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BenchLM
Model A
GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #43

90% interval 47.9–77.9

GLM-5.2 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

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

  • 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: 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
GLM-5.2 only
14
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
GLM-5.2
81.0
Sakana Fugu-Ultra
82.1
Weighted basis
1 vs 1 rows
Reading
Sakana Fugu-Ultra leads

Coding

Directional only
GLM-5.2
62.1
Sakana Fugu-Ultra
64.5
Weighted basis
1 vs 2 rows
Reading
Directional only

Knowledge

Directional only
GLM-5.2
59.6
Sakana Fugu-Ultra
95.5
Weighted basis
2 vs 1 rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.2
Not measured
Sakana Fugu-Ultra
93.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
GLM-5.2
95.9
Sakana Fugu-Ultra
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.2
Not measured
Sakana Fugu-Ultra
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.2
Not measured
Sakana Fugu-Ultra
86.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.2
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

GLM-5.2
$0.0036
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

GLM-5.2
$0.0832
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

GLM-5.2
$0.352
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

GLM-5.2 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.

GLM-5.2

1M

Sakana Fugu-Ultra

1M

API model ID

GLM-5.2

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.

GLM-5.2

Not published

Sakana Fugu-Ultra

No comparable hosted API rate

Documented inputs

GLM-5.2

Not sourced

Sakana Fugu-Ultra

Not sourced

Documented outputs

GLM-5.2

Not sourced

Sakana Fugu-Ultra

Not sourced

Provider availability

GLM-5.2

Not sourced

Sakana Fugu-Ultra

Not sourced

Reasoning profile

GLM-5.2

Reasoning

Sakana Fugu-Ultra

Reasoning

Weight access

GLM-5.2

Open Weight

Sakana Fugu-Ultra

Proprietary

License

GLM-5.2

Open Weight

Sakana Fugu-Ultra

Proprietary

Release date

GLM-5.2

2026-06-16

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 evidence25 rows

Agentic

  • Terminal-Bench 3.0

    GLM-5.24.6%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.281%
    Source
    Sakana Fugu-Ultra82.1%
    Source

    Sakana Fugu-Ultra leads this result

  • MCP Atlas

    GLM-5.276.8%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Toolathlon

    GLM-5.248.2%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • ResearchClawBench

    GLM-5.220.7%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

Coding

  • SWE-bench Pro

    GLM-5.262.1%
    Source
    Sakana Fugu-Ultra73.7%
    Source

    Sakana Fugu-Ultra leads this result

  • NL2Repo

    GLM-5.248.9%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.281.0%
    Source
    Sakana Fugu-Ultra82.1%
    Source

    Sakana Fugu-Ultra leads this result

  • ProgramBench

    GLM-5.263.7%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • cursorBench32

    GLM-5.255.0%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • EEBench

    GLM-5.216.7%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • LiveCodeBench v6

    GLM-5.2—
    Sakana Fugu-Ultra93.2%
    Source

    Not directly comparable

  • LiveCodeBench Pro

    GLM-5.2—
    Sakana Fugu-Ultra90.8%
    Source

    Not directly comparable

  • SciCode

    GLM-5.2—
    Sakana Fugu-Ultra58.7%
    Source

    Not directly comparable

Reasoning

  • CritPt

    GLM-5.220.9%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • MRCRv2

    GLM-5.2—
    Sakana Fugu-Ultra93.6%
    Source

    Not directly comparable

Knowledge

  • GPQA

    GLM-5.291.2%
    Source
    Sakana Fugu-Ultra95.5%
    Source

    Sakana Fugu-Ultra leads this result

  • GPQA-D

    GLM-5.291.2%
    Source
    Sakana Fugu-Ultra95.5%
    Source

    Sakana Fugu-Ultra leads this result

  • HLE

    GLM-5.254.7%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • HLE w/o tools

    GLM-5.240.5%
    Source
    Sakana Fugu-Ultra50%
    Source

    Sakana Fugu-Ultra leads this result

Math

  • AIME26

    GLM-5.299.2%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • HMMT Nov 2025

    GLM-5.294.4%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • HMMT Feb 2026

    GLM-5.292.5%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

  • MMAnswerBench

    GLM-5.291.0%
    Source
    Sakana Fugu-Ultra—

    Not directly comparable

Multimodal

  • CharXiv

    GLM-5.2—
    Sakana Fugu-Ultra86.6%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GLM-5.2 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, GLM-5.2 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, GLM-5.2 or Sakana Fugu-Ultra?

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

Which costs less, GLM-5.2 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, GLM-5.2 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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