPT-5.6 Sol82.1%
    Source

    Not directly comparable

  • HealthBench Professional

    Claude Fable 5—
    GPT-5.6 Sol60.5%
    Source

    Not directly comparable

  • HealthBench Hard

    Claude Fable 5—
    GPT-5.6 Sol33.1%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    Claude Fable 5—
    GPT-5.6 Sol89%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Fable 5—
    GPT-5.6 Sol89.000%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Fable 5—
    GPT-5.6 Sol83.000%
    Source

    Not directly comparable

44 public results · 14 shared

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Last updated September 24, 2026