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Model comparison

Exaone 4.0 32B vs Qwen3.6-27B

Data verified

Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

LG AI Research
40.44/100
Margin
13.4pts
winning →
53.82/100
1 category wins0 category wins

Public leaderboard positions: Exaone 4.0 32B #170 (Estimated); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Exaone 4.0 32B and Qwen3.6-27B share 12 comparable benchmark results. 1 of 8 categories are comparable. 1 result is unique to Exaone 4.0 32B; 42 to Qwen3.6-27B.

Updated July 21, 2026
Shared results
12
Exaone 4.0 32B only
1
Qwen3.6-27B only
42
Comparable categories
1 / 8

Pick Qwen3.6-27B if you want the stronger benchmark profile. Exaone 4.0 32B only becomes the better choice if knowledge is the priority.

Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

Qwen3.6-27B is clearly ahead on the BenchAlign aggregate, 53.82 to 40.44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Qwen3.6-27B gives you the larger context window at 262K, compared with 128K for Exaone 4.0 32B.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for Exaone 4.0 32B and Qwen3.6-27B
CategoryExaone 4.0 32BΔQwen3.6-27B
KnowledgeExaone 4.0 32B81.8Margin 28.5Qwen3.6-27B53.3
AgenticExaone 4.0 32BNot measuredMarginNo overlapQwen3.6-27B59.3
CodingExaone 4.0 32BNot measuredMarginNo overlapQwen3.6-27B77.5
MathExaone 4.0 32BNot measuredMarginNo overlapQwen3.6-27B89.2
MultimodalExaone 4.0 32BNot measuredMarginNo overlapQwen3.6-27B76.7

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · Exaone 4.0 32BB · Qwen3.6-27B
  1. MMLU-Pro

    Knowledge
    Source ↗
    A 81.8%B 86.2%
    Winner: Qwen3.6-27BΔ 4.4
    MMLU-Pro: Exaone 4.0 32B scored 81.8%; Qwen3.6-27B scored 86.2%. Qwen3.6-27B wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricExaone 4.0 32BQwen3.6-27BComparison
Input / output priceUSD per 1M tokensExaone 4.0 32BNot availableQwen3.6-27B$0 input / $0 outputA complete price comparison is not available.
Generation speedtokens per secondExaone 4.0 32BNot availableQwen3.6-27BNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenExaone 4.0 32BNot availableQwen3.6-27BNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensExaone 4.0 32B128KQwen3.6-27B262KQwen3.6-27B lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkExaone 4.0 32BQwen3.6-27BResult
τ²-bench resultsSource 4.1%94.2%Qwen3.6-27B leads
Terminal-Bench 2.0Source 59.3%Not comparable
Claw-EvalSource 72.4%Not comparable
QwenClawBenchSource 53.4%Not comparable
QwenWebBenchSource 1487Not comparable
AndroidWorldSource 70.3%Not comparable
AA Agentic IndexSource 27.0%Not comparable
GDPval-AASource 32.0%Not comparable
GDPval-AASource 1140Not comparable
Gert LabsSource 54.84%Not comparable
Coding
BenchmarkExaone 4.0 32BQwen3.6-27BResult
AA-SciCodeSource 25.2%39.8%Qwen3.6-27B leads
SWE-bench VerifiedSource 77.2%Not comparable
SWE MultilingualSource 71.3%Not comparable
SWE-bench ProSource 53.5%Not comparable
Terminal-Bench 2.0Source 59.3%Not comparable
LiveCodeBenchSource 83.9%Not comparable
NL2RepoSource 36.2%Not comparable
AA Coding IndexSource 53.7%Not comparable
Reasoning
BenchmarkExaone 4.0 32BQwen3.6-27BResult
AA-LCRSource 8.0%68.7%Qwen3.6-27B leads
CritPtSource 0.0%1.1%Qwen3.6-27B leads
KnowledgeExaone 4.0 32B wins
BenchmarkExaone 4.0 32BQwen3.6-27BResult
MMLU-ProSource 81.8%86.2%Qwen3.6-27B leads
Artificial Analysis Intelligence IndexSource 6.0%37.0%Qwen3.6-27B leads
AA-GPQA DiamondSource 62.8%84.2%Qwen3.6-27B leads
AA-HLESource 4.9%21.6%Qwen3.6-27B leads
AA-Omniscience IndexSource -62.3%-19.8%Qwen3.6-27B leads
AA-Omniscience AccuracySource 10.4%19.2%Qwen3.6-27B leads
AA-Omniscience Hallucination RateSource 81.0%48.3%Qwen3.6-27B leads
MMLU-ReduxSource 93.5%Not comparable
SuperGPQASource 66%Not comparable
C-EvalSource 91.4%Not comparable
GPQASource 87.8%Not comparable
HLESource 24%Not comparable
Math
BenchmarkExaone 4.0 32BQwen3.6-27BResult
AIME 2025Source 85.3%Not comparable
HMMT Feb 2025Source 93.8%Not comparable
HMMT Nov 2025Source 90.7%Not comparable
HMMT Feb 2026Source 84.3%Not comparable
MMAnswerBenchSource 80.8%Not comparable
AIME26Source 94.1%Not comparable
Multimodal
BenchmarkExaone 4.0 32BQwen3.6-27BResult
MMMUSource 82.9%Not comparable
MMMU-ProSource 75.8%Not comparable
RealWorldQASource 84.1%Not comparable
DynaMathSource 85.6%Not comparable
MStarSource 81.4%Not comparable
SimpleVQASource 56.1%Not comparable
CharXivSource 78.4%Not comparable
CC-OCRSource 81.2%Not comparable
CountBenchSource 97.8%Not comparable
RefCOCO (avg)Source 92.5%Not comparable
ERQASource 62.5%Not comparable
Video-MME (with subtitle)Source 87.7%Not comparable
VideoMMMUSource 84.4%Not comparable
MLVU (M-Avg)Source 86.6%Not comparable
V*Source 94.7%Not comparable
AA-MMMU-ProSource 74.6%Not comparable
Inst. Following
BenchmarkExaone 4.0 32BQwen3.6-27BResult
AA-IFBenchSource 33.5%67.6%Qwen3.6-27B leads
Frequently Asked Questions (2)

Which is better, Exaone 4.0 32B or Qwen3.6-27B?

Qwen3.6-27B is ahead on BenchLM's BenchAlign leaderboard, 53.82 to 40.44. The biggest single separator in this matchup is MMLU-Pro, where the scores are 81.8% and 86.2%.

Which is better for knowledge tasks, Exaone 4.0 32B or Qwen3.6-27B?

Exaone 4.0 32B has the edge for knowledge tasks in this comparison, averaging 81.8 versus 53.3. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.

Self-host vs API cost

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

Exaone 4.0 32B
API / mo$0
Self-host / moNot listed
Break-even
Proprietary model — self-hosting not applicable.
Qwen3.6-27B
API / mo$0
Self-host / mo$429
Break-even
Model the full break-even

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Last updated: July 21, 2026

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