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GPT-5.5 vs Muse Spark 1.1

Updated October 21, 2026. 95 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-5.5 Replace Muse Spark 1.1
GPT-5.5OpenAI
Coding
67.8
Overall
38.4
Output $/M
$30.00
58
Muse Spark 1.1Meta
Coding
48.9
Overall
33.7
Output $/M
$4.25
36
vs boards won of 95 shared, 1 tied

Decision reading

Pick GPT-5.5 for coding: it scores 67.8 on the Vibe Coding Index against 48.9 for Muse Spark 1.1.

Pick Muse Spark 1.1 when output tokens cost 7.1x less, or when a solved task costs $0.43 against $1.65.

Model AGPT-5.5OpenAI67.8/1002 independent benchmarks$1.65 per solved taskModel BMuse Spark 1.1Meta48.9/1001 independent benchmark$0.43 per solved task
Shared results
95
GPT-5.5 only
37
Muse Spark 1.1 only
1
Like-for-like categories
8 of 9

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

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-5.5 67.8 from 2 benchmarks, Muse Spark 1.1 48.9 from 1 benchmark.
Widest category
Agentic work, 8.3 points apart on average over 15 shared benchmarks.
Widest single gap
Surface Evolver Bench: GPT-5.5 88.1% against Muse Spark 1.1 52.5%, 35.6 points apart on a 0 to 100 scale.
Output price
GPT-5.5 $30.00 against Muse Spark 1.1 $4.25 per million output tokens, 7.1x apart.
Context window
GPT-5.5 takes 1.05M tokens, Muse Spark 1.1 1.05M.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGPT-5.5BarsMuse Spark 1.1Ties
Coding78
Intelligence20
Agentic work78
Reasoning91
Math30
Knowledge64
Web design161 tie
General239
All shared boards58361 tie
CodingLike-for-likeFour of these boards feed the Vibe Coding Index78
Coding benchmarks for GPT-5.5 and Muse Spark 1.1
BenchmarkGPT-5.5Muse Spark 1.1Leader
LiveBench coding85.1%Not measuredOnly GPT-5.5 measured
LMArena WebDev15131542Muse Spark 1.1
AA Coding Index74.971.3GPT-5.5
LiveCodeBench (Vals AI)85.3%85.9%Muse Spark 1.1
SWE-bench Verified (Vals AI)82.6%82.0%GPT-5.5
Vibe Code Bench (Vals AI)69.8%72.2%Muse Spark 1.1
Code migration (Vals AI)45.2%31.1%GPT-5.5
ProgramBench (Vals AI)0.5%0.0%GPT-5.5
Terminal-Bench 2.1 (Vals AI)76.4%69.3%GPT-5.5
SkillsBench (Vals AI)62.5%59.2%GPT-5.5
SciCode56.1%58.8%Muse Spark 1.1
Terminal-Bench Hard (Artificial Analysis run)60.6%Not measuredOnly GPT-5.5 measured
Terminal-Bench84.7%Not measuredOnly GPT-5.5 measured
FrontierCode43.0%Not measuredOnly GPT-5.5 measured
GSO-Bench40.2%Not measuredOnly GPT-5.5 measured
WeirdML84.9%Not measuredOnly GPT-5.5 measured
MirrorCode10.0%Not measuredOnly GPT-5.5 measured
ALE-Bench1943.0Not measuredOnly GPT-5.5 measured
LMArena Text: coding14941498Muse Spark 1.1
LMArena Text: software and IT14931498Muse Spark 1.1
LMArena WebDev: React15061532Muse Spark 1.1
LMArena WebDev: HTML15431540GPT-5.5
LMArena WebDev: from an image15321540Muse Spark 1.1
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GPT-5.5 and Muse Spark 1.1
BenchmarkGPT-5.5Muse Spark 1.1Leader
AA Intelligence Index38.433.7GPT-5.5
Epoch Capabilities Index159.7154.1GPT-5.5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index78
Agentic work benchmarks for GPT-5.5 and Muse Spark 1.1
BenchmarkGPT-5.5Muse Spark 1.1Leader
AA Agentic Index36.425.8GPT-5.5
Design Arena: agents building web apps11171207Muse Spark 1.1
Design Arena: agents building full-stack apps10891199Muse Spark 1.1
Design Arena: agents building mobile apps11431177Muse Spark 1.1
Design Arena: agents building Android apps11691184Muse Spark 1.1
Design Arena: agents building Godot games11731103GPT-5.5
Design Arena: agents building games11791183Muse Spark 1.1
Design Arena: HTML slides10671194Muse Spark 1.1
Design Arena: PowerPoint slides11081156Muse Spark 1.1
Design Arena: spreadsheetsNot measured974Only Muse Spark 1.1 measured
LiveBench research61.6%Not measuredOnly GPT-5.5 measured
LiveBench browser48.0%Not measuredOnly GPT-5.5 measured
LiveBench long agentic44.6%Not measuredOnly GPT-5.5 measured
SREBench (Vals AI)3.8%Not measuredOnly GPT-5.5 measured
Finance Agent (Vals AI)51.8%57.2%Muse Spark 1.1
Tax Agent Bench (Vals AI)21.8%Not measuredOnly GPT-5.5 measured
Legal Research Bench (Vals AI)40.4%38.0%GPT-5.5
Excel Modeling Benchmark (Vals AI)64.5%56.4%GPT-5.5
Time Horizon Index (Vals AI)8.3%1.7%GPT-5.5
τ²-Bench Telecom93.9%Not measuredOnly GPT-5.5 measured
APEX-Agents55.1%31.8%GPT-5.5
Remote Labor Index6.3%Not measuredOnly GPT-5.5 measured
OSWorld 2.013.0%Not measuredOnly GPT-5.5 measured
DeepResearch Bench54.0%Not measuredOnly GPT-5.5 measured
PostTrainBench27.2%Not measuredOnly GPT-5.5 measured
Vending-Bench 2$7,524$6,520GPT-5.5
Neither model has a result on the other 13 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index91
