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Model comparison

BTL-4 vs Ling 3.0 Flash

Updated August 11, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

BTL-4

Bad Theory Labs

—

Evidence status unavailable

90% interval unavailable

Ling 3.0 Flash

InclusionAI

53.1/100

Estimated · Public rank #101

90% interval 41.5–64.6

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 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.

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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    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: 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

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
1
BTL-4 only
2
Ling 3.0 Flash only
16
Like-for-like categories
0 / 8

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

Not comparable
BTL-4
Not measured
Ling 3.0 Flash
72.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
BTL-4
78.4
Ling 3.0 Flash
47.1
Weighted basis
1 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
BTL-4
Not measured
Ling 3.0 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
BTL-4
Not measured
Ling 3.0 Flash
31.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
BTL-4
Not measured
Ling 3.0 Flash
90.1
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
BTL-4
Not measured
Ling 3.0 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
BTL-4
Not measured
Ling 3.0 Flash
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
BTL-4
Not measured
Ling 3.0 Flash
74.5
Weighted basis
0 vs 1 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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

BTL-4
Self-hosted; infrastructure cost varies
Fits in one request
Ling 3.0 Flash
API rate not published
Fits in one request

BTL-4 has no comparable published API token rate. Ling 3.0 Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

BTL-4
Self-hosted; infrastructure cost varies
Fits in one request
Ling 3.0 Flash
API rate not published
Fits in one request

BTL-4 has no comparable published API token rate. Ling 3.0 Flash has no comparable published API token rate.

Cache-heavy agent loop

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

BTL-4
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Ling 3.0 Flash
API rate not published
Fits in one request
Cached-input rate unavailable

BTL-4 has no comparable published API token rate. Ling 3.0 Flash 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.

API model ID

BTL-4

Not sourced

Ling 3.0 Flash

Not sourced

Documented inputs

BTL-4

Not sourced

Ling 3.0 Flash

Not sourced

Documented outputs

BTL-4

Not sourced

Ling 3.0 Flash

Not sourced

Provider availability

BTL-4

Not sourced

Ling 3.0 Flash

Not sourced

Reasoning profile

BTL-4

Reasoning

Ling 3.0 Flash

Reasoning

Weight access

BTL-4

Open Weight

Ling 3.0 Flash

Open Weight

License

BTL-4

Open Weight

Ling 3.0 Flash

Open Weight

Release date

BTL-4

2026-08-05

Ling 3.0 Flash

2026-07-23

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 262K.

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 evidence19 rows

Agentic

  • BFCL v4

    BTL-473.5%
    Source
    Ling 3.0 Flash73.0%
    Source

    BTL-4 leads this result

  • MCP Atlas

    BTL-4—
    Ling 3.0 Flash65.5%
    Source

    Not directly comparable

  • skillsBench

    BTL-4—
    Ling 3.0 Flash44.8%
    Source

    Not directly comparable

  • WideResearch

    BTL-4—
    Ling 3.0 Flash73.6%
    Source

    Not directly comparable

  • BrowseComp

    BTL-4—
    Ling 3.0 Flash72.2%
    Source

    Not directly comparable

  • DRACO

    BTL-4—
    Ling 3.0 Flash70.4%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    BTL-478.4%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • LiveCodeBench v6

    BTL-466.1%
    Source
    Ling 3.0 Flash—

    Not directly comparable

  • SWE-bench Pro

    BTL-4—
    Ling 3.0 Flash56.6%
    Source

    Not directly comparable

  • SWE Multilingual

    BTL-4—
    Ling 3.0 Flash72.4%
    Source

    Not directly comparable

  • LiveCodeBench v5

    BTL-4—
    Ling 3.0 Flash82.8%
    Source

    Not directly comparable

  • SciCode

    BTL-4—
    Ling 3.0 Flash41.2%
    Source

    Not directly comparable

Knowledge

  • GPQA

    BTL-4—
    Ling 3.0 Flash85.0%
    Source

    Not directly comparable

  • GPQA-D

    BTL-4—
    Ling 3.0 Flash85.0%
    Source

    Not directly comparable

  • HLE

    BTL-4—
    Ling 3.0 Flash22.7%
    Source

    Not directly comparable

Math

  • AIME26

    BTL-4—
    Ling 3.0 Flash93.2%
    Source

    Not directly comparable

  • HMMT Feb 2026

    BTL-4—
    Ling 3.0 Flash87.0%
    Source

    Not directly comparable

  • IMOAnswerBench

    BTL-4—
    Ling 3.0 Flash83.7%
    Source

    Not directly comparable

Instruction following

  • IFBench

    BTL-4—
    Ling 3.0 Flash74.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, BTL-4 or Ling 3.0 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, BTL-4 or Ling 3.0 Flash?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, BTL-4 or Ling 3.0 Flash?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, BTL-4 or Ling 3.0 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, BTL-4 or Ling 3.0 Flash?

Both models list the same context window, 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 11, 2026

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