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Ling 3.0 Flash Sante vs Beam 1.0 501B-A23B

Updated October 20, 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. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
InclusionAI logo
Ling 3.0 Flash Sante

InclusionAI

—

Evidence status unavailable

90% interval unavailable

Model B
Beam 1.0 501B-A23B

Reflection AI

51.55/100

Estimated · Public rank #91

Conditional range 37.2–65.9

Shared results
4
Ling 3.0 Flash Sante only
2
Beam 1.0 501B-A23B only
15
Like-for-like categories
0 / 8
Estimated: Beam 1.0 501B-A23B. 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

    Beam 1.0 501B-A23B

    Beam 1.0 501B-A23B 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 and Beam 1.0 501B-A23B are 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: listed-rates

  • 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.

—Ling 3.0 Flash Sante—Beam 1.0 501B-A23B

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 leaderboardShortlist models for this job
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.

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.

Agentic

Not comparable
Ling 3.0 Flash Sante
Not ranked
Beam 1.0 501B-A23B
49.9
Supported · #50/132
Basis
BenchAlign v5.8 lane · 4 vs 6 public rows
Reading
Not comparable

Coding

Not comparable
Ling 3.0 Flash Sante
Not ranked
Beam 1.0 501B-A23B
Not ranked
Basis
BenchAlign v5.8 lane · 0 vs 7 public rows
Reading
Not comparable

Reasoning

Not comparable
Ling 3.0 Flash Sante
73.4
Unranked · 1 rankable row
Beam 1.0 501B-A23B
78.7
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Ling 3.0 Flash Sante
Not ranked
Beam 1.0 501B-A23B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Ling 3.0 Flash Sante
Not ranked
Beam 1.0 501B-A23B
55.5
Supported · #58/187
Basis
BenchAlign v5.8 lane · 2 vs 2 public rows
Reading
Not comparable

Multilingual

Not comparable
Ling 3.0 Flash Sante
Not ranked
Beam 1.0 501B-A23B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Ling 3.0 Flash Sante
Not ranked
Beam 1.0 501B-A23B
Not ranked
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Ling 3.0 Flash Sante
Not ranked
Beam 1.0 501B-A23B
Not ranked
Basis
Provisional lane · 0 vs 1 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

Ling 3.0 Flash Sante
Self-hosted; infrastructure cost varies
Fits in one request
Beam 1.0 501B-A23B
API rate not published
Fits in one request

Ling 3.0 Flash Sante has no comparable published API token rate. Beam 1.0 501B-A23B 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
Beam 1.0 501B-A23B
API rate not published
Fits in one request

Ling 3.0 Flash Sante has no comparable published API token rate. Beam 1.0 501B-A23B 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
Beam 1.0 501B-A23B
API rate not published
Fits in one request
Cached-input rate unavailable

Ling 3.0 Flash Sante has no comparable published API token rate. Beam 1.0 501B-A23B 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.

Provider availability

Ling 3.0 Flash Sante

Not sourced

Beam 1.0 501B-A23B

Open Weights · Hugging Face, self-hosted inference, Reflection API public beta, partner endpoints

Reflection Beam 1.0 BF16 model card

Reasoning profile

Ling 3.0 Flash Sante

Reasoning

Beam 1.0 501B-A23B

Reasoning

Weight access

Ling 3.0 Flash Sante

Open Weight

Beam 1.0 501B-A23B

Open Weight

License

Ling 3.0 Flash Sante

Open Weight

Beam 1.0 501B-A23B

Open Weight

Release date

Ling 3.0 Flash Sante

2026-10-19

Beam 1.0 501B-A23B

2026-10-16

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
Beam 1.0 501B-A23B has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Ling 3.0 Flash Sante or Beam 1.0 501B-A23B?

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 Beam 1.0 501B-A23B?

Ling 3.0 Flash Sante and Beam 1.0 501B-A23B are 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 Beam 1.0 501B-A23B?

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 Beam 1.0 501B-A23B?

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 Beam 1.0 501B-A23B?

Beam 1.0 501B-A23B has the larger documented context window: 1M, compared with 262K.

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—
Beam 1.0 501B-A23B
API / mo$0
Self-host / mo$27,962
Break-even—
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 evidence21 rows

Agentic

  • BrowseComp

    Ling 3.0 Flash Sante80.2%
    Source
    Beam 1.0 501B-A23B76.8%
    Source

    Ling 3.0 Flash Sante leads this result

  • DeepSearchQA

    Ling 3.0 Flash Sante90.8%
    Source
    Beam 1.0 501B-A23B80.5%
    Source

    Ling 3.0 Flash Sante leads this result

  • HLE w/ tools

    Ling 3.0 Flash Sante57.1%
    Source
    Beam 1.0 501B-A23B51.1%
    Source

    Ling 3.0 Flash Sante leads this result

  • MCP Atlas

    Ling 3.0 Flash Sante75.6%
    Source
    Beam 1.0 501B-A23B80.8%
    Source

    Beam 1.0 501B-A23B leads this result

  • τ³-bench results

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B36.1%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B80.1%
    Source

    Not directly comparable

Coding

  • DeepSWE

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B43.3%
    Source

    Not directly comparable

  • SWE-bench Pro

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B66.1%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B80.1%
    Source

    Not directly comparable

  • SWE-Atlas Codebase QnA

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B31.8%
    Source

    Not directly comparable

  • SWE Multilingual

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B78.3%
    Source

    Not directly comparable

  • SWE-bench Verified

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B81.6%
    Source

    Not directly comparable

  • SciCode

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B49.2%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B16.3%
    Source

    Not directly comparable

  • LongBench v2

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B67.5%
    Source

    Not directly comparable

Knowledge

  • HealthBench Professional

    Ling 3.0 Flash Sante54.3%
    Source
    Beam 1.0 501B-A23B—

    Not directly comparable

  • MedXpertQA (Text)

    Ling 3.0 Flash Sante51.0%
    Source
    Beam 1.0 501B-A23B—

    Not directly comparable

  • GPQA-D

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B90.6%
    Source

    Not directly comparable

  • HLE w/o tools

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B36%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B79.6%
    Source

    Not directly comparable

Math

  • AIME26

    Ling 3.0 Flash Sante—
    Beam 1.0 501B-A23B97.7%
    Source

    Not directly comparable

21 public results · 4 shared

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Last updated October 20, 2026