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Model A
Claude Fable 5.1

Anthropic

84.38/100

Supported · Public rank #1

90% interval 78.290.5

Claude Fable 5.1 vs DeepSeek V4.1 Flash

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

DeepSeek logo
Model B
DeepSeek V4.1 Flash

DeepSeek

Evidence status unavailable

90% interval unavailable

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.

5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

    DeepSeek V4.1 Flash 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

    DeepSeek V4.1 Flash 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

    DeepSeek V4.1 Flash

    DeepSeek V4.1 Flash 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

    DeepSeek V4.1 Flash 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

    DeepSeek V4.1 Flash 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

    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
5
Claude Fable 5.1 only
19
DeepSeek V4.1 Flash only
16
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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 Fable 5.1
80.1
Supported · #1/152
DeepSeek V4.1 Flash
Not ranked
Basis
BenchAlign lane · 9 vs 8 public rows
Reading
Not comparable

Coding

Not comparable
Claude Fable 5.1
84.2
Supported · #1/151
DeepSeek V4.1 Flash
Not ranked
Basis
BenchAlign lane · 9 vs 6 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Fable 5.1
79.4
#2/18
DeepSeek V4.1 Flash
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Claude Fable 5.1
86.8
Supported · #1/182
DeepSeek V4.1 Flash
Not ranked
Basis
BenchAlign lane · 4 vs 3 public rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5.1
Not ranked
DeepSeek V4.1 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5.1
Not ranked
DeepSeek V4.1 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5.1
Not ranked
DeepSeek V4.1 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Fable 5.1
Not ranked
DeepSeek V4.1 Flash
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, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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 5.1
$0.035
Fits in one request
DeepSeek V4.1 Flash
$0.0009
Fits in one request

DeepSeek V4.1 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Fable 5.1
$0.65
Fits in one request
DeepSeek V4.1 Flash
$0.0186
Fits in one request

DeepSeek V4.1 Flash 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 5.1
$0.75
Fits in one request
DeepSeek V4.1 Flash
$0.0192
Fits in one request

DeepSeek V4.1 Flash 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

$0.25 per 1M cached input tokens

Anthropic Fable 5.1 launch

DeepSeek V4.1 Flash

$0.006 per 1M cached input tokens

DeepSeek: Models & Pricing

Reasoning profile

Claude Fable 5.1

Reasoning

DeepSeek V4.1 Flash

Reasoning

Weight access

Claude Fable 5.1

Proprietary

DeepSeek V4.1 Flash

Open Weight

License

Claude Fable 5.1

Proprietary

DeepSeek V4.1 Flash

Open Weight

Release date

Claude Fable 5.1

2026-09-01

DeepSeek V4.1 Flash

2026-09-10

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: $0.65 vs $0.0186. Cache-heavy agent loop: $0.75 vs $0.0192.
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 evidence40 rows

Agentic

  • Terminal-Bench 4.0

    Claude Fable 5.155.80%
    Source
    DeepSeek V4.1 Flash31.20%
    Source

    Claude Fable 5.1 leads this result

  • Terminal-Bench-Science 0.1

    Claude Fable 5.152.6%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • OSWorld 2.0

    Claude Fable 5.141.7%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • AutomationBench

    Claude Fable 5.131.4%
    Source
    DeepSeek V4.1 Flash54.8%
    Source

    DeepSeek V4.1 Flash leads this result

  • Toolathlon-Verified

    Claude Fable 5.177.8%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • Toolathlon Verified Pass@3

    Claude Fable 5.181.5%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • Toolathlon Verified Pass³

    Claude Fable 5.173.1%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • Toolathlon Verified avg. turns

    Claude Fable 5.123.7 turns
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Fable 5.185.0%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Fable 5.1
    DeepSeek V4.1 Flash90.6%
    Source

    Not directly comparable

  • terminalBench3

    Claude Fable 5.1
    DeepSeek V4.1 Flash30%
    Source

    Not directly comparable

  • CyberGym

    Claude Fable 5.1
    DeepSeek V4.1 Flash88.1%
    Source

    Not directly comparable

  • ExploitGym

    Claude Fable 5.1
    DeepSeek V4.1 Flash15.3%
    Source

    Not directly comparable

  • HLE w/ tools

    Claude Fable 5.1
    DeepSeek V4.1 Flash63.9%
    Source

    Not directly comparable

  • Agents' Last Exam

    Claude Fable 5.1
    DeepSeek V4.1 Flash31.8%
    Source

    Not directly comparable

Coding

  • Bug Hunt Bench

    Claude Fable 5.143 fixes
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • SWE-bench Pro

    Claude Fable 5.181.2%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • SWE Multilingual

    Claude Fable 5.189.1%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • SWE Multimodal

    Claude Fable 5.154.7%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • DeepSWE

    Claude Fable 5.167.4%
    Source
    DeepSeek V4.1 Flash74.2%
    Source

    DeepSeek V4.1 Flash leads this result

  • FrontierSWE v2

    Claude Fable 5.156.3%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • ProgramBench

    Claude Fable 5.187.6%
    Source
    DeepSeek V4.1 Flash20.3%
    Source

    Claude Fable 5.1 leads this result

  • cursorBench32

    Claude Fable 5.173.4%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Fable 5.190.5%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • Codeforces

    Claude Fable 5.1
    DeepSeek V4.1 Flash3471.0
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Fable 5.1
    DeepSeek V4.1 Flash90.6%
    Source

    Not directly comparable

  • terminalBench3

    Claude Fable 5.1
    DeepSeek V4.1 Flash30%
    Source

    Not directly comparable

  • NL2Repo

    Claude Fable 5.1
    DeepSeek V4.1 Flash65.4%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-1

    Claude Fable 5.197.50%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • ARC-AGI-2

    Claude Fable 5.190%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

Knowledge

  • HLE

    Claude Fable 5.165%
    Source
    DeepSeek V4.1 Flash36.8%
    Source

    Claude Fable 5.1 leads this result

  • HLE w/o tools

    Claude Fable 5.160.9%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude Fable 5.193.4%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Fable 5.192.4%
    Source
    DeepSeek V4.1 Flash

    Not directly comparable

  • GPQA

    Claude Fable 5.1
    DeepSeek V4.1 Flash90.9%
    Source

    Not directly comparable

  • GPQA-D

    Claude Fable 5.1
    DeepSeek V4.1 Flash90.9%
    Source

    Not directly comparable

Math

  • Apex

    Claude Fable 5.1
    DeepSeek V4.1 Flash65.6%
    Source

    Not directly comparable

Multimodal

  • Chartography (tools)

    Claude Fable 5.1
    DeepSeek V4.1 Flash78.9%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Claude Fable 5.1
    DeepSeek V4.1 Flash89.6%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    Claude Fable 5.1
    DeepSeek V4.1 Flash49.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Fable 5.1 or DeepSeek V4.1 Flash?

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 Fable 5.1 or DeepSeek V4.1 Flash?

DeepSeek V4.1 Flash is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude Fable 5.1 or DeepSeek V4.1 Flash?

DeepSeek V4.1 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Fable 5.1 or DeepSeek V4.1 Flash?

For the stated presets, chat costs $0.035 on Claude Fable 5.1 and $0.0009 on DeepSeek V4.1 Flash; repository review costs $0.65 and $0.0186; the cache-heavy agent loop costs $0.75 and $0.0192. Costs use the listed standard API rates.

Which has the larger context window, Claude Fable 5.1 or DeepSeek V4.1 Flash?

Both models list the same context window, 1M.

Related comparisons

Last updated September 10, 2026

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