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BenchLM
LG AI Research logo
Model A
K-EXAONE 2.0

LG AI Research

—

Evidence status unavailable

90% interval unavailable

K-EXAONE 2.0 vs Qwen3.8-27B

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Alibaba logo
Model B
Qwen3.8-27B

Alibaba

64.52/100

Supported · Public rank #41

90% interval 59.1–69.9

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.

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

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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

    K-EXAONE 2.0 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

    K-EXAONE 2.0 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: 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
5
K-EXAONE 2.0 only
10
Qwen3.8-27B only
28
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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
K-EXAONE 2.0
Not ranked
Qwen3.8-27B
63.4
Supported · #13/152
Basis
BenchAlign lane · 2 vs 8 public rows
Reading
Not comparable

Coding

Not comparable
K-EXAONE 2.0
Not ranked
Qwen3.8-27B
53.9
Supported · #43/151
Basis
BenchAlign lane · 3 vs 8 public rows
Reading
Not comparable

Reasoning

Not comparable
K-EXAONE 2.0
Not ranked
Qwen3.8-27B
77.4
#7/20
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
K-EXAONE 2.0
Not ranked
Qwen3.8-27B
54.1
Supported · #58/183
Basis
BenchAlign lane · 4 vs 6 public rows
Reading
Not comparable

Math

Not comparable
K-EXAONE 2.0
Not ranked
Qwen3.8-27B
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
K-EXAONE 2.0
Not ranked
Qwen3.8-27B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
K-EXAONE 2.0
Not ranked
Qwen3.8-27B
80.0
#11/48
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
K-EXAONE 2.0
Not ranked
Qwen3.8-27B
84.7
#45/123
Basis
Provisional lane · 1 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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

K-EXAONE 2.0
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request

K-EXAONE 2.0 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

K-EXAONE 2.0
Self-hosted; infrastructure cost varies
Fits in one request
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request

K-EXAONE 2.0 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

K-EXAONE 2.0
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
Qwen3.8-27B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

K-EXAONE 2.0 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.

Documented inputs

K-EXAONE 2.0

Not sourced

Qwen3.8-27B

Not sourced

Documented outputs

K-EXAONE 2.0

Not sourced

Qwen3.8-27B

Not sourced

Provider availability

K-EXAONE 2.0

Not sourced

Qwen3.8-27B

Not sourced

Reasoning profile

K-EXAONE 2.0

Reasoning

Qwen3.8-27B

Reasoning

Weight access

K-EXAONE 2.0

Open Weight

Qwen3.8-27B

Open Weight

License

K-EXAONE 2.0

Open Weight

Qwen3.8-27B

Open Weight

Release date

K-EXAONE 2.0

2026-07-31

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 evidence43 rows

Agentic

  • Terminal-Bench 2.1

    K-EXAONE 2.043.8%
    Source
    Qwen3.8-27B73.0%
    Source

    Qwen3.8-27B leads this result

  • Claw-Eval

    K-EXAONE 2.077.7%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • CoWorkBench

    K-EXAONE 2.0—
    Qwen3.8-27B70.7%
    Source

    Not directly comparable

  • JobBench

    K-EXAONE 2.0—
    Qwen3.8-27B33.4%
    Source

    Not directly comparable

  • Agents' Last Exam

    K-EXAONE 2.0—
    Qwen3.8-27B42.9%
    Source

    Not directly comparable

  • OSWorld-Verified

    K-EXAONE 2.0—
    Qwen3.8-27B84.3%
    Source

    Not directly comparable

  • WebArena-Verified

    K-EXAONE 2.0—
    Qwen3.8-27B64.8%
    Source

    Not directly comparable

  • AndroidWorld

    K-EXAONE 2.0—
    Qwen3.8-27B81.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    K-EXAONE 2.0—
    Qwen3.8-27B58.4%
    Source

    Not directly comparable

Coding

  • SciCode

    K-EXAONE 2.037.4%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • SWE-bench Verified

    K-EXAONE 2.068.2%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • Terminal-Bench 2.1

