Enter opens the highlighted result. Esc closes.

Muse Spark Open vs Step 3.5 Flash

Updated October 21, 2026. 3 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Muse Spark OpenMeta
Coding
47.4
Overall
No data
Output $/M
$4.25
3
Step 3.5 FlashStepFun
Coding
No data
Overall
17.0
Output $/M
$0.30
0
vs boards won of 3 shared

Decision reading

Only Muse Spark Open has a coding score, 47.4 on the Vibe Coding Index; Step 3.5 Flash has none, so the two cannot be ranked on coding.

Step 3.5 Flash has the edge where output tokens cost 14.2x less.

Shared results
3
Muse Spark Open only
23
Step 3.5 Flash only
32
Like-for-like categories
2 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
Muse Spark Open 47.4 from 4 benchmarks, Step 3.5 Flash not benchmarked.
Widest category
General, 15.3 points apart on average over 1 shared benchmark.
Widest single gap
Humanity's Last Exam (Artificial Analysis run): Muse Spark Open 43.9% against Step 3.5 Flash 24.5%, 19.4 points apart on a 0 to 100 scale.
Output price
Muse Spark Open $4.25 against Step 3.5 Flash $0.30 per million output tokens, 14.2x apart.
Context window
Muse Spark Open takes 1.05M tokens, Step 3.5 Flash 262K.

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
CategoryMuse Spark OpenBarsStep 3.5 FlashTies
Reasoning20
General10
All shared boards30
CodingDirectional onlySix of these boards feed the Vibe Coding Index12 boards, none shared
Coding benchmarks for Muse Spark Open and Step 3.5 Flash
BenchmarkMuse Spark OpenStep 3.5 FlashLeader
LiveBench coding73.6%Not measuredOnly Muse Spark Open measured
LMArena WebDev1528Not measuredOnly Muse Spark Open measured
Terminal-Bench 4 (Vals AI)6.6%Not measuredOnly Muse Spark Open measured
Vibe Code Bench (Vals AI)76.2%Not measuredOnly Muse Spark Open measured
Code migration (Vals AI)21.2%Not measuredOnly Muse Spark Open measured
SciCode58.4%Not measuredOnly Muse Spark Open measured
Terminal-Bench Hard (Artificial Analysis run)Not measured32.6%Only Step 3.5 Flash measured
ALE-Bench1590.1Not measuredOnly Muse Spark Open measured
LMArena Text: codingNot measured1436Only Step 3.5 Flash measured
LMArena Text: software and ITNot measured1434Only Step 3.5 Flash measured
LMArena WebDev: React1529Not measuredOnly Muse Spark Open measured
LMArena WebDev: HTML1495Not measuredOnly Muse Spark Open measured
Neither model has a result on the other 26 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for Muse Spark Open and Step 3.5 Flash
BenchmarkMuse Spark OpenStep 3.5 FlashLeader
AA Intelligence IndexNot measured17.0Only Step 3.5 Flash measured
Epoch Capabilities Index155.1Not measuredOnly Muse Spark Open measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Agentic work benchmarks for Muse Spark Open and Step 3.5 Flash
BenchmarkMuse Spark OpenStep 3.5 FlashLeader
LiveBench research80.3%Not measuredOnly Muse Spark Open measured
LiveBench browser36.0%Not measuredOnly Muse Spark Open measured
LiveBench long agentic18.9%Not measuredOnly Muse Spark Open measured
Finance Agent (Vals AI)61.3%Not measuredOnly Muse Spark Open measured
Tax Agent Bench (Vals AI)26.3%Not measuredOnly Muse Spark Open measured
Legal Research Bench (Vals AI)38.5%Not measuredOnly Muse Spark Open measured
Excel Modeling Benchmark (Vals AI)53.7%Not measuredOnly Muse Spark Open measured
τ²-Bench TelecomNot measured94.4%Only Step 3.5 Flash measured
APEX-Agents33.4%Not measuredOnly Muse Spark Open measured
Neither model has a result on the other 30 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
Reasoning benchmarks for Muse Spark Open and Step 3.5 Flash
BenchmarkMuse Spark OpenStep 3.5 FlashLeader
LiveBench reasoning73.7%Not measuredOnly Muse Spark Open measured
GPQA Diamond (Artificial Analysis run)89.0%83.1%Muse Spark Open
Humanity's Last Exam (Artificial Analysis run)43.9%24.5%Muse Spark Open
LMArena Text: hard promptsNot measured1411Only Step 3.5 Flash measured
LMArena Text: expertNot measured1421Only Step 3.5 Flash measured
LMArena Text: hard prompts in EnglishNot measured1421Only Step 3.5 Flash measured
Neither model has a result on the other 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Math benchmarks for Muse Spark Open and Step 3.5 Flash
BenchmarkMuse Spark OpenStep 3.5 FlashLeader
LMArena Text: mathNot measured1408Only Step 3.5 Flash measured
LMArena Text: mathematical workNot measured1421Only Step 3.5 Flash measured
Neither model has a result on the other 11 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Knowledge benchmarks for Muse Spark Open and Step 3.5 Flash
BenchmarkMuse Spark OpenStep 3.5 FlashLeader
LMArena Text: scienceNot measured1415Only Step 3.5 Flash measured
LMArena Text: medicine and healthcareNot measured1408Only Step 3.5 Flash measured
LMArena Text: legal and governmentNot measured1408Only Step 3.5 Flash measured
LMArena Text: business and financeNot measured1394Only Step 3.5 Flash measured
Neither model has a result on the other 13 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Web design benchmarks for Muse Spark Open and Step 3.5 Flash
BenchmarkMuse Spark OpenStep 3.5 FlashLeader
Design Arena: websites1313Not measuredOnly Muse Spark Open measured
Design Arena: game dev1303Not measuredOnly Muse Spark Open measured
LiveBench design60.3%Not measuredOnly Muse Spark Open measured
Neither model has a result on the other 6 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
General benchmarks for Muse Spark Open and Step 3.5 Flash
BenchmarkMuse Spark OpenStep 3.5 FlashLeader
LiveBench data analysis60.2%Not measuredOnly Muse Spark Open measured
IFBenchNot measured66.5%Only Step 3.5 Flash measured
Long context reasoning (AA-LCR)78.3%63.0%Muse Spark Open
LMArena TextNot measured1403Only Step 3.5 Flash measured
LMArena Text: instruction followingNot measured1385Only Step 3.5 Flash measured
LMArena Text: creative writingNot measured1357Only Step 3.5 Flash measured
LMArena Text: EnglishNot measured1417Only Step 3.5 Flash measured
LMArena Text: non-EnglishNot measured1385Only Step 3.5 Flash measured
LMArena Text: ChineseNot measured1446Only Step 3.5 Flash measured
LMArena Text: GermanNot measured1405Only Step 3.5 Flash measured
LMArena Text: JapaneseNot measured1353Only Step 3.5 Flash measured
LMArena Text: KoreanNot measured1351Only Step 3.5 Flash measured
LMArena Text: FrenchNot measured1421Only Step 3.5 Flash measured
LMArena Text: SpanishNot measured1419Only Step 3.5 Flash measured
LMArena Text: RussianNot measured1385Only Step 3.5 Flash measured
LMArena Text: PolishNot measured1389Only Step 3.5 Flash measured
LMArena Text: multi-turnNot measured1404Only Step 3.5 Flash measured
LMArena Text: longer queriesNot measured1402Only Step 3.5 Flash measured
LMArena Text: writing and languageNot measured1372Only Step 3.5 Flash measured
LMArena Text: entertainment and mediaNot measured1360Only Step 3.5 Flash measured
Neither model has a result on the other 26 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Muse Spark Open and Step 3.5 Flash
BenchmarkMuse Spark OpenStep 3.5 FlashLeader
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

