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BenchLM
Data

Laguna XS.2 vs Step 3.7 Flash

Updated October 14, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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

Model A
Poolside logo

Poolside

—

Evidence status unavailable

90% interval unavailable

Model B
StepFun logo

StepFun

43.74/100

Estimated · Public rank #119

Conditional range 29.4–58.1

Shared results
1
Laguna XS.2 only
9
Step 3.7 Flash only
10
Like-for-like categories
0 / 8
Estimated: Step 3.7 Flash. Conditional ranges do not establish rank confidence.How 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.

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

    Step 3.7 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited
  • Agentic work

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

    Not enough matched evidence

    Laguna XS.2 is not ranked on the public lane for agentic, so no winner is named for agentic.

    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

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.

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

21.7Laguna XS.230.2Step 3.7 Flash

Directional only · BenchAlign v5.8

Step 3.7 Flash has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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

Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

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 v5.8 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.

Coding

Directional only
Laguna XS.2
21.7
Supported · #116/149
Step 3.7 Flash
30.2
Estimated · #95/149
Basis
BenchAlign v5.8 lane · 6 vs 2 public rows
Reading
Directional only

Agentic

Not comparable
Laguna XS.2
Not ranked
Step 3.7 Flash
37.3
Estimated · #73/125
Basis
BenchAlign v5.8 lane · 2 vs 7 public rows
Reading
Not comparable

Reasoning

Not comparable
Laguna XS.2
Not ranked
Step 3.7 Flash
76.5
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Laguna XS.2
Not ranked
Step 3.7 Flash
75.4
Unranked · 3 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Laguna XS.2
Not ranked
Step 3.7 Flash
45.0
Estimated · #95/181
Basis
BenchAlign v5.8 lane · 2 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Laguna XS.2
Not ranked
Step 3.7 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Laguna XS.2
Not ranked
Step 3.7 Flash
82.1
#54/144
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Laguna XS.2
Not ranked
Step 3.7 Flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) 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.

Supported evidence per lane · 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

Laguna XS.2
Self-hosted; infrastructure cost varies
Fits in one request
Step 3.7 Flash
$0.00077
Fits in one request

Laguna XS.2 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Laguna XS.2
Self-hosted; infrastructure cost varies
Fits in one request
Step 3.7 Flash
$0.01345
Fits in one request

Laguna XS.2 has no comparable published API token rate.

Cache-heavy agent loop

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

Laguna XS.2
Self-hosted; infrastructure cost varies
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. Laguna XS.2 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.

Context window

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

Laguna XS.2

256K

Step 3.7 Flash

256K

API model ID

Laguna XS.2

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.

Laguna XS.2

No comparable hosted API rate

Step 3.7 Flash

Not published

Documented inputs

Laguna XS.2

Not sourced

Step 3.7 Flash

Not sourced

Documented outputs

Laguna XS.2

Not sourced

Step 3.7 Flash

Not sourced

Provider availability

Laguna XS.2

Not sourced

Step 3.7 Flash

Not sourced

Reasoning profile

Laguna XS.2

Reasoning

Step 3.7 Flash

Reasoning

Weight access

Laguna XS.2

Open Weight

Step 3.7 Flash

Open Weight

License

Laguna XS.2

Open Weight

Step 3.7 Flash

Open Weight

Release date

Laguna XS.2

2026-04-28

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
Both models list 256K.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Laguna XS.2 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, Laguna XS.2 or Step 3.7 Flash?

Step 3.7 Flash scores higher for coding on the public lane, 30.2 to 21.7. Step 3.7 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Laguna XS.2 or Step 3.7 Flash?

Laguna XS.2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Laguna XS.2 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, Laguna XS.2 or Step 3.7 Flash?

Both models list the same context window, 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 evidence20 rows

Agentic

  • Terminal-Bench 2.0

    Laguna XS.235.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Laguna XS.225.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Terminal-Bench 2.1

    Laguna XS.2—
    Step 3.7 Flash59.5%
    Source

    Not directly comparable

  • BrowseComp

    Laguna XS.2—
    Step 3.7 Flash75.8%
    Source

    Not directly comparable

  • DeepSearchQA

    Laguna XS.2—
    Step 3.7 Flash92.8%
    Source

    Not directly comparable

  • Toolathlon

    Laguna XS.2—
    Step 3.7 Flash49.5%
    Source

    Not directly comparable

  • Claw-Eval

    Laguna XS.2—
    Step 3.7 Flash67.1%
    Source

    Not directly comparable

  • HLE w/ tools

    Laguna XS.2—
    Step 3.7 Flash47.2%
    Source

    Not directly comparable

  • Gert Labs

    Laguna XS.2—
    Step 3.7 Flash51.57%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Laguna XS.269.9%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SWE Multilingual

    Laguna XS.257.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SWE-bench Pro

    Laguna XS.246.3%
    Source
    Step 3.7 Flash56.3%
    Source

    Step 3.7 Flash leads this result

  • Terminal-Bench 2.0

    Laguna XS.235.7%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • LiveCodeBench (Vals)

    Laguna XS.267.8%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • SWE-bench (Vals)

    Laguna XS.255.2%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • Terminal-Bench 2.1

    Laguna XS.2—
    Step 3.7 Flash59.5%
    Source

    Not directly comparable

Multimodal

  • SimpleVQA

    Laguna XS.2—
    Step 3.7 Flash79.2%
    Source

    Not directly comparable

  • V*

    Laguna XS.2—
    Step 3.7 Flash95.3%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Laguna XS.255.1%
    Source
    Step 3.7 Flash—

    Not directly comparable

  • MMLU-Pro (Vals)

    Laguna XS.269.1%
    Source
    Step 3.7 Flash—

    Not directly comparable

20 public results · 1 shared

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Last updated October 14, 2026