Sakana Fugu vs Step 3.7 Flash

Updated September 27, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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

Model A

Sakana Fugu

Sakana AI

—

Evidence status unavailable

90% interval unavailable

Model B

Step 3.7 Flash

StepFun

41.41/100

Estimated · Public rank #109

90% interval 29.9–52.9

Shared results: 3
Sakana Fugu only: 8
Step 3.7 Flash only: 8
Like-for-like categories: 0 / 8

Estimated: Step 3.7 FlashHow the comparison works

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
Sakana Fugu 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
Sakana Fugu is not ranked on the public lane for agentic, so no winner is named for agentic.
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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—Sakana Fugu32.4Step 3.7 Flash

Not comparable · BenchAlign v5.7

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

SWE-bench ProCoding
Normalized gap 2.7
Sakana Fugu:59%
Step 3.7 Flash:56.3%
Sakana Fugu source Step 3.7 Flash source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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

Sakana Fugu: Not ranked
Step 3.7 Flash: 35.2; Estimated · #56/105
Basis: BenchAlign v5.7 lane · 1 vs 7 public rows
Reading: Not comparable

Coding

Not comparable

Sakana Fugu: Not ranked
Step 3.7 Flash: 32.4; Estimated · #81/135
Basis: BenchAlign v5.7 lane · 5 vs 2 public rows
Reading: Not comparable

Reasoning

Not comparable

Sakana Fugu: 70.6; Unranked · 1 rankable row
Step 3.7 Flash: 72.7; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Sakana Fugu: 68.6; Unranked · 1 rankable row
Step 3.7 Flash: 71.0; Unranked · 3 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Knowledge

Not comparable

Sakana Fugu: Not ranked
Step 3.7 Flash: 43.1; Estimated · #78/158
Basis: BenchAlign v5.7 lane · 3 vs 0 public rows
Reading: Not comparable

Multilingual

Not comparable

Sakana Fugu: Not ranked
Step 3.7 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Sakana Fugu: Not ranked
Step 3.7 Flash: 80.6; #53/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Sakana Fugu: Not ranked
Step 3.7 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Sakana Fugu and Step 3.7 Flash
Category	Sakana Fugu	Step 3.7 Flash	Basis	Reading
Agentic	Not ranked	35.2Estimated · #56/105	Not comparableBenchAlign v5.7 lane · 1 vs 7 public rows	Not comparable
Coding	Not ranked	32.4Estimated · #81/135	Not comparableBenchAlign v5.7 lane · 5 vs 2 public rows	Not comparable
Reasoning	70.6Unranked · 1 rankable row	72.7Unranked · 2 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	68.6Unranked · 1 rankable row	71.0Unranked · 3 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Knowledge	Not ranked	43.1Estimated · #78/158	Not comparableBenchAlign v5.7 lane · 3 vs 0 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	80.6#53/124	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Bars run 0–100Methodology

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

Sakana Fugu: API rate not published; Fits in one request
Step 3.7 Flash: $0.00077; Fits in one request

Sakana Fugu has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Sakana Fugu: API rate not published; Fits in one request
Step 3.7 Flash: $0.01345; Fits in one request

Sakana Fugu has no comparable published API token rate.

Cache-heavy agent loop

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

Sakana Fugu: API rate not published; Fits in one request; Cached-input rate unavailable
Step 3.7 Flash: $0.0555; Fits in one request; Cached input priced at the published list-input rate

Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate. Sakana Fugu has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

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

SpecificationSakana FuguStep 3.7 Flash

Context window

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

Sakana Fugu

Step 3.7 Flash

256K

API model ID

Sakana Fugu

Not sourced

Step 3.7 Flash

Not sourced

Cached-input rate

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

Sakana Fugu

No comparable hosted API rate

Step 3.7 Flash

Not published

Documented inputs

Sakana Fugu

Not sourced

Step 3.7 Flash

Not sourced

Documented outputs

Sakana Fugu

Not sourced

Step 3.7 Flash

Not sourced

Provider availability

Sakana Fugu

Not sourced

Step 3.7 Flash

Not sourced

Reasoning profile

Sakana Fugu

Reasoning

Step 3.7 Flash

Reasoning

Weight access

Sakana Fugu

Proprietary

Step 3.7 Flash

Open Weight

License

Sakana Fugu

Proprietary

Step 3.7 Flash

Open Weight

Release date

Sakana Fugu

2026-06-22

Step 3.7 Flash

2026-05-29

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: Sakana Fugu has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Sakana Fugu or Step 3.7 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, Sakana Fugu or Step 3.7 Flash?

Sakana Fugu is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Sakana Fugu or Step 3.7 Flash?

Sakana Fugu is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Sakana Fugu or Step 3.7 Flash?

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, Sakana Fugu or Step 3.7 Flash?

Sakana Fugu has the larger documented context window: 1M, compared with 256K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence19 rows

Agentic

Terminal-Bench 2.1
Sakana Fugu80.2%
Source
Step 3.7 Flash59.5%
Source
Sakana Fugu leads this result
BrowseComp
Sakana Fugu—
Step 3.7 Flash75.8%
Source
Not directly comparable
DeepSearchQA
Sakana Fugu—
Step 3.7 Flash92.8%
Source
Not directly comparable
Toolathlon
Sakana Fugu—
Step 3.7 Flash49.5%
Source
Not directly comparable
Claw-Eval
Sakana Fugu—
Step 3.7 Flash67.1%
Source
Not directly comparable
HLE w/ tools
Sakana Fugu—
Step 3.7 Flash47.2%
Source
Not directly comparable
Gert Labs
Sakana Fugu—
Step 3.7 Flash51.57%
Source
Not directly comparable

Coding

SWE-bench Pro
Sakana Fugu59%
Source
Step 3.7 Flash56.3%
Source
Sakana Fugu leads this result
Terminal-Bench 2.1
Sakana Fugu80.2%
Source
Step 3.7 Flash59.5%
Source
Sakana Fugu leads this result
LiveCodeBench v6
Sakana Fugu92.9%
Source
Step 3.7 Flash—
Not directly comparable
LiveCodeBench Pro
Sakana Fugu87.8%
Source
Step 3.7 Flash—
Not directly comparable
SciCode
Sakana Fugu60.1%
Source
Step 3.7 Flash—
Not directly comparable

Reasoning

MRCRv2
Sakana Fugu86.6%
Source
Step 3.7 Flash—
Not directly comparable

Multimodal

CharXiv
Sakana Fugu85.1%
Source
Step 3.7 Flash—
Not directly comparable
SimpleVQA
Sakana Fugu—
Step 3.7 Flash79.2%
Source
Not directly comparable
V*
Sakana Fugu—
Step 3.7 Flash95.3%
Source
Not directly comparable

Knowledge

GPQA
Sakana Fugu95.5%
Source
Step 3.7 Flash—
Not directly comparable
GPQA-D
Sakana Fugu95.5%
Source
Step 3.7 Flash—
Not directly comparable
HLE w/o tools
Sakana Fugu47.2%
Source
Step 3.7 Flash—
Not directly comparable

19 public results · 3 shared

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Last updated September 27, 2026

Sakana Fugu vs Step 3.7 Flash

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Reasoning

Multimodal

Knowledge

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Sakana Fugu or Step 3.7 Flash?

Which is better for coding, Sakana Fugu or Step 3.7 Flash?

Which is better for agentic tasks, Sakana Fugu or Step 3.7 Flash?

Which costs less, Sakana Fugu or Step 3.7 Flash?

Which has the larger context window, Sakana Fugu or Step 3.7 Flash?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Related comparisons

Watch Sakana Fugu vs Step 3.7 Flash