    K-EXAONE 2.043.8%
    Source
    Qwen3.8-27B73.0%
    Source

    Qwen3.8-27B leads this result

  • SWE-bench Pro

    K-EXAONE 2.0—
    Qwen3.8-27B61.7%
    Source

    Not directly comparable

  • NL2Repo

    K-EXAONE 2.0—
    Qwen3.8-27B42.3%
    Source

    Not directly comparable

  • DeepSWE

    K-EXAONE 2.0—
    Qwen3.8-27B42.2%
    Source

    Not directly comparable

  • LiveCodeBench v6

    K-EXAONE 2.0—
    Qwen3.8-27B90.3%
    Source

    Not directly comparable

  • VulcanBench v3

    K-EXAONE 2.0—
    Qwen3.8-27B82.6%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    K-EXAONE 2.0—
    Qwen3.8-27B84.0%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    K-EXAONE 2.0—
    Qwen3.8-27B86.0%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    K-EXAONE 2.083.5%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • GPQA-D

    K-EXAONE 2.082.2%
    Source
    Qwen3.8-27B89.2%
    Source

    Qwen3.8-27B leads this result

  • HLE

    K-EXAONE 2.018.3%
    Source
    Qwen3.8-27B30.8%
    Source

    Qwen3.8-27B leads this result

  • MMMLU

    K-EXAONE 2.086.6%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • GPQA

    K-EXAONE 2.0—
    Qwen3.8-27B89.2%
    Source

    Not directly comparable

  • HLE w/o tools

    K-EXAONE 2.0—
    Qwen3.8-27B30.8%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    K-EXAONE 2.0—
    Qwen3.8-27B88.9%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    K-EXAONE 2.0—
    Qwen3.8-27B84.3%
    Source

    Not directly comparable

Math

  • AIME26

    K-EXAONE 2.092.3%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • HMMT Feb 2026

    K-EXAONE 2.078.4%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • IMOAnswerBench

    K-EXAONE 2.078.6%
    Source
    Qwen3.8-27B—

    Not directly comparable

Multilingual

  • PolyMath

    K-EXAONE 2.071.3%
    Source
    Qwen3.8-27B—

    Not directly comparable

Multimodal

  • MathVision

    K-EXAONE 2.0—
    Qwen3.8-27B90.0%
    Source

    Not directly comparable

  • MathVision w/ Python

    K-EXAONE 2.0—
    Qwen3.8-27B94.6%
    Source

    Not directly comparable

  • BabyVision

    K-EXAONE 2.0—
    Qwen3.8-27B65.7%
    Source

    Not directly comparable

  • BabyVision w/ Python

    K-EXAONE 2.0—
    Qwen3.8-27B85.6%
    Source

    Not directly comparable

  • Vision2Web

    K-EXAONE 2.0—
    Qwen3.8-27B62.9%
    Source

    Not directly comparable

  • CharXiv w/o tools

    K-EXAONE 2.0—
    Qwen3.8-27B83.7%
    Source

    Not directly comparable

  • CharXiv

    K-EXAONE 2.0—
    Qwen3.8-27B90.2%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    K-EXAONE 2.0—
    Qwen3.8-27B91.1%
    Source

    Not directly comparable

  • RealWorldQA

    K-EXAONE 2.0—
    Qwen3.8-27B85.9%
    Source

    Not directly comparable

  • ERQA

    K-EXAONE 2.0—
    Qwen3.8-27B65.5%
    Source

    Not directly comparable

Instruction following

  • IFEval

    K-EXAONE 2.092.4%
    Source
    Qwen3.8-27B—

    Not directly comparable

  • IFBench

    K-EXAONE 2.072.6%
    Source
    Qwen3.8-27B79.5%
    Source

    Qwen3.8-27B leads this result

Frequently asked questions

Which is better, K-EXAONE 2.0 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, K-EXAONE 2.0 or Qwen3.8-27B?

K-EXAONE 2.0 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, K-EXAONE 2.0 or Qwen3.8-27B?

K-EXAONE 2.0 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, K-EXAONE 2.0 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, K-EXAONE 2.0 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 September 10, 2026

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