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Open weights model

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Released August 2026

K-EXAONE 2.0: Intelligence, Performance & Price Analysis

Name: Intelligence
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

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Performance

N/A

Time per Intelligence Index task (minutes)

Cost

In -Out -

N/A

Cost per Intelligence Index task

Verbosity

Input tokens 4.42BOutput/input 1.426%

63.1M

Output tokens from Intelligence Index

K-EXAONE 2.0 is among the least intelligent models when comparing to other open weight models of similar size. The model supports text input, outputs text, and has a 262k tokens context window.

K-EXAONE 2.0 scores 8 on the Artificial Analysis Intelligence Index, placing it at the lower end among comparable models (median: 23). When evaluating the Intelligence Index, it generated 63.1M tokens, which is very concise in comparison to the median of 104M.

Reasoning	Yes
Input modality	Supports: text
Output modality	Supports: text
Context window	262k ~393 A4 pages of size 12 Arial font
Total parameters	750B
Active parameters	37B Number of parameters active per token during inference
License	Apache 2.0
Model weights	Hugging Face

Metrics are compared against models of the same class:

Non-reasoning models → compared only with other non-reasoning models
Reasoning models → compared across both reasoning and non-reasoning
Open weights models → compared only with other open weights models of the same size class:

Tiny: ≤4B parameters
Small: 4B–40B parameters
Medium: 40B–150B parameters
Large: >150B parameters

Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:

<$0.15 per 1M tokens
$0.15–$1 per 1M tokens
>$1 per 1M tokens

Updated

Intelligence

Artificial Analysis Intelligence Index · Higher is better

No publicly available API

Time per Task

Inference time (minutes) per Intelligence Index task · Lower is better

No publicly available API

Cost per Task

Weighted average cost (USD) per Intelligence Index task · Lower is better

No publicly available API

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

Intelligence Index vs. Cost per Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Capability Indexes

Measures the performance of models on specific capabilities and industries

Finance & Accounting Index

Incorporates 5 evaluations: AA-Briefcase v1.1, AA-Omniscience v1.1, HLE-Diamond, AutomationBench-AA, GDP.pdf · Higher is better

Strategy & Ops Index

Incorporates 4 evaluations: AutomationBench-AA, AA-Briefcase v1.1, AA-Omniscience v1.1, GDP.pdf · Higher is better

Legal Index

Incorporates 5 evaluations: AA-Omniscience v1.1, AA-Briefcase v1.1, HLE-Diamond, GDP.pdf, AutomationBench-AA · Higher is better

Engineering Index

Incorporates 7 evaluations: AA-Omniscience v1.1, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1, HLE-Diamond, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Briefcase v1.1 · Higher is better

Economics Index

Incorporates 4 evaluations: AA-Omniscience v1.1, AA-Briefcase v1.1, HLE-Diamond, GDP.pdf · Higher is better

Benchmarks

Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience v1.1 AccuracyUpdated

Knowledge

AA-Omniscience v1.1 Non-Hallucination RateUpdated

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode-AA 2.0New

Agentic software engineering in open-source repositories

HLE-DiamondNew

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

CritPt v2 (Hard Subset)

Physics reasoning

GDPval-AA v2.1

Agentic real-world work tasks, Elo

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

Openness Index

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Intelligence Index task · Segmented by token type · Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Token Use

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Context Window

Context window: tokens limit · Higher is better

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference (billions)

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Further details

								Weights
K-EXAONE 2.0	LG AI Research	8	750B 37B active at inference time	262k	-	-	-	🤗	-

Frequently Asked Questions

Common questions about K-EXAONE 2.0

K-EXAONE 2.0 was released on August 12, 2026.

K-EXAONE 2.0 was created by LG AI Research.

K-EXAONE 2.0 scores 8 on the Artificial Analysis Intelligence Index, placing it at the lower end among other open weight models of similar size (median: 23).

When evaluated on the Intelligence Index, K-EXAONE 2.0 generated 63.1M output tokens, which is very competitive compared to other open weight models of similar size (median: 104M).

Yes, K-EXAONE 2.0 is a reasoning model. It uses extended thinking or chain-of-thought reasoning to work through complex problems before providing an answer.

K-EXAONE 2.0 supports text input.

K-EXAONE 2.0 supports text output.

No, K-EXAONE 2.0 does not support image input. It can only process text.

No, K-EXAONE 2.0 is not multimodal. It only supports text input.

K-EXAONE 2.0 has a context window of 262k tokens. This determines how much text and conversation history the model can process in a single request.

Yes, K-EXAONE 2.0 is open weights. The model weights are publicly available and can be downloaded for self-hosting.

K-EXAONE 2.0 has 750B parameters (37B active).

K-EXAONE 2.0 is a Mixture of Experts (MoE) model with 750B total parameters, but only 37B active parameters are used during inference.

K-EXAONE 2.0 is released under the Apache 2.0 license. This license allows commercial use. View license

K-EXAONE 2.0 achieves a score of 8 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across knowledge work, coding, science, and general knowledge.

K-EXAONE 2.0 is an open weights model that can be self-hosted. View providers

K-EXAONE 2.0 is an open weights model that can be downloaded and self-hosted. Compare providers

K-EXAONE 2.0: Intelligence, Performance & Price Analysis

Model summary

IntelligenceUpdated

Performance

Cost

Verbosity

Technical specifications

131 models in this class

Intelligence

Time per Task

Cost per Task

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

Intelligence Index vs. Cost per Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Capability Indexes

Capability Indexes

Benchmarks

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

Openness Index

Artificial Analysis Openness Index: Score

Cost

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Token Use

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Context Window

Context Window

Context Window for RAG

Context Window

Model Size (Open Weights Models Only)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Further details

More from LG AI Research

Frequently Asked Questions

When was K-EXAONE 2.0 released?

Who created K-EXAONE 2.0?

How intelligent is K-EXAONE 2.0?

How verbose is K-EXAONE 2.0?

Is K-EXAONE 2.0 a reasoning model?

What input modalities does K-EXAONE 2.0 support?

What output modalities does K-EXAONE 2.0 support?

Can K-EXAONE 2.0 process images?

Is K-EXAONE 2.0 multimodal?

What is the context window of K-EXAONE 2.0?

Is K-EXAONE 2.0 open source?

How many parameters does K-EXAONE 2.0 have?

What are the active parameters of K-EXAONE 2.0?

What is the license for K-EXAONE 2.0?

How does K-EXAONE 2.0 perform on benchmarks?

Is K-EXAONE 2.0 available via API?

Where can I use K-EXAONE 2.0?