Reasoning benchmarks for GPT-5.5 and Muse Spark 1.1
BenchmarkGPT-5.5Muse Spark 1.1Leader
GPQA Diamond93.2%91.2%GPT-5.5
LiveBench reasoning89.7%Not measuredOnly GPT-5.5 measured
MMMU Pro (Vals AI)88.3%86.6%GPT-5.5
GPQA Diamond (Artificial Analysis run)93.5%89.8%GPT-5.5
Humanity's Last Exam (Artificial Analysis run)45.8%46.2%Muse Spark 1.1
GPQA Diamond (Epoch AI run)90.7%Not measuredOnly GPT-5.5 measured
ARC-AGI95.0%Not measuredOnly GPT-5.5 measured
ARC-AGI-285.0%Not measuredOnly GPT-5.5 measured
SimpleBench69.0%Not measuredOnly GPT-5.5 measured
Chess puzzles63.0%34.0%GPT-5.5
Mystery game puzzles56.0%Not measuredOnly GPT-5.5 measured
EBR-bench34.3%Not measuredOnly GPT-5.5 measured
LMArena Text: hard prompts14891485GPT-5.5
LMArena Text: expert15071478GPT-5.5
LMArena Text: hard prompts in English14831480GPT-5.5
LMArena Vision: diagrams13251305GPT-5.5
LMArena Vision: homework13321287GPT-5.5
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GPT-5.5 and Muse Spark 1.1
BenchmarkGPT-5.5Muse Spark 1.1Leader
OTIS Mock AIME84.4%Not measuredOnly GPT-5.5 measured
FrontierMath, tiers 1 to 385.3%Not measuredOnly GPT-5.5 measured
FrontierMath, tier 472.5%Not measuredOnly GPT-5.5 measured
ProofBench50.0%39.0%GPT-5.5
LMArena Text: math14871482GPT-5.5
LMArena Text: mathematical work14961487GPT-5.5
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index64
Knowledge benchmarks for GPT-5.5 and Muse Spark 1.1
BenchmarkGPT-5.5Muse Spark 1.1Leader
MMLU Pro88.1%88.7%Muse Spark 1.1
LegalBench (Vals AI)86.5%85.0%GPT-5.5
CaseLaw (Vals AI)66.2%Not measuredOnly GPT-5.5 measured
CorpFin (Vals AI)68.4%71.3%Muse Spark 1.1
TaxEval (Vals AI)75.0%79.7%Muse Spark 1.1
MortgageTax (Vals AI)68.8%66.1%GPT-5.5
MedCode (Vals AI)49.1%Not measuredOnly GPT-5.5 measured
SimpleQA Verified63.0%57.8%GPT-5.5
LMArena Text: science14851484GPT-5.5
LMArena Text: medicine and healthcare14651479Muse Spark 1.1
LMArena Text: legal and government14911481GPT-5.5
LMArena Text: business and finance14781470GPT-5.5
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index16
Web design benchmarks for GPT-5.5 and Muse Spark 1.1
BenchmarkGPT-5.5Muse Spark 1.1Leader
Design Arena: websites12561270Muse Spark 1.1
Design Arena: UI components12601290Muse Spark 1.1
Design Arena: game dev12941269GPT-5.5
Design Arena: data viz12651268Muse Spark 1.1
Design Arena: 3D11981260Muse Spark 1.1
Design Arena: all code categories12601276Muse Spark 1.1
Design Arena: SVG12341234Tie
Design Arena: ASCII art12541270Muse Spark 1.1
LiveBench design70.3%Not measuredOnly GPT-5.5 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index239
General benchmarks for GPT-5.5 and Muse Spark 1.1
BenchmarkGPT-5.5Muse Spark 1.1Leader
LiveBench data analysis81.6%Not measuredOnly GPT-5.5 measured
Public Benefits Bench (Vals AI)60.9%61.3%Muse Spark 1.1
SAGE (Vals AI)51.5%45.6%GPT-5.5
MedScribe (Vals AI)86.9%88.9%Muse Spark 1.1
Vals Multimodal Index68.1%66.7%GPT-5.5
IFBench75.9%Not measuredOnly GPT-5.5 measured
Long context reasoning (AA-LCR)84.3%77.7%GPT-5.5
DTBench96.0%94.4%GPT-5.5
LMCA54.3%49.9%GPT-5.5
Surface Evolver Bench88.1%52.5%GPT-5.5
Furniture assembly44.2%Not measuredOnly GPT-5.5 measured
ExploitBench47.4%Not measuredOnly GPT-5.5 measured
CL-bench Life22.2%Not measuredOnly GPT-5.5 measured
LMArena Text14711478Muse Spark 1.1
LMArena Text: instruction following14781457GPT-5.5
LMArena Text: creative writing14551435GPT-5.5
LMArena Vision12971293GPT-5.5
LMArena Text: English14701478Muse Spark 1.1
LMArena Text: non-English14671471Muse Spark 1.1
LMArena Text: Chinese15341515GPT-5.5
LMArena Text: German14801469GPT-5.5
LMArena Text: Japanese14991450GPT-5.5
LMArena Text: Korean14601459GPT-5.5
LMArena Text: French14851492Muse Spark 1.1
LMArena Text: Spanish14681464GPT-5.5
LMArena Text: Russian14731482Muse Spark 1.1
LMArena Text: Polish14651499Muse Spark 1.1
LMArena Text: multi-turn14761484Muse Spark 1.1
LMArena Text: longer queries14841462GPT-5.5
LMArena Text: writing and language14661448GPT-5.5
LMArena Text: entertainment and media14491438GPT-5.5
LMArena Vision: English12921292GPT-5.5
LMArena Vision: Chinese13601348GPT-5.5
LMArena Vision: reading text13061301GPT-5.5
LMArena Vision: humor12931288GPT-5.5
LMArena Vision: recognizing things1231Not measuredOnly GPT-5.5 measured
LMArena Vision: creative writing13011253GPT-5.5
LMArena Document14861465GPT-5.5
Neither model has a result on the other 8 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5.5 and Muse Spark 1.1
BenchmarkGPT-5.5Muse Spark 1.1Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-5.5$0.020Fits in one request
Muse Spark 1.1$0.0034Fits in one request

