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BenchLM
Model A
Ling 3.0 Flash FP8

InclusionAI

—

Evidence status unavailable

90% interval unavailable

Ling 3.0 Flash FP8 vs Qwen3.8-27B

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Qwen3.8-27B

Alibaba

—

Evidence status unavailable

90% interval unavailable

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 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
3
Ling 3.0 Flash FP8 only
1
Qwen3.8-27B only
24
Like-for-like categories
1 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Instruction following

Like-for-like
Ling 3.0 Flash FP8
73.4
Qwen3.8-27B
79.5
Weighted basis
1 vs 1 rows
Reading
Qwen3.8-27B leads

Knowledge

Directional only
Ling 3.0 Flash FP8
84.0
Qwen3.8-27B
38.7
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Ling 3.0 Flash FP8
Not measured
Qwen3.8-27B
84.3
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Ling 3.0 Flash FP8
40.4
Qwen3.8-27B
61.7
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Ling 3.0 Flash FP8
Not measured
Qwen3.8-27B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Ling 3.0 Flash FP8
Not measured
Qwen3.8-27B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Ling 3.0 Flash FP8
Not measured
Qwen3.8-27B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Ling 3.0 Flash FP8
Not measured
Qwen3.8-27B
90.2
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.

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.

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 FP8
API rate not published
Fits in one request
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request

Ling 3.0 Flash FP8 has no comparable published API token rate. Qwen3.8-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Ling 3.0 Flash FP8
API rate not published
Fits in one request
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request

Ling 3.0 Flash FP8 has no comparable published API token rate. Qwen3.8-27B has no comparable published API token rate.

Cache-heavy agent loop

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

Ling 3.0 Flash FP8
API rate not published
Fits in one request
Cached-input rate unavailable
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Ling 3.0 Flash FP8 has no comparable published API token rate. Qwen3.8-27B 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

Ling 3.0 Flash FP8

Not sourced

Qwen3.8-27B

Not sourced

Documented inputs

Ling 3.0 Flash FP8

Not sourced

Qwen3.8-27B

Not sourced

Documented outputs

Ling 3.0 Flash FP8

Not sourced

Qwen3.8-27B

Not sourced

Provider availability

Ling 3.0 Flash FP8

Not sourced

Qwen3.8-27B

Not sourced

Reasoning profile

Ling 3.0 Flash FP8

Reasoning

Qwen3.8-27B

Reasoning

Weight access

Ling 3.0 Flash FP8

Open Weight

Qwen3.8-27B

Open Weight

License

Ling 3.0 Flash FP8

Open Weight

Qwen3.8-27B

Open Weight

Release date

Ling 3.0 Flash FP8

2026-08-04

Qwen3.8-27B

2026-08-05

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 evidence28 rows

Agentic

  • Terminal-Bench 2.1

    Ling 3.0 Flash FP8—
    Qwen3.8-27B73.0%
    Source

    Not directly comparable

  • CoWorkBench

    Ling 3.0 Flash FP8—
    Qwen3.8-27B70.7%
    Source

    Not directly comparable

  • JobBench

    Ling 3.0 Flash FP8—
    Qwen3.8-27B33.4%
    Source

    Not directly comparable

  • Agents' Last Exam

    Ling 3.0 Flash FP8—
    Qwen3.8-27B42.9%
    Source

    Not directly comparable

  • OSWorld-Verified

    Ling 3.0 Flash FP8—
    Qwen3.8-27B84.3%
    Source

    Not directly comparable

  • WebArena-Verified

    Ling 3.0 Flash FP8—
    Qwen3.8-27B64.8%
    Source

    Not directly comparable

  • AndroidWorld

    Ling 3.0 Flash FP8—
    Qwen3.8-27B81.9%
    Source

    Not directly comparable

Coding

  • SciCode

    Ling 3.0 Flash FP840.4%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • Terminal-Bench 2.1

    Ling 3.0 Flash FP8—
    Qwen3.8-27B73.0%
    Source

    Not directly comparable

  • SWE-bench Pro

    Ling 3.0 Flash FP8—
    Qwen3.8-27B61.7%
    Source

    Not directly comparable

  • NL2Repo

    Ling 3.0 Flash FP8—
    Qwen3.8-27B42.3%
    Source

    Not directly comparable

  • deepSwe

    Ling 3.0 Flash FP8—
    Qwen3.8-27B42.2%
    Source

    Not directly comparable

  • LiveCodeBench v6

    Ling 3.0 Flash FP8—
    Qwen3.8-27B90.3%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Ling 3.0 Flash FP884%
    Source
    Qwen3.8-27B89.2%
    Source

    Qwen3.8-27B leads this result

  • GPQA-D

    Ling 3.0 Flash FP884.0%
    Source
    Qwen3.8-27B89.2%
    Source

    Qwen3.8-27B leads this result

  • HLE

    Ling 3.0 Flash FP8—
    Qwen3.8-27B30.8%
    Source

    Not directly comparable

  • HLE w/o tools

    Ling 3.0 Flash FP8—
    Qwen3.8-27B30.8%
    Source

    Not directly comparable

Multimodal

  • MathVision

    Ling 3.0 Flash FP8—
    Qwen3.8-27B90.0%
    Source

    Not directly comparable

  • MathVision w/ Python

    Ling 3.0 Flash FP8—
    Qwen3.8-27B94.6%
    Source

    Not directly comparable

  • BabyVision

    Ling 3.0 Flash FP8—
    Qwen3.8-27B65.7%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Ling 3.0 Flash FP8—
    Qwen3.8-27B85.6%
    Source

    Not directly comparable

  • Vision2Web

    Ling 3.0 Flash FP8—
    Qwen3.8-27B62.9%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Ling 3.0 Flash FP8—
    Qwen3.8-27B83.7%
    Source

    Not directly comparable

  • CharXiv

    Ling 3.0 Flash FP8—
    Qwen3.8-27B90.2%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    Ling 3.0 Flash FP8—
    Qwen3.8-27B91.1%
    Source

    Not directly comparable

  • RealWorldQA

    Ling 3.0 Flash FP8—
    Qwen3.8-27B85.9%
    Source

    Not directly comparable

  • ERQA

    Ling 3.0 Flash FP8—
    Qwen3.8-27B65.5%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Ling 3.0 Flash FP873.4%
    Source
    Qwen3.8-27B79.5%
    Source

    Qwen3.8-27B leads this result

Frequently asked questions

Which is better, Ling 3.0 Flash FP8 or Qwen3.8-27B?

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 FP8 or Qwen3.8-27B?

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, Ling 3.0 Flash FP8 or Qwen3.8-27B?

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, Ling 3.0 Flash FP8 or Qwen3.8-27B?

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 FP8 or Qwen3.8-27B?

Both models list the same context window, 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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