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Proprietary model

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Released August 2026

K-EXAONE 2.0 0803 Intelligence, Performance & Price Analysis

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

API Provider Benchmarks

Speed

N/A

Output tokens per second

Verbosity

84M

Output tokens from Intelligence Index

K-EXAONE 2.0 0803 is above average in intelligence and well priced when comparing to other models of similar price. The model supports text input, outputs text, and has a 262k tokens context window.

K-EXAONE 2.0 0803 scores 31 on the Artificial Analysis Intelligence Index, placing it above average among comparable models (median: 16). When evaluating the Intelligence Index, it generated 84M tokens, which is somewhat verbose in comparison to the median of 69M.

Pricing for K-EXAONE 2.0 0803 is $0.00 per 1M input tokens (competitively priced, median: $0.18) and $0.00 per 1M output tokens (competitively priced, median: $0.57).

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

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

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

Intelligence

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR

Reasoning models are indicated by a lightbulb icon

Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. 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

Reasoning models are indicated by a lightbulb icon

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if the weights are available but commercial use is limited (typically requires obtaining a paid license).

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

GDPval-AA v2

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

𝜏³-BankingUpdated

Agentic tool use

Terminal-Bench v2.1

Agentic coding & terminal use

SciCode

Coding

Humanity's Last ExamUpdated

Reasoning & knowledge

GPQA Diamond

Scientific reasoning

CritPt

Physics reasoning

AA-Omniscience AccuracyUpdated

Knowledge

AA-Omniscience Non-Hallucination RateUpdated

1 - hallucination rate

AA-LCRUpdated

Long context reasoning

AA-Briefcase

Agentic knowledge work, Elo

AutomationBench-AA

Agentic SaaS workflows

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgentNew

Quantitative analysis on spreadsheets & documents

IFBench

Instruction following

APEX-Agents-AA

Long-horizon agentic tasks

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

Reasoning models are indicated by a lightbulb icon

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.

Reasoning models are indicated by a lightbulb icon

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

Reasoning models are indicated by a lightbulb icon

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.

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

Reasoning models are indicated by a lightbulb icon

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).

Cost

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better

Reasoning models are indicated by a lightbulb icon

Cost to Run Artificial Analysis Intelligence Index

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

Reasoning models are indicated by a lightbulb icon

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)

Reasoning models are indicated by a lightbulb icon

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

Reasoning models are indicated by a lightbulb icon

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).

Frequently Asked Questions

Common questions about K-EXAONE 2.0 0803

K-EXAONE 2.0 0803 was released on August 12, 2026.

K-EXAONE 2.0 0803 was created by LG AI Research.

K-EXAONE 2.0 0803 scores 31 on the Artificial Analysis Intelligence Index, placing it above average among other reasoning models in a similar price tier (median: 16).

When evaluated on the Intelligence Index, K-EXAONE 2.0 0803 generated 84M output tokens, which is somewhat higher than average compared to other reasoning models in a similar price tier (median: 69M).

Yes, K-EXAONE 2.0 0803 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 0803 supports text input.

K-EXAONE 2.0 0803 supports text output.

No, K-EXAONE 2.0 0803 does not support image input. It can only process text.

No, K-EXAONE 2.0 0803 is not multimodal. It only supports text input.

K-EXAONE 2.0 0803 has a context window of 260k tokens. This determines how much text and conversation history the model can process in a single request.

No, K-EXAONE 2.0 0803 is proprietary. The model weights are not publicly available.

K-EXAONE 2.0 0803 is a proprietary model and LG AI Research has not disclosed the model size or parameter count.

K-EXAONE 2.0 0803 achieves a score of 31 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.

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K-EXAONE 2.0 0803 Intelligence, Performance & Price Analysis

Model summary

Intelligence

Speed

Input Price

Output Price

Verbosity

Comparison Summary

Technical specifications

605 models in this class

Intelligence

Speed

Cost per Task

Intelligence

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

Intelligence Index Comparisons

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Token Use

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

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

Frequently Asked Questions

When was K-EXAONE 2.0 0803 released?

Who created K-EXAONE 2.0 0803?

How intelligent is K-EXAONE 2.0 0803?

How verbose is K-EXAONE 2.0 0803?

Is K-EXAONE 2.0 0803 a reasoning model?

What input modalities does K-EXAONE 2.0 0803 support?

What output modalities does K-EXAONE 2.0 0803 support?

Can K-EXAONE 2.0 0803 process images?

Is K-EXAONE 2.0 0803 multimodal?

What is the context window of K-EXAONE 2.0 0803?

Is K-EXAONE 2.0 0803 open source?

How many parameters does K-EXAONE 2.0 0803 have?

How does K-EXAONE 2.0 0803 perform on benchmarks?

Is K-EXAONE 2.0 0803 available via API?

Where can I use K-EXAONE 2.0 0803?