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Claude Mythos 5.2 vs Pareto 26.10 Preview

Updated November 10, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 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
Claude Mythos 5.2

Anthropic

—

Evidence status unavailable

90% interval unavailable

Model B
Pareto 26.10 Preview

Unbiased

—

Evidence status unavailable

90% interval unavailable

API price per 1M tokens

Claude Mythos 5.2: $8 input / $40 output

Pareto 26.10 Preview: $2.50 input / $7.50 output

Context window

Claude Mythos 5.2: 1M

Pareto 26.10 Preview: Not sourced

Released

Claude Mythos 5.2: November 9, 2026

Pareto 26.10 Preview: October 1, 2026

Shared results
1
Claude Mythos 5.2 only
13
Pareto 26.10 Preview only
2
Like-for-like categories
0 / 8
How 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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Pareto 26.10 Preview

    Pareto 26.10 Preview 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

    Pareto 26.10 Preview

    Pareto 26.10 Preview 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

    Pareto 26.10 Preview

    Pareto 26.10 Preview has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Claude Mythos 5.2 and Pareto 26.10 Preview are not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Claude Mythos 5.2 and Pareto 26.10 Preview are not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    Confidence: limited

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.

—Claude Mythos 5.2—Pareto 26.10 Preview

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

What is actually comparable

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

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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

Not comparable
Claude Mythos 5.2
Not ranked
Pareto 26.10 Preview
Not ranked
Basis
BenchAlign v5.8 lane · 5 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
Claude Mythos 5.2
Not ranked
Pareto 26.10 Preview
Not ranked
Basis
BenchAlign v5.8 lane · 0 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Mythos 5.2
Not ranked
Pareto 26.10 Preview
Not ranked
Basis
BenchAlign v5.8 lane · 0 vs 0 public rows
Reading
Not comparable

Multimodal

Not comparable
Claude Mythos 5.2
Not ranked
Pareto 26.10 Preview
Not ranked
Basis
BenchAlign v5.8 lane · 1 vs 0 public rows
Reading
Not comparable

Knowledge

Not comparable
Claude Mythos 5.2
Not ranked
Pareto 26.10 Preview
Not ranked
Basis
BenchAlign v5.8 lane · 8 vs 1 public rows
Reading
Not comparable

Multilingual

Not comparable
Claude Mythos 5.2
Not ranked
Pareto 26.10 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Mythos 5.2
Not ranked
Pareto 26.10 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Mythos 5.2
Not ranked
Pareto 26.10 Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) 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.

Methodology

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 5.2
$0.028
Fits in one request
Pareto 26.10 Preview
$0.00625
Fit state unavailable

Pareto 26.10 Preview has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Mythos 5.2
$0.52
Fits in one request
Pareto 26.10 Preview
$0.1475
Fit state unavailable

Pareto 26.10 Preview 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 5.2
$0.6
Fits in one request
Pareto 26.10 Preview
$0.175
Fit state unavailable

Pareto 26.10 Preview has the lower modeled cost

Costs use the listed standard API rates.

Your workload

Enter a month of tokens to price it at both models’ listed API rates.

Claude Mythos 5.2
$160.00
per month
Pareto 26.10 Preview
$40.00
per month

Standard listed rates, no batch or volume discounts.

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.

Reasoning profile

Claude Mythos 5.2

Reasoning

Pareto 26.10 Preview

Reasoning

Weight access

Claude Mythos 5.2

Proprietary

Pareto 26.10 Preview

Proprietary

License

Claude Mythos 5.2

Proprietary

Pareto 26.10 Preview

Proprietary

Release date

Claude Mythos 5.2

2026-11-09

Pareto 26.10 Preview

2026-10-01

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.52 vs $0.1475. Cache-heavy agent loop: $0.6 vs $0.175.
Context tradeoff
A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Mythos 5.2 or Pareto 26.10 Preview?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude Mythos 5.2 or Pareto 26.10 Preview?

Claude Mythos 5.2 and Pareto 26.10 Preview are not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude Mythos 5.2 or Pareto 26.10 Preview?

Claude Mythos 5.2 and Pareto 26.10 Preview are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Mythos 5.2 or Pareto 26.10 Preview?

For the stated presets, chat costs $0.028 on Claude Mythos 5.2 and $0.00625 on Pareto 26.10 Preview; repository review costs $0.52 and $0.1475; the cache-heavy agent loop costs $0.6 and $0.175. Costs use the listed standard API rates.

Which has the larger context window, Claude Mythos 5.2 or Pareto 26.10 Preview?

A complete documented context-window comparison is not available.

Benchmark evidence

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

Browse raw public benchmark evidence16 rows

Agentic

  • Terminal-Bench 4.0

    Claude Mythos 5.270.50%
    Source
    Pareto 26.10 Preview50.80%
    Source

    Claude Mythos 5.2 leads this result

  • Terminal-Bench-Science 0.1

    Claude Mythos 5.275.3%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • CVE Rediscovery

    Claude Mythos 5.289.6%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • ExploitGym

    Claude Mythos 5.238.1%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • Binary Exploitation Benchmark

    Claude Mythos 5.2148
    Source
    Pareto 26.10 Preview—

    Not directly comparable

Coding

  • DeepSWE

    Claude Mythos 5.2—
    Pareto 26.10 Preview69.9%
    Source

    Not directly comparable

Multimodal

  • Biomedical image analysis

    Claude Mythos 5.280.7%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

Knowledge

  • De novo protein-binder design

    Claude Mythos 5.285.1%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • Protein Design

    Claude Mythos 5.264.6%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • SingleCellBench

    Claude Mythos 5.265.6%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • Protocols (troubleshooting)

    Claude Mythos 5.277.1%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • Protein Domain ID

    Claude Mythos 5.291.8%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • Protein Mutation Attribution

    Claude Mythos 5.251.5%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • Morphology-to-molecule matching

    Claude Mythos 5.235.2%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • Medicinal chemistry

    Claude Mythos 5.269.0%
    Source
    Pareto 26.10 Preview—

    Not directly comparable

  • GPQA-D

    Claude Mythos 5.2—
    Pareto 26.10 Preview92.4%
    Source

    Not directly comparable

16 public results · 1 shared

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Last updated November 10, 2026