DeepSeek V4 Pro 0813 vs K-Exaone

Updated November 11, 2026. Rank says DeepSeek V4 Pro 0813 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

DeepSeek V4 Pro 0813 has the higher public point estimate, 62.43 versus 38.02. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

DeepSeek V4 Pro 0813

DeepSeek

62.43/100

Estimated · Public rank #42

Conditional range 48.1–76.8

Model B

K-Exaone

LG AI Research

38.02/100

Estimated · Public rank #149

Conditional range 23.7–52.4

API price per 1M tokens

DeepSeek V4 Pro 0813: $1.32 input / $3.96 output

K-Exaone: No comparable API price

Context window

DeepSeek V4 Pro 0813: 1M

K-Exaone: 256K

Released

DeepSeek V4 Pro 0813: August 13, 2026

K-Exaone: December 31, 2025

Shared results: 1
DeepSeek V4 Pro 0813 only: 41
K-Exaone only: 0
Like-for-like categories: 0 / 8

Estimated: DeepSeek V4 Pro 0813 and K-Exaone. Conditional ranges do not establish rank confidence.How the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Long documents
Prompts that approach the documented context limit
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
K-Exaone is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
K-Exaone is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

47.9DeepSeek V4 Pro 081326.1K-Exaone

Directional only · BenchAlign v5.8

DeepSeek V4 Pro 0813 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

What do you need a model for?

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

SWE-bench VerifiedCoding
Normalized gap 31.2
DeepSeek V4 Pro 0813:80.6%
K-Exaone:49.4%
DeepSeek V4 Pro 0813 source K-Exaone source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Coding

Directional only

DeepSeek V4 Pro 0813: 47.9; Supported · #53/158
K-Exaone: 26.1; Estimated · #122/158
Basis: BenchAlign v5.8 lane · 15 vs 1 public rows
Reading: Directional only

Reasoning

Directional only

DeepSeek V4 Pro 0813: 58.1; Estimated · #39/158
K-Exaone: 29.9; Estimated · #124/158
Basis: BenchAlign v5.8 lane · 4 vs 0 public rows
Reading: Directional only

Knowledge

Directional only

DeepSeek V4 Pro 0813: 62.5; Estimated · #41/179
K-Exaone: 35.5; Estimated · #135/179
Basis: BenchAlign v5.8 lane · 7 vs 0 public rows
Reading: Directional only

Agentic

Not comparable

DeepSeek V4 Pro 0813: 55.0; Supported · #44/132
K-Exaone: Not ranked
Basis: BenchAlign v5.8 lane · 12 vs 0 public rows
Reading: Not comparable

Multimodal

Not comparable

DeepSeek V4 Pro 0813: Not ranked
K-Exaone: Not ranked
Basis: BenchAlign v5.8 lane · 0 vs 0 public rows
Reading: Not comparable

Multilingual

Not comparable

DeepSeek V4 Pro 0813: Not ranked
K-Exaone: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

DeepSeek V4 Pro 0813: Not ranked
K-Exaone: 78.7; Unranked · 1 rankable row
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

DeepSeek V4 Pro 0813: 84.4; Unranked · 4 rankable rows
K-Exaone: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for DeepSeek V4 Pro 0813 and K-Exaone
Category	DeepSeek V4 Pro 0813	K-Exaone	Basis	Reading
Coding	47.9Supported · #53/158	26.1Estimated · #122/158	Directional onlyBenchAlign v5.8 lane · 15 vs 1 public rows	Directional only
Reasoning	58.1Estimated · #39/158	29.9Estimated · #124/158	Directional onlyBenchAlign v5.8 lane · 4 vs 0 public rows	Directional only
Knowledge	62.5Estimated · #41/179	35.5Estimated · #135/179	Directional onlyBenchAlign v5.8 lane · 7 vs 0 public rows	Directional only
Agentic	55.0Supported · #44/132	Not ranked	Not comparableBenchAlign v5.8 lane · 12 vs 0 public rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	78.7Unranked · 1 rankable row	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	84.4Unranked · 4 rankable rows	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

DeepSeek V4 Pro 0813: $0.0033; Fits in one request
K-Exaone: API rate not published; Fits in one request

K-Exaone has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro 0813: $0.07788; Fits in one request
K-Exaone: API rate not published; Fits in one request

K-Exaone has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

DeepSeek V4 Pro 0813: $0.0748; Fits in one request
K-Exaone: API rate not published; Fits in one request; Cached-input rate unavailable

K-Exaone has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationDeepSeek V4 Pro 0813K-Exaone

Context window

Maximum documented context; output-token limits may be lower.

