Ling 3.0 Flash Sante vs Step 3.7 Flash

Updated October 20, 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

Ling 3.0 Flash Sante

InclusionAI

—

Evidence status unavailable

90% interval unavailable

Model B

Step 3.7 Flash

StepFun

43.61/100

Estimated · Public rank #124

Conditional range 29.3–58.0

Shared results: 3
Ling 3.0 Flash Sante only: 3
Step 3.7 Flash only: 8
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.

Long documents
Prompts that approach the documented context limit
Ling 3.0 Flash Sante
Ling 3.0 Flash Sante 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
Ling 3.0 Flash Sante 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
Ling 3.0 Flash Sante 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.

—Ling 3.0 Flash Sante30.3Step 3.7 Flash

Not comparable · BenchAlign v5.8

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.

BrowseCompAgentic
Normalized gap 4.4
Ling 3.0 Flash Sante:80.2%
Step 3.7 Flash:75.8%
Ling 3.0 Flash Sante 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 v5.8 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.

Agentic

Not comparable

Ling 3.0 Flash Sante: Not ranked
Step 3.7 Flash: 37.0; Estimated · #79/132
Basis: BenchAlign v5.8 lane · 4 vs 7 public rows
Reading: Not comparable

Coding

Not comparable

Ling 3.0 Flash Sante: Not ranked
Step 3.7 Flash: 30.3; Estimated · #99/153
Basis: BenchAlign v5.8 lane · 0 vs 2 public rows
Reading: Not comparable

Reasoning

Not comparable

Ling 3.0 Flash Sante: 73.4; Unranked · 1 rankable row
Step 3.7 Flash: 75.6; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Ling 3.0 Flash Sante: 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

Ling 3.0 Flash Sante: Not ranked
Step 3.7 Flash: 45.0; Estimated · #99/187
Basis: BenchAlign v5.8 lane · 2 vs 0 public rows
Reading: Not comparable

Multilingual

Not comparable

Ling 3.0 Flash Sante: Not ranked
Step 3.7 Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Ling 3.0 Flash Sante: Not ranked
Step 3.7 Flash: 82.1; #58/152
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Ling 3.0 Flash Sante: 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 Ling 3.0 Flash Sante and Step 3.7 Flash
Category	Ling 3.0 Flash Sante	Step 3.7 Flash	Basis	Reading
Agentic	Not ranked	37.0Estimated · #79/132	Not comparableBenchAlign v5.8 lane · 4 vs 7 public rows	Not comparable
Coding	Not ranked	30.3Estimated · #99/153	Not comparableBenchAlign v5.8 lane · 0 vs 2 public rows	Not comparable
Reasoning	73.4Unranked · 1 rankable row	75.6Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	75.4Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Knowledge	Not ranked	45.0Estimated · #99/187	Not comparableBenchAlign v5.8 lane · 2 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	82.1#58/152	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 v5.8) 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.

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

Ling 3.0 Flash Sante: Self-hosted; infrastructure cost varies; Fits in one request
Step 3.7 Flash: $0.00077; Fits in one request

Ling 3.0 Flash Sante has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Ling 3.0 Flash Sante: Self-hosted; infrastructure cost varies; Fits in one request
Step 3.7 Flash: $0.01345; Fits in one request

Ling 3.0 Flash Sante has no comparable published API token rate.

Cache-heavy agent loop

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

Ling 3.0 Flash Sante: 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. Ling 3.0 Flash Sante 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.

SpecificationLing 3.0 Flash SanteStep 3.7 Flash

Context window

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

Ling 3.0 Flash Sante

262K

InclusionAI Ling-3.0-flash-Sante model card

Step 3.7 Flash

256K

API model ID

Ling 3.0 Flash Sante

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.

