GPT-5.4 vs K-Exaone

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.4

OpenAI

71.27/100

Supported · Public rank #14

90% interval 68.2–74.3

Model B

K-Exaone

LG AI Research

43.48/100

Estimated · Public rank #176

90% interval 32.0–55.0

Updated September 22, 2026. Rank says GPT-5.4 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Share or export

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
GPT-5.4
GPT-5.4 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 scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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.

54.7GPT-5.445.2K-Exaone

Directional only · BenchAlign

GPT-5.4 scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.

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

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

38 GPT-5.4 only

Shared results: 0
GPT-5.4 only: 38
K-Exaone only: 0
Like-for-like categories: 0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, 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.

Agentic

Directional only

GPT-5.4: 53.4; Supported · #40/154
K-Exaone: 46.0; Estimated · #86/154
Basis: BenchAlign lane · 14 vs 0 public rows
Reading: Directional only

Coding

Directional only

GPT-5.4: 54.7; Supported · #41/156
K-Exaone: 45.2; Estimated · #93/156
Basis: BenchAlign lane · 4 vs 0 public rows
Reading: Directional only

Knowledge

Directional only

GPT-5.4: 69.2; Supported · #16/186
K-Exaone: 42.7; Estimated · #124/186
Basis: BenchAlign lane · 7 vs 0 public rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.4: 57.2; #16/19
K-Exaone: 63.1; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.4: 69.3; #20/49
K-Exaone: Not ranked
Basis: Provisional lane · 3 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.4: Not ranked
K-Exaone: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.4: 89.2; #19/124
K-Exaone: 77.2; Unranked · 1 rankable row
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.4: 64.4; Unranked · 2 rankable rows
K-Exaone: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.4 and K-Exaone
Category	GPT-5.4	K-Exaone	Basis	Reading
Agentic	53.4Supported · #40/154	46.0Estimated · #86/154	Directional onlyBenchAlign lane · 14 vs 0 public rows	Directional only
Coding	54.7Supported · #41/156	45.2Estimated · #93/156	Directional onlyBenchAlign lane · 4 vs 0 public rows	Directional only
Knowledge	69.2Supported · #16/186	42.7Estimated · #124/186	Directional onlyBenchAlign lane · 7 vs 0 public rows	Directional only
Reasoning	57.2#16/19	63.1Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multimodal	69.3#20/49	Not ranked	Not comparableProvisional lane · 3 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	89.2#19/124	77.2Unranked · 1 rankable row	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	64.4Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

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

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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

GPT-5.4: $0.01; 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

GPT-5.4: $0.17; 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

GPT-5.4: $0.25; 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.

Specification differences

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

SpecificationGPT-5.4K-Exaone

Context window

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

GPT-5.4

1.05M

OpenAI pricing

K-Exaone

256K

API model ID

GPT-5.4

gpt-5.4

OpenAI 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.

GPT-5.4

$0.25 per 1M cached input tokens

OpenAI pricing

K-Exaone

No comparable hosted API rate

Documented inputs

GPT-5.4

Not sourced

K-Exaone

Not sourced

Documented outputs

GPT-5.4

Not sourced

K-Exaone

Not sourced

Provider availability

GPT-5.4

Not sourced

K-Exaone

Not sourced

Reasoning profile

GPT-5.4

Reasoning

K-Exaone

Reasoning

Weight access

GPT-5.4

Proprietary

K-Exaone

Proprietary

License

GPT-5.4

Proprietary

K-Exaone

Proprietary

Release date

GPT-5.4

2026-03-05

K-Exaone

Not sourced

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: The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.4 has the larger documented window (1.05M).

