GPT-5.2 vs Muse Spark

Updated October 17, 2026. Rank says Muse Spark is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Muse Spark has the higher public score estimate, 60.47 versus 59.86, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 8 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-5.2

OpenAI

59.86/100

Supported · Public rank #56

90% interval 52.7–67.0

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.

Coding work
Code generation, repair, and software-engineering tasks
Muse Spark
Muse Spark has the higher public coding point estimate, 49.9 to 36.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Muse Spark
Muse Spark has the higher public agentic point estimate, 51 to 44.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.2
GPT-5.2 has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

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

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.

36.8GPT-5.249.9Muse Spark

Like-for-like · BenchAlign v5.8

Muse Spark 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

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-2Reasoning
Normalized gap 10.4
GPT-5.2:52.9%
Muse Spark:42.5%
GPT-5.2 source Muse Spark source
CharXivMultimodal
Normalized gap 4.3
GPT-5.2:82.1%
Muse Spark:86.4%
GPT-5.2 source Muse Spark source
SWE-bench ProCoding
Normalized gap 3.2
GPT-5.2:55.6%
Muse Spark:52.4%
GPT-5.2 source Muse Spark source
SWE-bench VerifiedCoding
Normalized gap 2.6
GPT-5.2:80%
Muse Spark:77.4%
GPT-5.2 source Muse Spark source
MMMU-ProMultimodal
Normalized gap 0.9
GPT-5.2:79.5%
Muse Spark:80.4%
GPT-5.2 source Muse Spark 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, 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

Like-for-like

GPT-5.2: 44.4; Supported · #58/128
Muse Spark: 51.0; Supported · #46/128
Basis: BenchAlign v5.8 lane · 4 vs 5 public rows
Reading: Muse Spark leads · intervals overlap

Coding

Like-for-like

GPT-5.2: 36.8; Supported · #73/150
Muse Spark: 49.9; Supported · #44/150
Basis: BenchAlign v5.8 lane · 3 vs 5 public rows
Reading: Muse Spark leads · intervals overlap

Multimodal

Like-for-like

GPT-5.2: 75.9; #22/54
Muse Spark: 84.4; #12/54
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Muse Spark leads

Knowledge

Like-for-like

GPT-5.2: 59.7; Supported · #50/184
Muse Spark: 62.5; Supported · #44/184
Basis: BenchAlign v5.8 lane · 1 vs 7 public rows
Reading: Muse Spark leads · intervals overlap

Instruction following

Directional only

GPT-5.2: 92.6; #19/148
Muse Spark: 93.3; #10/148
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.2: 64.3; Unranked · 4 rankable rows
Muse Spark: 57.1; Unranked · 3 rankable rows
Basis: Provisional lane · 1 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.2: Not ranked
Muse Spark: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.2: 71.1; Unranked · 3 rankable rows
Muse Spark: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.2 and Muse Spark
Category	GPT-5.2	Muse Spark	Basis	Reading
Agentic	44.4Supported · #58/128	51.0Supported · #46/128	Like-for-likeBenchAlign v5.8 lane · 4 vs 5 public rows	Muse Spark leads · intervals overlap
Coding	36.8Supported · #73/150	49.9Supported · #44/150	Like-for-likeBenchAlign v5.8 lane · 3 vs 5 public rows	Muse Spark leads · intervals overlap
Multimodal	75.9#22/54	84.4#12/54	Like-for-likeProvisional lane · 2 vs 2 weighted rows	Muse Spark leads
Knowledge	59.7Supported · #50/184	62.5Supported · #44/184	Like-for-likeBenchAlign v5.8 lane · 1 vs 7 public rows	Muse Spark leads · intervals overlap
Instruction following	92.6#19/148	93.3#10/148	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	64.3Unranked · 4 rankable rows	57.1Unranked · 3 rankable rows	Not comparableProvisional lane · 1 vs 1 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	71.1Unranked · 3 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 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

GPT-5.2: $0.00875; 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-5.2: $0.1295; 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-5.2: $0.525; 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-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Muse Spark 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.

SpecificationGPT-5.2Muse Spark

Context window

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

GPT-5.2

400K

Muse Spark

262K

API model ID

GPT-5.2

Not sourced

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-5.2

Not published

Muse Spark

No comparable hosted API rate

Documented inputs

GPT-5.2

Not sourced

Muse Spark

Not sourced

Documented outputs

GPT-5.2

Not sourced

Muse Spark

Not sourced

Provider availability

GPT-5.2

Not sourced

Muse Spark

Not sourced

Reasoning profile

GPT-5.2

Reasoning

Muse Spark

Reasoning

Weight access

GPT-5.2

Proprietary

Muse Spark

Proprietary

License

GPT-5.2

Proprietary

Muse Spark

Proprietary

Release date

GPT-5.2

2025-12-11

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, 60.47 versus 59.86, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-5.2 has the larger documented window (400K).

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

Questions

Which is better, GPT-5.2 or Muse Spark?

Muse Spark has the higher public score estimate, 60.47 versus 59.86, 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-5.2 or Muse Spark?

Muse Spark has the higher public coding point estimate, 49.9 to 36.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, GPT-5.2 or Muse Spark?

Muse Spark has the higher public agentic tasks point estimate, 51 to 44.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, GPT-5.2 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-5.2 or Muse Spark?

GPT-5.2 has the larger documented context window: 400K, compared with 262K.

