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BenchLM
Model A
Grok 4.20

xAI

54.2/100

Estimated · Public rank #92

90% interval 37.4–71.1

Grok 4.20 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.

6 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.

  • Agentic work

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

    Sakana Fugu-Ultra

    Sakana Fugu-Ultra leads on the same 1 weighted benchmark row.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    Grok 4.20

    Grok 4.20 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

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

    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

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

    Confidence: rate-fallback

  • 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
6
Grok 4.20 only
12
Sakana Fugu-Ultra only
5
Like-for-like categories
1 / 8

2 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

Like-for-like
Grok 4.20
47.1
Sakana Fugu-Ultra
82.1
Weighted basis
1 vs 1 rows
Reading
Sakana Fugu-Ultra leads

Coding

Directional only
Grok 4.20
67.1
Sakana Fugu-Ultra
64.5
Weighted basis
2 vs 2 rows
Reading
Directional only

Multimodal

Directional only
Grok 4.20
70.1
Sakana Fugu-Ultra
86.6
Weighted basis
2 vs 1 rows
Reading
Directional only

Reasoning

Not comparable
Grok 4.20
53.3
Sakana Fugu-Ultra
93.6
Weighted basis
1 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Grok 4.20
Not measured
Sakana Fugu-Ultra
95.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Grok 4.20
Not measured
Sakana Fugu-Ultra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Grok 4.20
Not measured
Sakana Fugu-Ultra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Grok 4.20
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

Grok 4.20
$0.005
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

Grok 4.20
$0.118
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

Grok 4.20
$0.5
Fits in one request
Cached input priced at the published list-input rate
Sakana Fugu-Ultra
API rate not published
Fits in one request
Cached-input rate unavailable

Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate. 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.

Grok 4.20

2M

Sakana Fugu-Ultra

1M

API model ID

Grok 4.20

Not sourced

Sakana Fugu-Ultra

Not sourced

Cached-input rate

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

Grok 4.20

Not published

Sakana Fugu-Ultra

No comparable hosted API rate

Documented inputs

Grok 4.20

Not sourced

Sakana Fugu-Ultra

Not sourced

Documented outputs

Grok 4.20

Not sourced

Sakana Fugu-Ultra

Not sourced

Provider availability

Grok 4.20

Not sourced

Sakana Fugu-Ultra

Not sourced

Reasoning profile

Grok 4.20

Reasoning

Sakana Fugu-Ultra

Reasoning

Weight access

Grok 4.20

Proprietary

Sakana Fugu-Ultra

Proprietary

License

Grok 4.20

Proprietary

Sakana Fugu-Ultra

Proprietary

Release date

Grok 4.20

2026-03-10

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
Grok 4.20 has the larger documented window (2M).

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 evidence23 rows

Agentic

  • Terminal-Bench 2.0

    Grok 4.2047.1%
    Source
    Sakana Fugu-Ultra82.1%
    Source

    Sakana Fugu-Ultra leads this result

  • DeepSearchQA

    Grok 4.2062.8%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Gert Labs

    Grok 4.2038.36%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

Coding

  • LiveCodeBench Pro

    Grok 4.2074.2%
    Source
    Sakana Fugu-Ultra90.8%
    Source

    Sakana Fugu-Ultra leads this result

  • SWE-bench Verified

    Grok 4.2076.7%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • SWE-bench Pro

    Grok 4.2051.8%
    Source
    Sakana Fugu-Ultra73.7%
    Source

    Sakana Fugu-Ultra leads this result

  • Vibe Code Bench

    Grok 4.204.06%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Terminal-Bench 2.0

    Grok 4.20—
    Sakana Fugu-Ultra82.1%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Grok 4.20—
    Sakana Fugu-Ultra93.2%
    Source

    Not directly comparable

  • SciCode

    Grok 4.20—
    Sakana Fugu-Ultra58.7%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Grok 4.2053.3%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • ARC-AGI-3

    Grok 4.200.1%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • MRCRv2

    Grok 4.20—
    Sakana Fugu-Ultra93.6%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Grok 4.2088.5%
    Source
    Sakana Fugu-Ultra95.5%
    Source

    Sakana Fugu-Ultra leads this result

  • HLE w/o tools

    Grok 4.2031.6%
    Source
    Sakana Fugu-Ultra50%
    Source

    Sakana Fugu-Ultra leads this result

  • HealthBench Hard

    Grok 4.2020.3%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • MedXpertQA (Text)

    Grok 4.2050.2%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • GPQA

    Grok 4.20—
    Sakana Fugu-Ultra95.5%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Grok 4.2075.2%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • CharXiv

    Grok 4.2060.9%
    Source
    Sakana Fugu-Ultra86.6%
    Source

    Sakana Fugu-Ultra leads this result

  • ERQA

    Grok 4.2054.1%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • SimpleVQA

    Grok 4.2057.4%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • MedXpertQA (MM)

    Grok 4.2065.8%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

Frequently asked questions

Which is better, Grok 4.20 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, Grok 4.20 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, Grok 4.20 or Sakana Fugu-Ultra?

Sakana Fugu-Ultra leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.

Which costs less, Grok 4.20 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, Grok 4.20 or Sakana Fugu-Ultra?

Grok 4.20 has the larger documented context window: 2M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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