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BenchLM
Model A
Claude Mythos 5

Anthropic

83.2/100

Supported · Public rank #1

90% interval 79.6–86.9

Claude Mythos 5 vs Claude Sonnet 5

Updated August 15, 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 Mythos 5 has the higher public score, 83.21 versus 64.78, and the 90% score intervals do not overlap.

10 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

  • Agentic work

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

    Claude Mythos 5

    Claude Mythos 5 leads on the same 3 weighted benchmark rows.

    Confidence: stronger

  • 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

Show secondary and unsupported calls

  • 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

  • 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

  • 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
10
Claude Mythos 5 only
5
Claude Sonnet 5 only
9
Like-for-like categories
3 / 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.

Agentic

Like-for-like
Claude Mythos 5
87.0
Claude Sonnet 5
81.9
Weighted basis
3 vs 3 rows
Reading
Claude Mythos 5 leads

Coding

Like-for-like
Claude Mythos 5
89.7
Claude Sonnet 5
76.7
Weighted basis
2 vs 2 rows
Reading
Claude Mythos 5 leads

Multimodal

Like-for-like
Claude Mythos 5
93.5
Claude Sonnet 5
88.3
Weighted basis
1 vs 1 rows
Reading
Claude Mythos 5 leads

Knowledge

Directional only
Claude Mythos 5
68.5
Claude Sonnet 5
57.4
Weighted basis
2 vs 1 rows
Reading
Directional only

Reasoning

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

Math

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

Multilingual

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

Instruction following

Not comparable
Claude Mythos 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 Mythos 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 Mythos 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 Mythos 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 Mythos 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 Mythos 5

Reasoning

Claude Sonnet 5

Reasoning

Weight access

Claude Mythos 5

Proprietary

Claude Sonnet 5

Proprietary

License

Claude Mythos 5

Proprietary

Claude Sonnet 5

Proprietary

Release date

Claude Mythos 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 Mythos 5 has the higher public score, 83.21 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 2.0

    Claude Mythos 588%
    Source
    Claude Sonnet 580.4%
    Source

    Claude Mythos 5 leads this result

  • OSWorld-Verified

    Claude Mythos 585%
    Source
    Claude Sonnet 581.2%
    Source

    Claude Mythos 5 leads this result

  • BrowseComp

    Claude Mythos 588%
    Source
    Claude Sonnet 584.7%
    Source

    Claude Mythos 5 leads this result

  • CyberGym

    Claude Mythos 583.8%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • Terminal-Bench 3.0

    Claude Mythos 5—
    Claude Sonnet 514.6%
    Source

    Not directly comparable

  • HLE w/ tools

    Claude Mythos 5—
    Claude Sonnet 557.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Mythos 595.5%
    Source
    Claude Sonnet 585.2%
    Source

    Claude Mythos 5 leads this result

  • SWE-bench Pro

    Claude Mythos 580.3%
    Source
    Claude Sonnet 563.2%
    Source

    Claude Mythos 5 leads this result

  • Terminal-Bench 2.0

    Claude Mythos 588.0%
    Source
    Claude Sonnet 580.4%
    Source

    Claude Mythos 5 leads this result

  • SWE Multilingual

    Claude Mythos 5—
    Claude Sonnet 578.3%
    Source

    Not directly comparable

  • SWE Multimodal

    Claude Mythos 5—
    Claude Sonnet 528.1%
    Source

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Mythos 5—
    Claude Sonnet 542.7%
    Source

    Not directly comparable

  • cursorBench32

    Claude Mythos 5—
    Claude Sonnet 561.5%
    Source

    Not directly comparable

  • APEX-SWE

    Claude Mythos 5—
    Claude Sonnet 546.4%
    Source

    Not directly comparable

  • EEBench

    Claude Mythos 5—
    Claude Sonnet 540.3%
    Source

    Not directly comparable

  • 3DCodeBench

    Claude Mythos 5—
    Claude Sonnet 539.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Mythos 594.1%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • HLE

    Claude Mythos 564.5%
    Source
    Claude Sonnet 557.4%
    Source

    Claude Mythos 5 leads this result

  • HLE w/o tools

    Claude Mythos 559%
    Source
    Claude Sonnet 543.2%
    Source

    Claude Mythos 5 leads this result

Math

  • USAMO 2026

    Claude Mythos 597.6%
    Source
    Claude Sonnet 5—

    Not directly comparable

Multilingual

  • SWE Multilingual

    Claude Mythos 592.2%
    Source
    Claude Sonnet 5—

    Not directly comparable

Multimodal

  • SWE-bench Multimodal

    Claude Mythos 554.9%
    Source
    Claude Sonnet 5—

    Not directly comparable

  • CharXiv

    Claude Mythos 593.5%
    Source
    Claude Sonnet 588.3%
    Source

    Claude Mythos 5 leads this result

  • CharXiv w/o tools

    Claude Mythos 588.9%
    Source
    Claude Sonnet 577%
    Source

    Claude Mythos 5 leads this result

Frequently asked questions

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

Claude Mythos 5 has the higher public score, 83.21 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 Mythos 5 or Claude Sonnet 5?

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

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

Claude Mythos 5 leads the like-for-like agentic tasks comparison across 3 shared weighted benchmark rows.

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

For the stated presets, chat costs $0.035 on Claude Mythos 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 Mythos 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 15, 2026

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