DeepSeek V4 Pro 0813

DeepSeek models and pricing

K-Exaone

256K

API model ID

DeepSeek V4 Pro 0813

deepseek-v4-pro

DeepSeek models and pricing

K-Exaone

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

DeepSeek V4 Pro 0813

$0.044 per 1M cached input tokens

DeepSeek: Models & Pricing

K-Exaone

No comparable hosted API rate

Documented inputs

DeepSeek V4 Pro 0813

text

DeepSeek GitHub Copilot integration

K-Exaone

text

K-EXAONE-236B-A23B model card

Documented outputs

DeepSeek V4 Pro 0813

text

DeepSeek GitHub Copilot integration

K-Exaone

text

K-EXAONE-236B-A23B model card

Provider availability

DeepSeek V4 Pro 0813

Generally Available · DeepSeek API

DeepSeek models and pricing, V4 Pro continuation footnote

K-Exaone

Not sourced

Reasoning profile

DeepSeek V4 Pro 0813

Reasoning

K-Exaone

Reasoning

Weight access

DeepSeek V4 Pro 0813

Open Weight

K-Exaone

Proprietary

License

DeepSeek V4 Pro 0813

Open Weight

K-Exaone

Proprietary

Release date

DeepSeek V4 Pro 0813

2026-08-13

K-Exaone

2025-12-31

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: DeepSeek V4 Pro 0813 has the higher public point estimate, 62.43 versus 38.02. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: DeepSeek V4 Pro 0813 has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek V4 Pro 0813 or K-Exaone?

DeepSeek V4 Pro 0813 has the higher public point estimate, 62.43 versus 38.02. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, DeepSeek V4 Pro 0813 or K-Exaone?

DeepSeek V4 Pro 0813 scores higher for coding on the public lane, 47.9 to 26.1. K-Exaone is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, DeepSeek V4 Pro 0813 or K-Exaone?

K-Exaone is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek V4 Pro 0813 or K-Exaone?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, DeepSeek V4 Pro 0813 or K-Exaone?

DeepSeek V4 Pro 0813 has the larger documented context window: 1M, compared with 256K.

Self-host vs API cost

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

DeepSeek V4 Pro 0813

API / mo$3,960

Self-host / mo$37,843

Break-evenNot reached at 70% load

K-Exaone

API / moNo API price on file

Self-host / moNot listed

Break-even—

Proprietary model — self-hosting not applicable.

Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence42 rows

Agentic

Finch
DeepSeek V4 Pro 081367.4%
Source
K-Exaone—
Not directly comparable
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%
Source
K-Exaone—
Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%
Source
K-Exaone—
Not directly comparable
BrowseComp
DeepSeek V4 Pro 081383.4%
Source
K-Exaone—
Not directly comparable
HLE w/ tools
DeepSeek V4 Pro 081360.0%
Source
K-Exaone—
Not directly comparable
MCP Atlas
DeepSeek V4 Pro 081373.6%
Source
K-Exaone—
Not directly comparable
Toolathlon
DeepSeek V4 Pro 081351.8%
Source
K-Exaone—
Not directly comparable
CyberGym
DeepSeek V4 Pro 081383.3%
Source
K-Exaone—
Not directly comparable
Toolathlon-Verified
DeepSeek V4 Pro 081374.1%
Source
K-Exaone—
Not directly comparable
Agents' Last Exam
DeepSeek V4 Pro 081325.7%
Source
K-Exaone—
Not directly comparable
AutomationBench
DeepSeek V4 Pro 081331.8%
Source
K-Exaone—
Not directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V4 Pro 081354.7%
Source
K-Exaone—
Not directly comparable

