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BenchLM
Model A
Claude Fable 5

Anthropic

83.0/100

Supported · Public rank #3

90% interval 80.1–85.8

Claude Fable 5 vs Claude Sonnet 5

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Claude Sonnet 5

Anthropic

64.8/100

Estimated · Public rank #37

90% interval 50.5–79.1

Decision reading

Claude Fable 5 has the higher public score, 82.96 versus 64.78, and the 90% score intervals do not overlap.

11 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

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 same 2 weighted benchmark rows.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Claude Sonnet 5

    Claude Sonnet 5 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

    Claude Sonnet 5

    Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Claude Sonnet 5

    Claude Sonnet 5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Agentic work

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

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

What is actually comparable

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

Shared results
11
Claude Fable 5 only
5
Claude Sonnet 5 only
8
Like-for-like categories
1 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Coding

Like-for-like
Claude Fable 5
89.2
Claude Sonnet 5
76.7
Weighted basis
2 vs 2 rows
Reading
Claude Fable 5 leads

Agentic

Directional only
Claude Fable 5
84.6
Claude Sonnet 5
81.9
Weighted basis
2 vs 3 rows
Reading
Directional only

Reasoning

Not comparable
Claude Fable 5
Not measured
Claude Sonnet 5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Fable 5
Not measured
Claude Sonnet 5
57.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5
Not measured
Claude Sonnet 5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5
Not measured
Claude Sonnet 5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5
57.9
Claude Sonnet 5
88.3
Weighted basis
1 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Fable 5
Not measured
Claude Sonnet 5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

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.

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
Claude Sonnet 5
$0.007
Fits in one request

Claude Sonnet 5 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
Claude Sonnet 5
$0.13
Fits in one request

Claude Sonnet 5 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
Claude Sonnet 5
$0.18
Fits in one request

Claude Sonnet 5 has the lower modeled cost

Costs use the listed standard API rates.

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

Claude Sonnet 5

$0.2 per 1M cached input tokens

Claude API pricing

Reasoning profile

Claude Fable 5

Reasoning

Claude Sonnet 5

Reasoning

Weight access

Claude Fable 5

Proprietary

Claude Sonnet 5

Proprietary

License

Claude Fable 5

Proprietary

Claude Sonnet 5

Proprietary

Release date

Claude Fable 5

2026-06-09

Claude Sonnet 5

2026-06-30

If you already use one of these models
Deployment change
Both entries list Anthropic as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
Claude Fable 5 has the higher public score, 82.96 versus 64.78, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.65 vs $0.13. Cache-heavy agent loop: $0.9 vs $0.18.
Context tradeoff
Both models list 1M.

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

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

Browse raw public benchmark evidence24 rows

Agentic

  • Terminal-Bench 3.0

    Shared source
    Claude Fable 534.0%
    Claude Sonnet 514.6%

    Claude Fable 5 leads this result

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    Claude Sonnet 580.4%
    Source

    Claude Fable 5 leads this result

  • OSWorld-Verified

    Claude Fable 585%
    Source
    Claude Sonnet 581.2%
    Source

    Claude Fable 5 leads this result

  • BrowseComp

    Claude Fable 5—
    Claude Sonnet 584.7%
    Source

    Not directly comparable

  • HLE w/ tools

    Claude Fable 5—
    Claude Sonnet 557.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Fable 595%
    Source
    Claude Sonnet 585.2%
    Source

    Claude Fable 5 leads this result

  • SWE-bench Pro

    Claude Fable 580%
    Source
    Claude Sonnet 563.2%
    Source

    Claude Fable 5 leads this result

  • FrontierCode 1.1 Main

    Shared source
    Claude Fable 553.5%
    Claude Sonnet 542.7%

    Claude Fable 5 leads this result

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    Claude Sonnet 580.4%
    Source

    Claude Fable 5 leads this result

  • cursorBench31

    Claude Fable 570.6%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • cursorBench32

    Shared source
    Claude Fable 570.5%
    Claude Sonnet 561.5%

    Claude Fable 5 leads this result

  • VulcanBench v3

    Claude Fable 587.0%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • APEX-SWE

    Shared source
    Claude Fable 558.8%
    Claude Sonnet 546.4%

    Claude Fable 5 leads this result

  • EEBench

    Shared source
    Claude Fable 554.2%
    Claude Sonnet 540.3%

    Claude Fable 5 leads this result

  • 3DCodeBench

    Shared source
    Claude Fable 543.7%
    Claude Sonnet 539.2%

    Claude Fable 5 leads this result

  • CADGenBench Generation

    Claude Fable 537.3%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • SWE Multilingual

    Claude Fable 5—
    Claude Sonnet 578.3%
    Source

    Not directly comparable

  • SWE Multimodal

    Claude Fable 5—
    Claude Sonnet 528.1%
    Source

    Not directly comparable

Knowledge

  • HLE

    Claude Fable 5—
    Claude Sonnet 557.4%
    Source

    Not directly comparable

  • HLE w/o tools

    Claude Fable 5—
    Claude Sonnet 543.2%
    Source

    Not directly comparable

Multimodal

  • Blueprint-Bench 2

    Claude Fable 538.6%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • OfficeQA Pro

    Claude Fable 557.9%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • CharXiv

    Claude Fable 5—
    Claude Sonnet 588.3%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Claude Fable 5—
    Claude Sonnet 577%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Fable 5 or Claude Sonnet 5?

Claude Fable 5 has the higher public score, 82.96 versus 64.78, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Fable 5 or Claude Sonnet 5?

Claude Fable 5 leads the like-for-like coding comparison across 2 shared weighted benchmark rows.

Which is better for agentic tasks, Claude Fable 5 or Claude Sonnet 5?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Fable 5 or Claude Sonnet 5?

For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.007 on Claude Sonnet 5; repository review costs $0.65 and $0.13; the cache-heavy agent loop costs $0.9 and $0.18. Costs use the listed standard API rates.

Which has the larger context window, Claude Fable 5 or Claude Sonnet 5?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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