Model A

OpenAI

61.2/100

Estimated · Public rank #54

90% interval 44.3–78.1

GPT-5.4 Pro vs Muse Spark

Updated August 22, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Muse Spark

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 Pro
GPT-5.4 Pro 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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: rate-fallback
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

What is actually comparable

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

5 GPT-5.4 Pro only

6 Shared

18 Muse Spark only

Shared results: 6
GPT-5.4 Pro only: 5
Muse Spark only: 18
Like-for-like categories: 3 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Reasoning

Like-for-like

GPT-5.4 Pro: 83.3
Muse Spark: 42.5
Weighted basis: 1 vs 1 rows
Reading: GPT-5.4 Pro leads

Knowledge

Like-for-like

GPT-5.4 Pro: 58.7
Muse Spark: 50.4
Weighted basis: 1 vs 1 rows
Reading: GPT-5.4 Pro leads

Math

Like-for-like

GPT-5.4 Pro: 46.9
Muse Spark: 32.9
Weighted basis: 2 vs 2 rows
Reading: GPT-5.4 Pro leads

Multimodal

Directional only

GPT-5.4 Pro: 94.0
Muse Spark: 82.5
Weighted basis: 1 vs 2 rows
Reading: Directional only

Agentic

Not comparable

GPT-5.4 Pro: 89.3
Muse Spark: 59.0
Weighted basis: 1 vs 1 rows
Reading: Not comparable

Coding

Not comparable

GPT-5.4 Pro: Not measured
Muse Spark: 67.8
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.4 Pro: Not measured
Muse Spark: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Instruction following

Not comparable

GPT-5.4 Pro: Not measured
Muse Spark: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for GPT-5.4 Pro and Muse Spark
Category	GPT-5.4 Pro	Muse Spark	Weighted basis	Reading
Reasoning	83.3	42.5	Like-for-like1 vs 1 rows	GPT-5.4 Pro leads
Knowledge	58.7	50.4	Like-for-like1 vs 1 rows	GPT-5.4 Pro leads
Math	46.9	32.9	Like-for-like2 vs 2 rows	GPT-5.4 Pro leads
Multimodal	94.0	82.5	Directional only1 vs 2 rows	Directional only
Agentic	89.3	59.0	Not comparable1 vs 1 rows	Not comparable
Coding	Not measured	67.8	Not comparable0 vs 2 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable

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.

ARC-AGI-2
Reasoning
GPT-5.4 Pro: 83.3%Muse Spark: 42.5%Normalized gap 40.8GPT-5.4 Pro source Muse Spark source
FrontierMath v2 (Tier 4)
Math
GPT-5.4 Pro: 37.500%Muse Spark: 14.600%Normalized gap 22.9Shared source
MMMU-Pro
Multimodal
GPT-5.4 Pro: 94%Muse Spark: 80.4%Normalized gap 13.6GPT-5.4 Pro source Muse Spark source
FrontierMath v2 (Tiers 1-3)
Math
GPT-5.4 Pro: 50.000%Muse Spark: 39.000%Normalized gap 11.0Shared source
HLE
Knowledge
GPT-5.4 Pro: 58.7%Muse Spark: 50.4%Normalized gap 8.3GPT-5.4 Pro source Muse Spark source

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 Pro: $0.12; Fits in one request
Muse Spark: API rate not published; Fits in one request

Muse Spark has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.4 Pro: $2.04; Fits in one request
Muse Spark: API rate not published; Fits in one request

Muse Spark has no comparable published API token rate.

Cache-heavy agent loop

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

GPT-5.4 Pro: $8.40; Fits in one request; Cached input priced at the published list-input rate
Muse Spark: API rate not published; Fits in one request; Cached-input rate unavailable

GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark 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-5.4 ProMuse Spark

Context window

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

GPT-5.4 Pro

1.05M

OpenAI GPT-5.4 Pro model documentation

Muse Spark

262K

API model ID

GPT-5.4 Pro

gpt-5.4-pro

OpenAI GPT-5.4 Pro model documentation

Muse Spark

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 Pro

Not published

OpenAI pricing

Muse Spark

No comparable hosted API rate

Documented inputs

GPT-5.4 Pro

text, image

OpenAI model catalog

Muse Spark

Not sourced

Documented outputs

GPT-5.4 Pro

text

OpenAI model catalog

Muse Spark

Not sourced

Provider availability

GPT-5.4 Pro

Generally Available · OpenAI Responses API

OpenAI model catalog

Muse Spark

Not sourced

Reasoning profile

GPT-5.4 Pro

Reasoning

Muse Spark

Reasoning

Weight access

GPT-5.4 Pro

Proprietary

Muse Spark

Proprietary

License

GPT-5.4 Pro

Proprietary

Muse Spark

Proprietary

Release date

GPT-5.4 Pro

2026-03-05

Muse Spark

2026-04-08

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: Muse Spark has the higher public score estimate, 70.6 versus 61.24, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.4 Pro 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 evidence29 rows

