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BenchLM
Model A
Claude Fable 5

Anthropic

82.7/100

Supported · Public rank #3

90% interval 80.2–85.2

Claude Fable 5 vs Claude Mythos 5

Updated August 22, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Claude Mythos 5

Anthropic

83.0/100

Supported · Public rank #1

90% interval 79.6–86.3

Decision reading

Claude Mythos 5 has the higher public score estimate, 82.95 versus 82.7, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

5 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 Mythos 5

    Claude Mythos 5 leads on the same 2 weighted benchmark rows.

    Confidence: limited

Show secondary and unsupported calls

  • 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

  • Chat turn cost

    1K fresh input + 500 output tokens

    No clear pick

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    No clear pick

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    No clear pick

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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
5
Claude Fable 5 only
7
Claude Mythos 5 only
10
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 Mythos 5
89.7
Weighted basis
2 vs 2 rows
Reading
Claude Mythos 5 leads

Agentic

Directional only
Claude Fable 5
84.6
Claude Mythos 5
87.0
Weighted basis
2 vs 3 rows
Reading
Directional only

Reasoning

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

Knowledge

Not comparable
Claude Fable 5
Not measured
Claude Mythos 5
68.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5
Not measured
Claude Mythos 5
97.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

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

Multimodal

Not comparable
Claude Fable 5
57.9
Claude Mythos 5
93.5
Weighted basis
1 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Fable 5
Not measured
Claude Mythos 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.

  • Terminal-Bench 2.0

    Agentic

    Claude Fable 5: 84.3%Claude Mythos 5: 88%Normalized gap 3.7Shared source

  • SWE-bench Verified

    Coding

    Claude Fable 5: 95%Claude Mythos 5: 95.5%Normalized gap 0.5Shared source

  • SWE-bench Pro

    Coding

    Claude Fable 5: 80%Claude Mythos 5: 80.3%Normalized gap 0.3Shared source

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 Mythos 5
$0.035
Fits in one request

Modeled costs are equal

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 Mythos 5
$0.65
Fits in one request

Modeled costs are equal

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 Mythos 5
$0.9
Fits in one request

Modeled costs are equal

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 Mythos 5

$1 per 1M cached input tokens

Claude API pricing

Reasoning profile

Claude Fable 5

Reasoning

Claude Mythos 5

Reasoning

Weight access

Claude Fable 5

Proprietary

Claude Mythos 5

Proprietary

License

Claude Fable 5

Proprietary

Claude Mythos 5

Proprietary

Release date

Claude Fable 5

2026-06-09

Claude Mythos 5

2026-06-09

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 Mythos 5 has the higher public score estimate, 82.95 versus 82.7, but the 90% score intervals overlap.
Workload cost
Repository review: $0.65 vs $0.65. Cache-heavy agent loop: $0.9 vs $0.9.
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 evidence22 rows

Agentic

  • Terminal-Bench 3.0

    Claude Fable 534.0%
    Source
    Claude Mythos 5—

    Not directly comparable

  • Terminal-Bench 2.0

    Shared source
    Claude Fable 584.3%
    Claude Mythos 588%

    Claude Mythos 5 leads this result

  • OSWorld-Verified

    Shared source
    Claude Fable 585%
    Claude Mythos 585%

    Tie

  • BrowseComp

    Claude Fable 5—
    Claude Mythos 588%
    Source

    Not directly comparable

  • CyberGym

    Claude Fable 5—
    Claude Mythos 583.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Shared source
    Claude Fable 595%
    Claude Mythos 595.5%

    Claude Mythos 5 leads this result

  • SWE-bench Pro

    Shared source
    Claude Fable 580%
    Claude Mythos 580.3%

    Claude Mythos 5 leads this result

  • FrontierCode 1.1 Main

    Claude Fable 553.5%
    Source
    Claude Mythos 5—

    Not directly comparable

  • Terminal-Bench 2.0

    Shared source
    Claude Fable 584.3%
    Claude Mythos 588.0%

    Claude Mythos 5 leads this result

  • cursorBench31

    Claude Fable 570.6%
    Source
    Claude Mythos 5—

    Not directly comparable

  • cursorBench32

    Claude Fable 570.5%
    Source
    Claude Mythos 5—

    Not directly comparable

  • VulcanBench v3

    Claude Fable 587.0%
    Source
    Claude Mythos 5—

    Not directly comparable

Knowledge

  • GPQA

    Claude Fable 5—
    Claude Mythos 594.1%
    Source

    Not directly comparable

  • HLE

    Claude Fable 5—
    Claude Mythos 564.5%
    Source

    Not directly comparable

  • HLE w/o tools

    Claude Fable 5—
    Claude Mythos 559%
    Source

    Not directly comparable

Math

  • USAMO 2026

    Claude Fable 5—
    Claude Mythos 597.6%
    Source

    Not directly comparable

Multilingual

  • SWE Multilingual

    Claude Fable 5—
    Claude Mythos 592.2%
    Source

    Not directly comparable

Multimodal

  • Blueprint-Bench 2

    Claude Fable 538.6%
    Source
    Claude Mythos 5—

    Not directly comparable

  • OfficeQA Pro

    Claude Fable 557.9%
    Source
    Claude Mythos 5—

    Not directly comparable

  • SWE-bench Multimodal

    Claude Fable 5—
    Claude Mythos 554.9%
    Source

    Not directly comparable

  • CharXiv

    Claude Fable 5—
    Claude Mythos 593.5%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Claude Fable 5—
    Claude Mythos 588.9%
    Source

    Not directly comparable

Frequently asked questions

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

Claude Mythos 5 has the higher public score estimate, 82.95 versus 82.7, 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 Claude Mythos 5?

Claude Mythos 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 Mythos 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 Mythos 5?

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

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

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 22, 2026

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