Ling 3.0 Flash Sante

No comparable hosted API rate

InclusionAI Ling-3.0-flash-Sante model card

Step 3.7 Flash

Not published

Documented inputs

Ling 3.0 Flash Sante

Not sourced

Step 3.7 Flash

Not sourced

Documented outputs

Ling 3.0 Flash Sante

Not sourced

Step 3.7 Flash

Not sourced

Provider availability

Ling 3.0 Flash Sante

Not sourced

Step 3.7 Flash

Not sourced

Reasoning profile

Ling 3.0 Flash Sante

Reasoning

Step 3.7 Flash

Reasoning

Weight access

Ling 3.0 Flash Sante

Open Weight

Step 3.7 Flash

Open Weight

License

Ling 3.0 Flash Sante

Open Weight

Step 3.7 Flash

Open Weight

Release date

Ling 3.0 Flash Sante

2026-10-19

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: Ling 3.0 Flash Sante has the larger documented window (262K).

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

Questions

Which is better, Ling 3.0 Flash Sante 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, Ling 3.0 Flash Sante or Step 3.7 Flash?

Ling 3.0 Flash Sante is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Ling 3.0 Flash Sante or Step 3.7 Flash?

Ling 3.0 Flash Sante is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Ling 3.0 Flash Sante 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, Ling 3.0 Flash Sante or Step 3.7 Flash?

Ling 3.0 Flash Sante has the larger documented context window: 262K, compared with 256K.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Ling 3.0 Flash Sante

API / mo$0

Self-host / mo$3,495

Break-even—

Step 3.7 Flash

API / mo$1,012

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence14 rows

Agentic

BrowseComp
Ling 3.0 Flash Sante80.2%
Source
Step 3.7 Flash75.8%
Source
Ling 3.0 Flash Sante leads this result
DeepSearchQA
Ling 3.0 Flash Sante90.8%
Source
Step 3.7 Flash92.8%
Source
Step 3.7 Flash leads this result
HLE w/ tools
Ling 3.0 Flash Sante57.1%
Source
Step 3.7 Flash47.2%
Source
Ling 3.0 Flash Sante leads this result
MCP Atlas
Ling 3.0 Flash Sante75.6%
Source
Step 3.7 Flash—
Not directly comparable
Terminal-Bench 2.1
Ling 3.0 Flash Sante—
Step 3.7 Flash59.5%
Source
Not directly comparable
Toolathlon
Ling 3.0 Flash Sante—
Step 3.7 Flash49.5%
Source
Not directly comparable
Claw-Eval
Ling 3.0 Flash Sante—
Step 3.7 Flash67.1%
Source
Not directly comparable
Gert Labs
Ling 3.0 Flash Sante—
Step 3.7 Flash51.57%
Source
Not directly comparable

Coding

SWE-bench Pro
Ling 3.0 Flash Sante—
Step 3.7 Flash56.3%
Source
Not directly comparable
Terminal-Bench 2.1
Ling 3.0 Flash Sante—
Step 3.7 Flash59.5%
Source
Not directly comparable

Multimodal

SimpleVQA
Ling 3.0 Flash Sante—
Step 3.7 Flash79.2%
Source
Not directly comparable
V*
Ling 3.0 Flash Sante—
Step 3.7 Flash95.3%
Source
Not directly comparable

Knowledge

HealthBench Professional
Ling 3.0 Flash Sante54.3%
Source
Step 3.7 Flash—
Not directly comparable
MedXpertQA (Text)
Ling 3.0 Flash Sante51.0%
Source
Step 3.7 Flash—
Not directly comparable

14 public results · 3 shared

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Last updated October 20, 2026

Ling 3.0 Flash Sante 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, Ling 3.0 Flash Sante or Step 3.7 Flash?

Which is better for coding, Ling 3.0 Flash Sante or Step 3.7 Flash?

Which is better for agentic tasks, Ling 3.0 Flash Sante or Step 3.7 Flash?

Which costs less, Ling 3.0 Flash Sante or Step 3.7 Flash?

Which has the larger context window, Ling 3.0 Flash Sante or Step 3.7 Flash?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Multimodal

Knowledge

Related comparisons

Watch Ling 3.0 Flash Sante vs Step 3.7 Flash