Muse Spark Open$0.0034Fits in one request
Step 3.5 Flash$0.0003Fits in one request

Step 3.5 Flash costs less

Repository review

50K input + 3K output tokens

Muse Spark Open$0.075Fits in one request
Step 3.5 Flash$0.0059Fits in one request

Step 3.5 Flash costs less

Agent loop

200K input + 10K output tokens

Muse Spark Open$0.292Fits in one request
Step 3.5 Flash$0.023Fits in one request

Step 3.5 Flash 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 Muse Spark Open and Step 3.5 Flash
SpecificationMeta Muse Spark OpenStepFun Step 3.5 Flash
MakerMetaStepFun
WeightsOpen weightsOpen weights
ReleasedOctober 15, 2026January 29, 2026
Context window1.05M262K
Input $/Mtok$1.25$0.10
Output $/Mtok$4.25$0.30
Coding index47.4Not benchmarked
Independent measurements behind it40
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Muse Spark Open better than Step 3.5 Flash for coding?

Only Muse Spark Open has a coding score, 47.4 on the Vibe Coding Index; Step 3.5 Flash has none, so the two cannot be ranked on coding. Step 3.5 Flash has the edge where output tokens cost 14.2x less.

Which is cheaper, Muse Spark Open or Step 3.5 Flash?

Muse Spark Open costs $1.25 per million input tokens and $4.25 per million output tokens; Step 3.5 Flash costs $0.10 and $0.30. Cost per solved task is known for neither of them.

Which has the bigger context window, Muse Spark Open or Step 3.5 Flash?

Muse Spark Open, with 1.05M tokens against 262K.

How many benchmarks is the Muse Spark Open vs Step 3.5 Flash comparison based on?

3 benchmarks have a result for both models as of October 21, 2026. Muse Spark Open's coding index rests on 4 independent benchmarks and Step 3.5 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.

Compare next