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

Benchmark evidence

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

Browse raw public benchmark evidence38 rows

Agentic

Terminal-Bench 2.0
GPT-5.475.1%
Source
K-Exaone—
Not directly comparable
CyberGym
GPT-5.479.0%
Source
K-Exaone—
Not directly comparable
BrowseComp
GPT-5.482.7%
Source
K-Exaone—
Not directly comparable
OSWorld-Verified
GPT-5.475%
Source
K-Exaone—
Not directly comparable
MCP Atlas
GPT-5.470.6%
Source
K-Exaone—
Not directly comparable
Toolathlon
GPT-5.454.6%
Source
K-Exaone—
Not directly comparable
τ²-bench results
GPT-5.498.9%
Source
K-Exaone—
Not directly comparable
Claw-Eval
GPT-5.460.3%
Source
K-Exaone—
Not directly comparable
DeepSearchQA
GPT-5.473.6%
Source
K-Exaone—
Not directly comparable
Gert Labs
GPT-5.464.89%
Source
K-Exaone—
Not directly comparable
ResearchClawBench
GPT-5.415.3%
Source
K-Exaone—
Not directly comparable
JobBench
GPT-5.438.9%
Source
K-Exaone—
Not directly comparable
ExploitGym
GPT-5.46.0%
Source
K-Exaone—
Not directly comparable
ApprenticeBench
GPT-5.411%
Source
K-Exaone—
Not directly comparable

Coding

LiveCodeBench Pro
GPT-5.487.5%
Source
K-Exaone—
Not directly comparable
SWE-bench Pro
GPT-5.457.7%
Source
K-Exaone—
Not directly comparable
React Native Evals
GPT-5.485.3%
Source
K-Exaone—
Not directly comparable
Vibe Code Bench
GPT-5.467.42%
Source
K-Exaone—
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.474.0%
Source
K-Exaone—
Not directly comparable
ARC-AGI-3
GPT-5.40.2%
Source
K-Exaone—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.481.2%
Source
K-Exaone—
Not directly comparable
OfficeQA Pro
GPT-5.453.2%
Source
K-Exaone—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.482.1%
Source
K-Exaone—
Not directly comparable
CharXiv
GPT-5.482.8%
Source
K-Exaone—
Not directly comparable
ERQA
GPT-5.465.4%
Source
K-Exaone—
Not directly comparable
SimpleVQA
GPT-5.461.1%
Source
K-Exaone—
Not directly comparable
ScreenSpot Pro
GPT-5.485.4%
Source
K-Exaone—
Not directly comparable
ZeroBench
GPT-5.441.0%
Source
K-Exaone—
Not directly comparable
MedXpertQA (MM)
GPT-5.477.1%
Source
K-Exaone—
Not directly comparable

Knowledge

GPQA
GPT-5.492.8%
Source
K-Exaone—
Not directly comparable
HLE
GPT-5.452.1%
Source
K-Exaone—
Not directly comparable
HLE w/o tools
GPT-5.439.8%
Source
K-Exaone—
Not directly comparable
GPQA-D
GPT-5.492.8%
Source
K-Exaone—
Not directly comparable
HealthBench Hard
GPT-5.440.1%
Source
K-Exaone—
Not directly comparable
MedXpertQA (Text)
GPT-5.459.6%
Source
K-Exaone—
Not directly comparable
HealthBench Professional
GPT-5.448.1%
Source
K-Exaone—
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GPT-5.447.600%
Source
K-Exaone—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.427.100%
Source
K-Exaone—
Not directly comparable

Questions

Which is better, GPT-5.4 or K-Exaone?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-5.4 or K-Exaone?

GPT-5.4 scores higher for coding on the public lane, 54.7 to 45.2. 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, GPT-5.4 or K-Exaone?

GPT-5.4 scores higher for agentic tasks on the public lane, 53.4 to 46. K-Exaone is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.4 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, GPT-5.4 or K-Exaone?

GPT-5.4 has the larger documented context window: 1.05M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 22, 2026

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GPT-5.4 vs K-Exaone

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

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

Math

Shape of the matched evidence

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

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Questions

Which is better, GPT-5.4 or K-Exaone?

Which is better for coding, GPT-5.4 or K-Exaone?

Which is better for agentic tasks, GPT-5.4 or K-Exaone?

Which costs less, GPT-5.4 or K-Exaone?

Which has the larger context window, GPT-5.4 or K-Exaone?

Related comparisons

Watch GPT-5.4 vs K-Exaone