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BenchLM
Model A
Nemotron-4 15B

NVIDIA

—

Evidence status unavailable

90% interval unavailable

Nemotron-4 15B vs Sakana Fugu

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

Model B
Sakana Fugu

Sakana AI

—

Evidence status unavailable

90% interval unavailable

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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.

  • Long documents

    Prompts that approach the documented context limit

    Sakana Fugu

    Sakana Fugu has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Nemotron-4 15B does not fit this workload in one request. Nemotron-4 15B has no comparable published API token rate. Sakana Fugu has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Nemotron-4 15B does not fit this workload in one request. Nemotron-4 15B has no comparable published API token rate. Sakana Fugu has no comparable published API token rate.

    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
0
Nemotron-4 15B only
0
Sakana Fugu only
11
Like-for-like categories
0 / 8

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

Not comparable
Nemotron-4 15B
Not measured
Sakana Fugu
80.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Nemotron-4 15B
Not measured
Sakana Fugu
59.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Nemotron-4 15B
Not measured
Sakana Fugu
86.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Nemotron-4 15B
Not measured
Sakana Fugu
95.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Nemotron-4 15B
Not measured
Sakana Fugu
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Nemotron-4 15B
Not measured
Sakana Fugu
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Nemotron-4 15B
Not measured
Sakana Fugu
85.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Nemotron-4 15B
Not measured
Sakana Fugu
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.

A shared-evidence shape is not available.

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

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

Nemotron-4 15B
Self-hosted; infrastructure cost varies
Fits in one request
Sakana Fugu
API rate not published
Fits in one request

Nemotron-4 15B has no comparable published API token rate. Sakana Fugu has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Nemotron-4 15B
Self-hosted; infrastructure cost varies
Does not fit in one request
Sakana Fugu
API rate not published
Fits in one request

Nemotron-4 15B does not fit this workload in one request. Nemotron-4 15B has no comparable published API token rate. Sakana Fugu has no comparable published API token rate.

Cache-heavy agent loop

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

Nemotron-4 15B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Sakana Fugu
API rate not published
Fits in one request
Cached-input rate unavailable

Nemotron-4 15B does not fit this workload in one request. Nemotron-4 15B has no comparable published API token rate. Sakana Fugu 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.

Nemotron-4 15B

32K

Sakana Fugu

1M

API model ID

Nemotron-4 15B

Not sourced

Sakana Fugu

Not sourced

Cached-input rate

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

Nemotron-4 15B

No comparable hosted API rate

Sakana Fugu

No comparable hosted API rate

Documented inputs

Nemotron-4 15B

Not sourced

Sakana Fugu

Not sourced

Documented outputs

Nemotron-4 15B

Not sourced

Sakana Fugu

Not sourced

Provider availability

Nemotron-4 15B

Not sourced

Sakana Fugu

Not sourced

Reasoning profile

Nemotron-4 15B

Non-Reasoning

Sakana Fugu

Reasoning

Weight access

Nemotron-4 15B

Open Weight

Sakana Fugu

Proprietary

License

Nemotron-4 15B

Open Weight

Sakana Fugu

Proprietary

Release date

Nemotron-4 15B

Not sourced

Sakana Fugu

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Sakana Fugu has the larger documented window (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 evidence11 rows

Agentic

  • Terminal-Bench 2.0

    Nemotron-4 15B—
    Sakana Fugu80.2%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    Nemotron-4 15B—
    Sakana Fugu59%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Nemotron-4 15B—
    Sakana Fugu80.2%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Nemotron-4 15B—
    Sakana Fugu92.9%
    Source

    Not directly comparable

  • LiveCodeBench Pro

    Nemotron-4 15B—
    Sakana Fugu87.8%
    Source

    Not directly comparable

  • SciCode

    Nemotron-4 15B—
    Sakana Fugu60.1%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Nemotron-4 15B—
    Sakana Fugu86.6%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Nemotron-4 15B—
    Sakana Fugu95.5%
    Source

    Not directly comparable

  • GPQA-D

    Nemotron-4 15B—
    Sakana Fugu95.5%
    Source

    Not directly comparable

  • HLE w/o tools

    Nemotron-4 15B—
    Sakana Fugu47.2%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Nemotron-4 15B—
    Sakana Fugu85.1%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Nemotron-4 15B or Sakana Fugu?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Nemotron-4 15B or Sakana Fugu?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Nemotron-4 15B or Sakana Fugu?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Nemotron-4 15B or Sakana Fugu?

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, Nemotron-4 15B or Sakana Fugu?

Sakana Fugu has the larger documented context window: 1M, compared with 32K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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