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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

Try it out API Provider Benchmarks

Speed

N/A

Output tokens per second

Cost

In $0.00Out $0.00

N/A

Cost per Intelligence Index task

Verbosity

N/A

Output tokens from Intelligence Index

K-EXAONE 2.0 is above average in intelligence and well priced 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 20 on the Artificial Analysis Intelligence Index, placing it above average among comparable models (median: 18).

Pricing for K-EXAONE 2.0 is $0.00 per 1M input tokens (competitively priced, median: $0.30) and $0.00 per 1M output tokens (competitively priced, median: $1.18).

Reasoning	Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist.
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

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

Highlights

Updated

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Speed

Output tokens per second · Higher is better

Cost per Task

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

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Estimate (independent evaluation forthcoming)

Artificial Analysis Intelligence Index v4.3 includes: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench v4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. 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

Estimate (independent evaluation forthcoming)

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.

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2

Agentic real-world work tasks, (Elo-500)/2000

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench v4.0New

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

IFBench

Instruction following

𝜏³-Banking

Agentic tool use

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

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

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Openness Index

Artificial Analysis Openness Index: Score

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

Intelligence Index Comparisons

Intelligence Index vs. Cost per Intelligence Index 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.

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis 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.

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

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.

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 20 (estimated) on the Artificial Analysis Intelligence Index, placing it above average among other open weight models of similar size (median: 18).

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 260k 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.

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

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

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

K-EXAONE 2.0 achieves a score of 20 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

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

Speed

Cost

Verbosity

Comparison Summary

Technical specifications

113 models in this class

Intelligence

Speed

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

Benchmarks

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index

Openness Index

Artificial Analysis Openness Index: Score

Intelligence Index Comparisons

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

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

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

Frequently Asked Questions

When was K-EXAONE 2.0 released?

Who created K-EXAONE 2.0?

How intelligent 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?