Model A

OpenAI

66.4/100

Supported · Public rank #30

90% interval 55.5–77.3

GPT-5.4 nano vs Muse Spark

Updated August 21, 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 nano
GPT-5.4 nano 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
The category averages use different weighted benchmark sets, so they are 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

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 nano only

8 Shared

16 Muse Spark only

Shared results: 8
GPT-5.4 nano only: 5
Muse Spark only: 16
Like-for-like categories: 1 / 8

3 categories use 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.

Math

Like-for-like

GPT-5.4 nano: 21.0
Muse Spark: 32.9
Weighted basis: 2 vs 2 rows
Reading: Muse Spark leads

Agentic

Directional only

GPT-5.4 nano: 42.9
Muse Spark: 59.0
Weighted basis: 2 vs 1 rows
Reading: Directional only

Knowledge

Directional only

GPT-5.4 nano: 43.8
Muse Spark: 50.4
Weighted basis: 2 vs 1 rows
Reading: Directional only

Multimodal

Directional only

GPT-5.4 nano: 66.1
Muse Spark: 82.5
Weighted basis: 1 vs 2 rows
Reading: Directional only

Coding

Not comparable

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

Reasoning

Not comparable

GPT-5.4 nano: Not measured
Muse Spark: 42.5
Weighted basis: 0 vs 1 rows
Reading: Not comparable

Multilingual

Not comparable

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

Instruction following

Not comparable

GPT-5.4 nano: 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 nano and Muse Spark
Category	GPT-5.4 nano	Muse Spark	Weighted basis	Reading
Math	21.0	32.9	Like-for-like2 vs 2 rows	Muse Spark leads
Agentic	42.9	59.0	Directional only2 vs 1 rows	Directional only
Knowledge	43.8	50.4	Directional only2 vs 1 rows	Directional only
Multimodal	66.1	82.5	Directional only1 vs 2 rows	Directional only
Coding	Not measured	67.8	Not comparable0 vs 2 rows	Not comparable
Reasoning	Not measured	42.5	Not comparable0 vs 1 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.

MMMU-Pro
Multimodal
GPT-5.4 nano: 66.1%Muse Spark: 80.4%Normalized gap 14.3GPT-5.4 nano source Muse Spark source
FrontierMath v2 (Tiers 1-3)
Math
GPT-5.4 nano: 25.860%Muse Spark: 39.000%Normalized gap 13.1Shared source
Terminal-Bench 2.0
Agentic
GPT-5.4 nano: 46.3%Muse Spark: 59%Normalized gap 12.7GPT-5.4 nano source Muse Spark source
HLE
Knowledge
GPT-5.4 nano: 37.7%Muse Spark: 50.4%Normalized gap 12.7GPT-5.4 nano source Muse Spark source
FrontierMath v2 (Tier 4)
Math
GPT-5.4 nano: 6.250%Muse Spark: 14.600%Normalized gap 8.3Shared 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 nano: $0.00082; 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 nano: $0.01375; 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 nano: $0.0205; Fits in one request
Muse Spark: API rate not published; Fits in one request; Cached-input rate unavailable

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 nanoMuse Spark

Context window

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

GPT-5.4 nano

400K

OpenAI GPT-5.4 nano model documentation

Muse Spark

262K

API model ID

GPT-5.4 nano

gpt-5.4-nano

OpenAI GPT-5.4 nano 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 nano

$0.02 per 1M cached input tokens

OpenAI pricing

Muse Spark

No comparable hosted API rate

Documented inputs

GPT-5.4 nano

text, image

OpenAI model catalog

Muse Spark

Not sourced

Documented outputs

GPT-5.4 nano

text

OpenAI model catalog

Muse Spark

Not sourced

Provider availability

GPT-5.4 nano

Generally Available · OpenAI Responses API

OpenAI model catalog

Muse Spark

Not sourced

Reasoning profile

GPT-5.4 nano

Reasoning

Muse Spark

Reasoning

Weight access

GPT-5.4 nano

Proprietary

Muse Spark

Proprietary

License

GPT-5.4 nano

Proprietary

Muse Spark

Proprietary

Release date

GPT-5.4 nano

2026-03-17

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 66.38, 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 nano has the larger documented window (400K).