Agentic

BrowseComp
GPT-5.4 Pro89.3%
Source
Muse Spark—
Not directly comparable
Terminal-Bench 2.0
GPT-5.4 Pro—
Muse Spark59%
Source
Not directly comparable
τ²-bench results
GPT-5.4 Pro—
Muse Spark91.5%
Source
Not directly comparable
DeepSearchQA
GPT-5.4 Pro—
Muse Spark74.8%
Source
Not directly comparable
CyberGym
GPT-5.4 Pro—
Muse Spark43.5%
Source
Not directly comparable
Claw-Eval
GPT-5.4 Pro—
Muse Spark63.8%
Source
Not directly comparable

Coding

SWE-bench Verified
GPT-5.4 Pro—
Muse Spark77.4%
Source
Not directly comparable
SWE-bench Pro
GPT-5.4 Pro—
Muse Spark52.4%
Source
Not directly comparable
LiveCodeBench Pro
GPT-5.4 Pro—
Muse Spark80.0%
Source
Not directly comparable
Vibe Code Bench
GPT-5.4 Pro—
Muse Spark19.67%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.4 Pro83.3%
Source
Muse Spark42.5%
Source
GPT-5.4 Pro leads this result

Knowledge

HLE
GPT-5.4 Pro58.7%
Source
Muse Spark50.4%
Source
GPT-5.4 Pro leads this result
FrontierScience
GPT-5.4 Pro36.7%
Source
Muse Spark—
Not directly comparable
FrontierScience Research
GPT-5.4 Pro36.7%
Source
Muse Spark—
Not directly comparable
HLE w/o tools
GPT-5.4 Pro42.7%
Source
Muse Spark42.8%
Source
Muse Spark leads this result
GPQA-D
GPT-5.4 Pro—
Muse Spark89.5%
Source
Not directly comparable
HealthBench Hard
GPT-5.4 Pro—
Muse Spark42.8%
Source
Not directly comparable
MedXpertQA (Text)
GPT-5.4 Pro—
Muse Spark52.6%
Source
Not directly comparable

Math

IPhO 2025 (Theory)
GPT-5.4 Pro93.5%
Source
Muse Spark—
Not directly comparable
FrontierMath (legacy)
GPT-5.4 Pro50%
Source
Muse Spark—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared source
GPT-5.4 Pro50.000%
Muse Spark39.000%
GPT-5.4 Pro leads this result
FrontierMath v2 (Tier 4)
Shared source
GPT-5.4 Pro37.500%
Muse Spark14.600%
GPT-5.4 Pro leads this result

Multimodal

MMMU-Pro
GPT-5.4 Pro94%
Source
Muse Spark80.4%
Source
GPT-5.4 Pro leads this result
CharXiv
GPT-5.4 Pro—
Muse Spark86.4%
Source
Not directly comparable
ERQA
GPT-5.4 Pro—
Muse Spark64.7%
Source
Not directly comparable
SimpleVQA
GPT-5.4 Pro—
Muse Spark71.3%
Source
Not directly comparable
ScreenSpot Pro
GPT-5.4 Pro—
Muse Spark84.1%
Source
Not directly comparable
ZeroBench
GPT-5.4 Pro—
Muse Spark33.0%
Source
Not directly comparable
MedXpertQA (MM)
GPT-5.4 Pro—
Muse Spark78.4%
Source
Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 Pro or Muse Spark?

Muse Spark has the higher public score estimate, 70.6 versus 61.24, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-5.4 Pro or Muse Spark?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, GPT-5.4 Pro or Muse Spark?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, GPT-5.4 Pro or Muse Spark?

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 Pro or Muse Spark?

GPT-5.4 Pro has the larger documented context window: 1.05M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 22, 2026

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GPT-5.4 Pro vs Muse Spark

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

What is actually comparable

Category results, on a stated basis

Reasoning

Knowledge

Math

Multimodal

Agentic

Coding

Multilingual

Instruction following

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

Knowledge

Math

Multimodal

Frequently asked questions

Which is better, GPT-5.4 Pro or Muse Spark?

Which is better for coding, GPT-5.4 Pro or Muse Spark?

Which is better for agentic tasks, GPT-5.4 Pro or Muse Spark?

Which costs less, GPT-5.4 Pro or Muse Spark?

Which has the larger context window, GPT-5.4 Pro or Muse Spark?

Related comparisons

Watch GPT-5.4 Pro vs Muse Spark