Coding
Like-for-like- GPT-5.2
- 70.6
- Muse Spark
- 67.8
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.2 leads
GPT-5.2 vs Muse Spark
Updated August 22, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Muse Spark has the higher public score estimate, 70.6 versus 57.96, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
8 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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
GPT-5.2
GPT-5.2 leads on the same 2 weighted benchmark rows.
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
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.
7 GPT-5.2 only
8 Shared
16 Muse Spark only
- Shared results
- 8
- GPT-5.2 only
- 7
- Muse Spark only
- 16
- Like-for-like categories
- 4 / 8
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-5.2
- 52.9
- Muse Spark
- 42.5
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.2 leads
Math
Like-for-like- GPT-5.2
- 35.2
- Muse Spark
- 32.9
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.2 leads
Multimodal
Like-for-like- GPT-5.2
- 80.4
- Muse Spark
- 82.5
- Weighted basis
- 2 vs 2 rows
- Reading
- Muse Spark leads
Agentic
Not comparable- GPT-5.2
- 55.7
- Muse Spark
- 59.0
- Weighted basis
- 2 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.2
- 92.4
- Muse Spark
- 50.4
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.2
- Not measured
- Muse Spark
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.2 | Muse Spark | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 70.6 | 67.8 | Like-for-like2 vs 2 rows | GPT-5.2 leads |
| Reasoning | 52.9 | 42.5 | Like-for-like1 vs 1 rows | GPT-5.2 leads |
| Math | 35.2 | 32.9 | Like-for-like2 vs 2 rows | GPT-5.2 leads |
| Multimodal | 80.4 | 82.5 | Like-for-like2 vs 2 rows | Muse Spark leads |
| Agentic | 55.7 | 59.0 | Not comparable2 vs 1 rows | Not comparable |
| Knowledge | 92.4 | 50.4 | Not comparable1 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.
ARC-AGI-2
Reasoning
CharXiv
Multimodal
FrontierMath v2 (Tier 4)
Math
SWE-bench Pro
Coding
SWE-bench Verified
Coding
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.
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, 70.6 versus 57.96, 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.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence31 rows
Agentic
BrowseComp
GPT-5.265.8%SourceMuse Spark—Not directly comparable
OSWorld-Verified
GPT-5.247.3%SourceMuse Spark—Not directly comparable
Gert Labs
GPT-5.246.54%SourceMuse Spark—Not directly comparable
JobBench
GPT-5.234.3%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
GPT-5.2—Muse Spark59%SourceNot directly comparable
τ²-bench results
GPT-5.2—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
GPT-5.2—Muse Spark74.8%SourceNot directly comparable
CyberGym
GPT-5.2—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
GPT-5.2—Muse Spark63.8%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-5.280%SourceMuse Spark77.4%SourceGPT-5.2 leads this result
SWE-bench Pro
GPT-5.255.6%SourceMuse Spark52.4%SourceGPT-5.2 leads this result
Vibe Code Bench
Shared sourceGPT-5.253.50%Muse Spark19.67%GPT-5.2 leads this result
LiveCodeBench Pro
GPT-5.2—Muse Spark80.0%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.292.4%SourceMuse Spark—Not directly comparable
GPQA-D
GPT-5.2—Muse Spark89.5%SourceNot directly comparable
HLE
GPT-5.2—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
GPT-5.2—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
GPT-5.2—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
GPT-5.2—Muse Spark52.6%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-5.240.700%Muse Spark39.000%GPT-5.2 leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-5.218.800%Muse Spark14.600%GPT-5.2 leads this result
Multimodal
MMMU-Pro
GPT-5.279.5%SourceMuse Spark80.4%SourceMuse Spark leads this result
MathVision
GPT-5.283.0%SourceMuse Spark—Not directly comparable
CharXiv
GPT-5.282.1%SourceMuse Spark86.4%SourceMuse Spark leads this result
V*
GPT-5.275.9%SourceMuse Spark—Not directly comparable
ERQA
GPT-5.2—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
GPT-5.2—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
GPT-5.2—Muse Spark84.1%SourceNot directly comparable
ZeroBench
GPT-5.2—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
GPT-5.2—Muse Spark78.4%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.2 or Muse Spark?
Muse Spark has the higher public score estimate, 70.6 versus 57.96, 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?
GPT-5.2 leads the like-for-like coding comparison across 2 shared weighted benchmark rows.
Which is better for agentic tasks, GPT-5.2 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-5.2 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.
Related comparisons
Last updated August 22, 2026
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