Benchmark evidence

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

Browse raw public benchmark evidence48 rows

Agentic

BrowseComp
GPT-5.265.8%
Source
Muse Spark—
Not directly comparable
OSWorld-Verified
GPT-5.247.3%
Source
Muse Spark—
Not directly comparable
Gert Labs
GPT-5.246.54%
Source
Muse Spark—
Not directly comparable
JobBench
GPT-5.234.3%
Source
Muse Spark—
Not directly comparable
Terminal-Bench 2.0
GPT-5.2—
Muse Spark59%
Source
Not directly comparable
τ²-bench results
GPT-5.2—
Muse Spark91.5%
Source
Not directly comparable
DeepSearchQA
GPT-5.2—
Muse Spark74.8%
Source
Not directly comparable
CyberGym
GPT-5.2—
Muse Spark43.5%
Source
Not directly comparable
Claw-Eval
GPT-5.2—
Muse Spark63.8%
Source
Not directly comparable

Coding

SWE-bench Verified
GPT-5.280%
Source
Muse Spark77.4%
Source
GPT-5.2 leads this result
SWE-bench Pro
GPT-5.255.6%
Source
Muse Spark52.4%
Source
GPT-5.2 leads this result
Vibe Code Bench
Shared source
GPT-5.253.50%
Muse Spark19.67%
GPT-5.2 leads this result
LiveCodeBench Pro
GPT-5.2—
Muse Spark80.0%
Source
Not directly comparable
SWE-bench (Vals)
GPT-5.2—
Muse Spark74.4%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.252.9%
Source
Muse Spark42.5%
Source
GPT-5.2 leads this result
ARC-AGI-1
GPT-5.286.17%
Source
Muse Spark—
Not directly comparable

Multimodal

MMMU
GPT-5.286.7%
Source
Muse Spark—
Not directly comparable
MMMU-Pro
GPT-5.279.5%
Source
Muse Spark80.4%
Source
Muse Spark leads this result
RealWorldQA
GPT-5.283.3%
Source
Muse Spark—
Not directly comparable
OmniDocBench 1.5
GPT-5.285.7%
Source
Muse Spark—
Not directly comparable
Video-MME (with subtitle)
GPT-5.286.0%
Source
Muse Spark—
Not directly comparable
Video-MME (w/o subtitle)
GPT-5.285.8%
Source
Muse Spark—
Not directly comparable
MathVision
GPT-5.283.0%
Source
Muse Spark—
Not directly comparable
We-Math
GPT-5.279.0%
Source
Muse Spark—
Not directly comparable
DynaMath
GPT-5.286.8%
Source
Muse Spark—
Not directly comparable
SimpleVQA
GPT-5.255.8%
Source
Muse Spark71.3%
Source
Muse Spark leads this result
CC-OCR
GPT-5.270.3%
Source
Muse Spark—
Not directly comparable
AI2D_TEST
GPT-5.292.2%
Source
Muse Spark—
Not directly comparable
CountBench
GPT-5.291.9%
Source
Muse Spark—
Not directly comparable
ERQA
GPT-5.259.8%
Source
Muse Spark64.7%
Source
Muse Spark leads this result
VideoMMMU
GPT-5.285.9%
Source
Muse Spark—
Not directly comparable
MLVU (M-Avg)
GPT-5.285.6%
Source
Muse Spark—
Not directly comparable
CharXiv
GPT-5.282.1%
Source
Muse Spark86.4%
Source
Muse Spark leads this result
V*
GPT-5.275.9%
Source
Muse Spark—
Not directly comparable
ScreenSpot Pro
GPT-5.2—
Muse Spark84.1%
Source
Not directly comparable
ZeroBench
GPT-5.2—
Muse Spark33.0%
Source
Not directly comparable
MedXpertQA (MM)
GPT-5.2—
Muse Spark78.4%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.292.4%
Source
Muse Spark—
Not directly comparable
GPQA-D
GPT-5.2—
Muse Spark89.5%
Source
Not directly comparable
HLE
GPT-5.2—
Muse Spark50.4%
Source
Not directly comparable
HLE w/o tools
GPT-5.2—
Muse Spark42.8%
Source
Not directly comparable
HealthBench Hard
GPT-5.2—
Muse Spark42.8%
Source
Not directly comparable
MedXpertQA (Text)
GPT-5.2—
Muse Spark52.6%
Source
Not directly comparable
GPQA Diamond (Vals)
GPT-5.2—
Muse Spark89.6%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-5.2—
Muse Spark87.3%
Source
Not directly comparable

Math

AIME 2025
GPT-5.2100%
Source
Muse Spark—
Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.267.400%
Source
Muse Spark—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.231.700%
Source
Muse Spark—
Not directly comparable

48 public results · 8 shared

Related comparisons

Compare API pricing Read the methodology Open the model selector

Watch GPT-5.2 vs Muse Spark

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

Read a sample issue

Join 5,600+ readers.

Last updated October 17, 2026

GPT-5.2 vs Muse Spark

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Multimodal

Knowledge

Instruction following

Reasoning

Multilingual

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, GPT-5.2 or Muse Spark?

Which is better for coding, GPT-5.2 or Muse Spark?

Which is better for agentic tasks, GPT-5.2 or Muse Spark?

Which costs less, GPT-5.2 or Muse Spark?

Which has the larger context window, GPT-5.2 or Muse Spark?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GPT-5.2 vs Muse Spark