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BenchLM
Model A
DeepSeek V4 Pro 0813

DeepSeek

60.9/100

Estimated · Public rank #48

90% interval 51.1–70.8

DeepSeek V4 Pro 0813 vs Sakana Fugu-Ultra

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Sakana Fugu-Ultra

Sakana AI

—

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 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • 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

    Not enough matched evidence

    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

    Not enough matched evidence

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

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    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
DeepSeek V4 Pro 0813 only
29
Sakana Fugu-Ultra only
6
Like-for-like categories
0 / 8

3 categories use 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

Directional only
DeepSeek V4 Pro 0813
74.5
Sakana Fugu-Ultra
82.1
Weighted basis
2 vs 1 rows
Reading
Directional only

Coding

Directional only
DeepSeek V4 Pro 0813
70.9
Sakana Fugu-Ultra
64.5
Weighted basis
2 vs 2 rows
Reading
Directional only

Knowledge

Directional only
DeepSeek V4 Pro 0813
62.5
Sakana Fugu-Ultra
95.5
Weighted basis
4 vs 1 rows
Reading
Directional only

Reasoning

Not comparable
DeepSeek V4 Pro 0813
Not measured
Sakana Fugu-Ultra
93.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro 0813
95.2
Sakana Fugu-Ultra
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro 0813
Not measured
Sakana Fugu-Ultra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro 0813
Not measured
Sakana Fugu-Ultra
86.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro 0813
Not measured
Sakana Fugu-Ultra
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

DeepSeek V4 Pro 0813
$0.00087
Fits in one request
Sakana Fugu-Ultra
API rate not published
Fits in one request

Sakana Fugu-Ultra has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro 0813
$0.02436
Fits in one request
Sakana Fugu-Ultra
API rate not published
Fits in one request

Sakana Fugu-Ultra has no comparable published API token rate.

Cache-heavy agent loop

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

DeepSeek V4 Pro 0813
$0.01812
Fits in one request
Sakana Fugu-Ultra
API rate not published
Fits in one request
Cached-input rate unavailable

Sakana Fugu-Ultra has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

DeepSeek V4 Pro 0813

1M

DeepSeek models and pricing

Sakana Fugu-Ultra

1M

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

DeepSeek V4 Pro 0813

$0.003625 per 1M cached input tokens

Sakana Fugu-Ultra

No comparable hosted API rate

Reasoning profile

DeepSeek V4 Pro 0813

Reasoning

Sakana Fugu-Ultra

Reasoning

Weight access

DeepSeek V4 Pro 0813

Proprietary

Sakana Fugu-Ultra

Proprietary

License

DeepSeek V4 Pro 0813

Proprietary

Sakana Fugu-Ultra

Proprietary

Release date

DeepSeek V4 Pro 0813

2026-08-13

Sakana Fugu-Ultra

2026-06-22

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
A complete comparable API-rate estimate is not available for both models.
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 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    Sakana Fugu-Ultra82.1%
    Source

    Sakana Fugu-Ultra leads this result

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Pro 081383.4%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4 Pro 081360.0%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro 081373.6%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Pro 081351.8%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • CyberGym

    DeepSeek V4 Pro 081383.3%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Pro 081374.1%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Pro 081325.7%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Pro 081331.8%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro 081393.5%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Pro 08133206.0
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro 081380.6%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro 081355.4%
    Source
    Sakana Fugu-Ultra73.7%
    Source

    Sakana Fugu-Ultra leads this result

  • SWE Multilingual

    DeepSeek V4 Pro 081376.2%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro 081367.9%
    Source
    Sakana Fugu-Ultra82.1%
    Source

    Sakana Fugu-Ultra leads this result

  • Vibe Code Bench

    DeepSeek V4 Pro 081349.93%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Pro 081387.9%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • NL2Repo

    DeepSeek V4 Pro 081361.5%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • deepSwe

    DeepSeek V4 Pro 081362.7%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • DSBench-FullStack

    DeepSeek V4 Pro 081371.1%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Pro 081367.2%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • LiveCodeBench v6

    DeepSeek V4 Pro 0813—
    Sakana Fugu-Ultra93.2%
    Source

    Not directly comparable

  • LiveCodeBench Pro

    DeepSeek V4 Pro 0813—
    Sakana Fugu-Ultra90.8%
    Source

    Not directly comparable

  • SciCode

    DeepSeek V4 Pro 0813—
    Sakana Fugu-Ultra58.7%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro 081383.5%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro 081362.0%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • MRCRv2

    DeepSeek V4 Pro 0813—
    Sakana Fugu-Ultra93.6%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro 081387.5%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro 081357.9%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro 081384.4%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro 081390.1%
    Source
    Sakana Fugu-Ultra95.5%
    Source

    Sakana Fugu-Ultra leads this result

  • GPQA-D

    DeepSeek V4 Pro 081390.1%
    Source
    Sakana Fugu-Ultra95.5%
    Source

    Sakana Fugu-Ultra leads this result

  • HLE

    DeepSeek V4 Pro 081342.7%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • HLE w/o tools

    DeepSeek V4 Pro 0813—
    Sakana Fugu-Ultra50%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro 081395.2%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro 081389.8%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro 081338.3%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro 081390.2%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

Multimodal

  • CharXiv

    DeepSeek V4 Pro 0813—
    Sakana Fugu-Ultra86.6%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Pro 0813 or Sakana Fugu-Ultra?

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, DeepSeek V4 Pro 0813 or Sakana Fugu-Ultra?

The current coding 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 is better for agentic tasks, DeepSeek V4 Pro 0813 or Sakana Fugu-Ultra?

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, DeepSeek V4 Pro 0813 or Sakana Fugu-Ultra?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, DeepSeek V4 Pro 0813 or Sakana Fugu-Ultra?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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