Muse Spark 1.1 costs less

Repository review

50K input + 3K output tokens

GPT-5.5$0.340Fits in one request
Muse Spark 1.1$0.075Fits in one request

Muse Spark 1.1 costs less

Agent loop

200K input + 10K output tokens

GPT-5.5$1.30Fits in one request
Muse Spark 1.1$0.292Fits in one request

Muse Spark 1.1 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-5.5 and Muse Spark 1.1
SpecificationOpenAI GPT-5.5Meta Muse Spark 1.1
MakerOpenAIMeta
WeightsNo public weights listedNo public weights listed
ReleasedApril 24, 2026July 16, 2026
Context window1.05M1.05M
Input $/Mtok$5.00$1.25
Output $/Mtok$30.00$4.25
Coding index67.848.9
Independent measurements behind it21
Cost per solved task$1.65$0.43
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.5 better than Muse Spark 1.1 for coding?

Pick GPT-5.5 for coding: it scores 67.8 on the Vibe Coding Index against 48.9 for Muse Spark 1.1. Pick Muse Spark 1.1 when output tokens cost 7.1x less, or when a solved task costs $0.43 against $1.65.

Which is cheaper, GPT-5.5 or Muse Spark 1.1?

GPT-5.5 costs $5.00 per million input tokens and $30.00 per million output tokens; Muse Spark 1.1 costs $1.25 and $4.25. Per solved SWE-bench Verified task, GPT-5.5 costs $1.65 and Muse Spark 1.1 $0.43.

Which has the bigger context window, GPT-5.5 or Muse Spark 1.1?

GPT-5.5, with 1.05M tokens against 1.05M.

How many benchmarks is the GPT-5.5 vs Muse Spark 1.1 comparison based on?

95 benchmarks have a result for both models as of October 21, 2026. GPT-5.5's coding index rests on 2 independent benchmarks and Muse Spark 1.1's on 1. Where only one model has a result, the page says so instead of naming a winner.

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