LLM Reference

K-EXAONE 236B-A23B

Researched 14d ago

Last refreshed 2026-05-19. Next refresh: weekly.

Open SourceLong context

K-EXAONE 236B-A23B has model metadata, but missing tracked provider pricing keeps it from being a default production pick.

Decision context: Long context task fit, 0 tracked provider routes, and research from 2026-05-19.

Use it for

  • Teams evaluating long context
  • Workloads that can use a 256k context window

Do not use it for

  • Cost-sensitive launches that need sourced token pricing
  • Vision or document-understanding workloads
  • Strict JSON or tool-calling flows

Cheapest output

-

No tracked output price

Provider routes

0

No provider route in seed

Quality / dollar

Unknown

No task benchmark coverage yet

Freshness

2026-05-19

Researched 14d ago

fresh

Top use-case fit

Long context

Included by capability and metadata signals in the decision map.

Provider price ladder

No tracked provider token pricing is available for this model yet.

Benchmark peer barsfor Long context

No task-mapped benchmark peers are available for this model yet.

Migration checks

No linked migration route is available for this model yet.

About

LG AI Research's K-EXAONE 236B-A23B is a large-scale open-source Mixture-of-Experts model featuring 236B total parameters with 23B active during inference. It uses a fine-grained MoE design and supports six languages: Korean, English, Spanish, German, Japanese, and Vietnamese. K-EXAONE achieved first place in 10 of 13 benchmarks under South Korea's national AI foundation model project and ranked 7th globally on the Artificial Analysis Intelligence Index at launch. Free API access was available via Friendli.ai through January 2026.

K-EXAONE 236B-A23B is an open-source model in the K-EXAONE family. The structured metadata tracks a 256k-token context window. Headline tracked benchmarks include Google-Proof Q&A 78.3.

Capabilities

No model capability flags are currently sourced.

Benchmark Scores(1)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionSource
Google-Proof Q&A78.3diamondhttps://artificialanalysis.ai/leaderboards/models

Rankings

Specifications

FamilyK-EXAONE
Released2025-12-31
Parameters236B
Context256k
ArchitectureMoE

Created by

Advancing AI for a Better Life

South Korea
Founded 2020
Website