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

Terminal-Bench 2.0
GPT-5.4 nano46.3%
Source
Muse Spark59%
Source
Muse Spark leads this result
OSWorld-Verified
GPT-5.4 nano39%
Source
Muse Spark—
Not directly comparable
MCP Atlas
GPT-5.4 nano56.1%
Source
Muse Spark—
Not directly comparable
Toolathlon
GPT-5.4 nano35.5%
Source
Muse Spark—
Not directly comparable
τ²-bench results
GPT-5.4 nano92.5%
Source
Muse Spark91.5%
Source
GPT-5.4 nano leads this result
DeepSearchQA
GPT-5.4 nano—
Muse Spark74.8%
Source
Not directly comparable
CyberGym
GPT-5.4 nano—
Muse Spark43.5%
Source
Not directly comparable
Claw-Eval
GPT-5.4 nano—
Muse Spark63.8%
Source
Not directly comparable

Coding

Vibe Code Bench
Shared source
GPT-5.4 nano26.10%
Muse Spark19.67%
GPT-5.4 nano leads this result
SWE-bench Verified
GPT-5.4 nano—
Muse Spark77.4%
Source
Not directly comparable
SWE-bench Pro
GPT-5.4 nano—
Muse Spark52.4%
Source
Not directly comparable
LiveCodeBench Pro
GPT-5.4 nano—
Muse Spark80.0%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.4 nano—
Muse Spark42.5%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.4 nano82.8%
Source
Muse Spark—
Not directly comparable
HLE
GPT-5.4 nano37.7%
Source
Muse Spark50.4%
Source
Muse Spark leads this result
HLE w/o tools
GPT-5.4 nano24.3%
Source
Muse Spark42.8%
Source
Muse Spark leads this result
GPQA-D
GPT-5.4 nano—
Muse Spark89.5%
Source
Not directly comparable
HealthBench Hard
GPT-5.4 nano—
Muse Spark42.8%
Source
Not directly comparable
MedXpertQA (Text)
GPT-5.4 nano—
Muse Spark52.6%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Shared source
GPT-5.4 nano25.860%
Muse Spark39.000%
Muse Spark leads this result
FrontierMath v2 (Tier 4)
Shared source
GPT-5.4 nano6.250%
Muse Spark14.600%
Muse Spark leads this result

Multimodal

MMMU-Pro
GPT-5.4 nano66.1%
Source
Muse Spark80.4%
Source
Muse Spark leads this result
MMMU-Pro w/ Python
GPT-5.4 nano69.5%
Source
Muse Spark—
Not directly comparable
CharXiv
GPT-5.4 nano—
Muse Spark86.4%
Source
Not directly comparable
ERQA
GPT-5.4 nano—
Muse Spark64.7%
Source
Not directly comparable
SimpleVQA
GPT-5.4 nano—
Muse Spark71.3%
Source
Not directly comparable
ScreenSpot Pro
GPT-5.4 nano—
Muse Spark84.1%
Source
Not directly comparable
ZeroBench
GPT-5.4 nano—
Muse Spark33.0%
Source
Not directly comparable
MedXpertQA (MM)
GPT-5.4 nano—
Muse Spark78.4%
Source
Not directly comparable

Frequently asked questions

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

Muse Spark has the higher public score estimate, 70.6 versus 66.38, 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 nano 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 nano or Muse Spark?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

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

GPT-5.4 nano has the larger documented context window: 400K, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 21, 2026

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GPT-5.4 nano 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

Math

Agentic

Knowledge

Multimodal

Coding

Reasoning

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 nano or Muse Spark?

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

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

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

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

Related comparisons

Watch GPT-5.4 nano vs Muse Spark