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Claude Mythos Preview vs DeepSeek V4.1 Flash 1017

Updated October 25, 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. 2 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

—

Evidence status unavailable

90% interval unavailable

Model B
DeepSeek logo

DeepSeek

65.78/100

Supported · Public rank #35

90% interval 60.8–70.8

Shared results
2
Claude Mythos Preview only
0
DeepSeek V4.1 Flash 1017 only
31
Like-for-like categories
0 / 8
Supported: DeepSeek V4.1 Flash 1017How 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

    DeepSeek V4.1 Flash 1017

    DeepSeek V4.1 Flash 1017 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

    DeepSeek V4.1 Flash 1017

    DeepSeek V4.1 Flash 1017 has the lower estimated token cost for this stated workload. Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback
Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    DeepSeek V4.1 Flash 1017

    DeepSeek V4.1 Flash 1017 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 Preview is 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 Preview is 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 Preview61.1DeepSeek V4.1 Flash 1017

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.

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.

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, 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 Preview
Not ranked
DeepSeek V4.1 Flash 1017
61.7
Supported · #24/132
Basis
BenchAlign v5.8 lane · 2 vs 11 public rows
Reading
Not comparable

Coding

Not comparable
Claude Mythos Preview
Not ranked
DeepSeek V4.1 Flash 1017
61.1
Supported · #18/153
Basis
BenchAlign v5.8 lane · 0 vs 8 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Mythos Preview
Not ranked
DeepSeek V4.1 Flash 1017
82.5
Unranked · 4 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Mythos Preview
Not ranked
DeepSeek V4.1 Flash 1017
78.8
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Claude Mythos Preview
Not ranked
DeepSeek V4.1 Flash 1017
64.4
Supported · #37/187
Basis
BenchAlign v5.8 lane · 0 vs 3 public rows
Reading
Not comparable

Multilingual

Not comparable
Claude Mythos Preview
Not ranked
DeepSeek V4.1 Flash 1017
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Mythos Preview
Not ranked
DeepSeek V4.1 Flash 1017
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Mythos Preview
Not ranked
DeepSeek V4.1 Flash 1017
70.1
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 v5.8) 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.

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 Mythos Preview
$0.0875
Fit state unavailable
DeepSeek V4.1 Flash 1017
$0.0009
Fits in one request

DeepSeek V4.1 Flash 1017 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Mythos Preview
$1.63
Fit state unavailable
DeepSeek V4.1 Flash 1017
$0.0186
Fits in one request

DeepSeek V4.1 Flash 1017 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 Preview
$6.75
Fit state unavailable
Cached input priced at the published list-input rate
DeepSeek V4.1 Flash 1017
$0.0192
Fits in one request

DeepSeek V4.1 Flash 1017 has the lower modeled cost

Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate.

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.

Documented inputs

Claude Mythos Preview

Not sourced

DeepSeek V4.1 Flash 1017

Reasoning profile

Claude Mythos Preview

Reasoning

DeepSeek V4.1 Flash 1017

Reasoning

Weight access

Claude Mythos Preview

Proprietary

DeepSeek V4.1 Flash 1017

Open Weight

License

Claude Mythos Preview

Proprietary

DeepSeek V4.1 Flash 1017

Open Weight

Release date

Claude Mythos Preview

2026-04-07

DeepSeek V4.1 Flash 1017

2026-10-17

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: $1.63 vs $0.0186. Cache-heavy agent loop: $6.75 vs $0.0192.
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 Preview or DeepSeek V4.1 Flash 1017?

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 Preview or DeepSeek V4.1 Flash 1017?

Claude Mythos Preview is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude Mythos Preview or DeepSeek V4.1 Flash 1017?

Claude Mythos Preview is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Mythos Preview or DeepSeek V4.1 Flash 1017?

For the stated presets, chat costs $0.0875 on Claude Mythos Preview and $0.0009 on DeepSeek V4.1 Flash 1017; repository review costs $1.63 and $0.0186; the cache-heavy agent loop costs $6.75 and $0.0192. Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Mythos Preview or DeepSeek V4.1 Flash 1017?

A complete documented context-window comparison is not available.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Mythos Preview
API / mo$112,500
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
DeepSeek V4.1 Flash 1017
API / mo$1,125
Self-host / mo$27,962
Break-even1.6B/day
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence33 rows

Agentic

  • CyberGym

    Claude Mythos Preview83.1%
    Source
    DeepSeek V4.1 Flash 101788.1%
    Source

    DeepSeek V4.1 Flash 1017 leads this result

  • ExploitGym

    Claude Mythos Preview17.5%
    Source
    DeepSeek V4.1 Flash 101715.3%
    Source

    Claude Mythos Preview leads this result

  • Terminal-Bench 2.1

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101790.6%
    Source

    Not directly comparable

  • terminalBench3

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101730%
    Source

    Not directly comparable

  • Terminal-Bench 4.0

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101742.30%
    Source

    Not directly comparable

  • HLE w/ tools

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101763.9%
    Source

    Not directly comparable

  • AutomationBench

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101760.5%
    Source

    Not directly comparable

  • Agents' Last Exam

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101735.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101774.5%
    Source

    Not directly comparable

  • CWE-bench v1

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101755.0%
    Source

    Not directly comparable

  • MazeBench

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 10171%
    Source

    Not directly comparable

Coding

  • Codeforces

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 10173471.0
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101790.6%
    Source

    Not directly comparable

  • terminalBench3

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101730%
    Source

    Not directly comparable

  • DeepSWE

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101777.0%
    Source

    Not directly comparable

  • ProgramBench

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101729.1%
    Source

    Not directly comparable

  • NL2Repo

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101768.2%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101760.3%
    Source

    Not directly comparable

  • Bug Hunt Bench

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101739.1 fixes
    Source

    Not directly comparable

Reasoning

  • STEMBench

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 10172.49
    Source

    Not directly comparable

  • JevBench 1.4

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 10174.77
    Source

    Not directly comparable

  • JevBench 1.5

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 10176.65
    Source

    Not directly comparable

Multimodal

  • ExtractBench (LlamaIndex)

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101787.11%
    Source

    Not directly comparable

  • ParseBench

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101764.56%
    Source

    Not directly comparable

  • Chartography (tools)

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101783.0%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101792.5%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101751.0%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101790.9%
    Source

    Not directly comparable

  • GPQA-D

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101790.9%
    Source

    Not directly comparable

  • HLE

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101742.6%
    Source

    Not directly comparable

Math

  • Apex

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101765.6%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101767.368%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Mythos Preview—
    DeepSeek V4.1 Flash 101726.829%
    Source

    Not directly comparable

33 public results · 2 shared

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Last updated October 25, 2026