Long documents
Prompts that approach the documented context limit
GPT-4.1
GPT-4.1 has the larger documented context window.
Confidence: documented
GPT-4.1 vs Muse Spark
Updated October 5, 2026. Rank says Muse Spark is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
Muse Spark has the higher public score estimate, 59.99 versus 37.85, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 3
- GPT-4.1 only
- 5
- Muse Spark only
- 24
- Like-for-like categories
- 1 / 8
Supported: GPT-4.1 and Muse SparkHow the comparison works
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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-4.1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-4.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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-fallbackRepository 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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
—GPT-4.151.1Muse Spark
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
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-4.1 only
3 Shared
24 Muse Spark only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 33.5GPT-4.1:5.517%Muse Spark:39.000%SWE-bench VerifiedCoding
Normalized gap 22.8GPT-4.1:54.6%Muse Spark:77.4%FrontierMath v2 (Tier 4)Math
Normalized gap 14.6GPT-4.1:0.000%Muse Spark:14.600%
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.
Knowledge
Like-for-like- GPT-4.1
- 35.9
- Supported · #113/171
- Muse Spark
- 60.3
- Supported · #40/171
- Basis
- BenchAlign v5.8 lane · 2 vs 7 public rows
- Reading
- Muse Spark leads
Instruction following
Directional only- GPT-4.1
- 48.9
- #82/125
- Muse Spark
- 91.9
- #8/125
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-4.1
- Not ranked
- Muse Spark
- 49.8
- Supported · #43/119
- Basis
- BenchAlign v5.8 lane · 1 vs 5 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-4.1
- Not ranked
- Muse Spark
- 51.1
- Supported · #39/144
- Basis
- BenchAlign v5.8 lane · 1 vs 5 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-4.1
- 69.2
- Unranked · 2 rankable rows
- Muse Spark
- 53.4
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-4.1
- 51.3
- Unranked · 1 rankable row
- Muse Spark
- 78.5
- #14/49
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-4.1
- 61.5
- #13/16
- Muse Spark
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-4.1
- 28.0
- Unranked · 2 rankable rows
- Muse Spark
- 55.1
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
| Category | GPT-4.1 | Muse Spark | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 35.9Supported · #113/171 | 60.3Supported · #40/171 | Like-for-likeBenchAlign v5.8 lane · 2 vs 7 public rows | Muse Spark leads |
| Instruction following | 48.9#82/125 | 91.9#8/125 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 49.8Supported · #43/119 | Not comparableBenchAlign v5.8 lane · 1 vs 5 public rows | Not comparable |
| Coding | Not ranked | 51.1Supported · #39/144 | Not comparableBenchAlign v5.8 lane · 1 vs 5 public rows | Not comparable |
| Reasoning | 69.2Unranked · 2 rankable rows | 53.4Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multimodal | 51.3Unranked · 1 rankable row | 78.5#14/49 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | 61.5#13/16 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 28.0Unranked · 2 rankable rows | 55.1Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 2 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-4.1
- $0.006
- 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-4.1
- $0.124
- 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-4.1
- $0.52
- 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-4.1 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-4.1Muse Spark
Context window
Maximum documented context; output-token limits may be lower.
GPT-4.1
1M
Muse Spark
262K
API model ID
GPT-4.1
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-4.1
Not published
Muse Spark
No comparable hosted API rate
Documented inputs
GPT-4.1
Not sourced
Muse Spark
Not sourced
Documented outputs
GPT-4.1
Not sourced
Muse Spark
Not sourced
Provider availability
GPT-4.1
Not sourced
Muse Spark
Not sourced
Reasoning profile
GPT-4.1
Non-Reasoning
Muse Spark
Reasoning
Weight access
GPT-4.1
Proprietary
Muse Spark
Proprietary
License
GPT-4.1
Proprietary
Muse Spark
Proprietary
Release date
GPT-4.1
2025-04-14
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, 59.99 versus 37.85, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-4.1 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-4.1 or Muse Spark?
Muse Spark has the higher public score estimate, 59.99 versus 37.85, 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-4.1 or Muse Spark?
GPT-4.1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-4.1 or Muse Spark?
GPT-4.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-4.1 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-4.1 or Muse Spark?
GPT-4.1 has the larger documented context window: 1M, 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 evidence32 rows
Agentic
Gert Labs
GPT-4.125.65%SourceMuse Spark—Not directly comparable
Terminal-Bench 2.0
GPT-4.1—Muse Spark59%SourceNot directly comparable
τ²-bench results
GPT-4.1—Muse Spark91.5%SourceNot directly comparable
DeepSearchQA
GPT-4.1—Muse Spark74.8%SourceNot directly comparable
CyberGym
GPT-4.1—Muse Spark43.5%SourceNot directly comparable
Claw-Eval
GPT-4.1—Muse Spark63.8%SourceNot directly comparable
Coding
SWE-bench Verified
GPT-4.154.6%SourceMuse Spark77.4%SourceMuse Spark leads this result
SWE-bench Pro
GPT-4.1—Muse Spark52.4%SourceNot directly comparable
LiveCodeBench Pro
GPT-4.1—Muse Spark80.0%SourceNot directly comparable
Vibe Code Bench
GPT-4.1—Muse Spark19.67%SourceNot directly comparable
SWE-bench (Vals)
GPT-4.1—Muse Spark74.4%SourceNot directly comparable
Reasoning
ARC-AGI-2
GPT-4.1—Muse Spark42.5%SourceNot directly comparable
Multimodal
CharXiv
GPT-4.1—Muse Spark86.4%SourceNot directly comparable
MMMU-Pro
GPT-4.1—Muse Spark80.4%SourceNot directly comparable
ERQA
GPT-4.1—Muse Spark64.7%SourceNot directly comparable
SimpleVQA
GPT-4.1—Muse Spark71.3%SourceNot directly comparable
ScreenSpot Pro
GPT-4.1—Muse Spark84.1%SourceNot directly comparable
ZeroBench
GPT-4.1—Muse Spark33.0%SourceNot directly comparable
MedXpertQA (MM)
GPT-4.1—Muse Spark78.4%SourceNot directly comparable
Knowledge
MMLU
GPT-4.190.2%SourceMuse Spark—Not directly comparable
GPQA
GPT-4.166.3%SourceMuse Spark—Not directly comparable
GPQA-D
GPT-4.1—Muse Spark89.5%SourceNot directly comparable
HLE
GPT-4.1—Muse Spark50.4%SourceNot directly comparable
HLE w/o tools
GPT-4.1—Muse Spark42.8%SourceNot directly comparable
HealthBench Hard
GPT-4.1—Muse Spark42.8%SourceNot directly comparable
MedXpertQA (Text)
GPT-4.1—Muse Spark52.6%SourceNot directly comparable
GPQA Diamond (Vals)
GPT-4.1—Muse Spark89.6%SourceNot directly comparable
MMLU-Pro (Vals)
GPT-4.1—Muse Spark87.3%SourceNot directly comparable
Multilingual
MMLU-ProX
GPT-4.172.7%SourceMuse Spark—Not directly comparable
Instruction following
IFEval
GPT-4.187.4%SourceMuse Spark—Not directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Shared sourceGPT-4.15.517%Muse Spark39.000%Muse Spark leads this result
FrontierMath v2 (Tier 4)
Shared sourceGPT-4.10.000%Muse Spark14.600%Muse Spark leads this result
32 public results · 3 shared
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Last updated October 5, 2026