Coding

LiveCodeBench Pass@1-COT
DeepSeek V4 Pro 081393.5%
Source
K-Exaone—
Not directly comparable
Codeforces
DeepSeek V4 Pro 08133206.0
Source
K-Exaone—
Not directly comparable
SWE-bench Verified
DeepSeek V4 Pro 081380.6%
Source
K-Exaone49.4%
Source
DeepSeek V4 Pro 0813 leads this result
SWE-bench Pro
DeepSeek V4 Pro 081355.4%
Source
K-Exaone—
Not directly comparable
SWE Multilingual
DeepSeek V4 Pro 081376.2%
Source
K-Exaone—
Not directly comparable
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%
Source
K-Exaone—
Not directly comparable
Vibe Code Bench
DeepSeek V4 Pro 081349.93%
Source
K-Exaone—
Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%
Source
K-Exaone—
Not directly comparable
NL2Repo
DeepSeek V4 Pro 081361.5%
Source
K-Exaone—
Not directly comparable
DeepSWE
DeepSeek V4 Pro 081362.7%
Source
K-Exaone—
Not directly comparable
DSBench-FullStack
DeepSeek V4 Pro 081371.1%
Source
K-Exaone—
Not directly comparable
DSBench-Hard
DeepSeek V4 Pro 081367.2%
Source
K-Exaone—
Not directly comparable
OpenHarmony Bench
DeepSeek V4 Pro 081359.0%
Source
K-Exaone—
Not directly comparable
LiveCodeBench (Vals)
DeepSeek V4 Pro 081387.5%
Source
K-Exaone—
Not directly comparable
SWE-bench (Vals)
DeepSeek V4 Pro 081396.4%
Source
K-Exaone—
Not directly comparable

Reasoning

MRCR 1M
DeepSeek V4 Pro 081383.5%
Source
K-Exaone—
Not directly comparable
CorpusQA 1M
DeepSeek V4 Pro 081362.0%
Source
K-Exaone—
Not directly comparable
ARC-AGI-1
DeepSeek V4 Pro 081390.00%
Source
K-Exaone—
Not directly comparable
ARC-AGI-2
DeepSeek V4 Pro 081361.3%
Source
K-Exaone—
Not directly comparable

Knowledge

MMLU-Pro
DeepSeek V4 Pro 081387.5%
Source
K-Exaone—
Not directly comparable
Chinese-SimpleQA
DeepSeek V4 Pro 081384.4%
Source
K-Exaone—
Not directly comparable
GPQA
DeepSeek V4 Pro 081390.1%
Source
K-Exaone—
Not directly comparable
GPQA-D
DeepSeek V4 Pro 081390.1%
Source
K-Exaone—
Not directly comparable
HLE
DeepSeek V4 Pro 081342.7%
Source
K-Exaone—
Not directly comparable
GPQA Diamond (Vals)
DeepSeek V4 Pro 081392.4%
Source
K-Exaone—
Not directly comparable
MMLU-Pro (Vals)
DeepSeek V4 Pro 081387.0%
Source
K-Exaone—
Not directly comparable

Math

HMMT Feb 2026
DeepSeek V4 Pro 081395.2%
Source
K-Exaone—
Not directly comparable
IMOAnswerBench
DeepSeek V4 Pro 081389.8%
Source
K-Exaone—
Not directly comparable
Apex
DeepSeek V4 Pro 081338.3%
Source
K-Exaone—
Not directly comparable
Apex Shortlist
DeepSeek V4 Pro 081390.2%
Source
K-Exaone—
Not directly comparable

42 public results · 1 shared

Related comparisons

Compare API pricing Read the methodology Open the model selector

Watch DeepSeek V4 Pro 0813 vs K-Exaone

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 7,000+ subscribers.

Last updated November 11, 2026

DeepSeek V4 Pro 0813 vs K-Exaone

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

Or describe your own job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Reasoning

Knowledge

Agentic

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, DeepSeek V4 Pro 0813 or K-Exaone?

Which is better for coding, DeepSeek V4 Pro 0813 or K-Exaone?

Which is better for agentic tasks, DeepSeek V4 Pro 0813 or K-Exaone?

Which costs less, DeepSeek V4 Pro 0813 or K-Exaone?

Which has the larger context window, DeepSeek V4 Pro 0813 or K-Exaone?

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Math

Related comparisons

Watch DeepSeek V4 Pro 0813 vs K-Exaone