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DeepSeek V3.1 vs Muse Spark 1.3

Muse Spark 1.3 leads DeepSeek V3.1 on the Artificial Analysis Intelligence Index, 48.1 to 13.7, and DeepSeek V3.1 costs 2.8x less per output token ($1.50 vs $4.25 per million). Updated October 22, 2026; 10 benchmarks have a result for both.

DeepSeek V3.1DeepSeek
Coding
No data
Overall
No data
Output $/M
$1.50
0
Muse Spark 1.3Meta
Coding
57.4
Overall
68.6
Output $/M
$4.25
10
vs boards won of 10 shared

DeepSeek V3.1 or Muse Spark 1.3: which to pick

Only Muse Spark 1.3 has a coding score, 57.4 on the Vibe Coding Index; DeepSeek V3.1 has none, so the two cannot be ranked on coding.

Pick DeepSeek V3.1 for output tokens 2.8x cheaper ($1.50 vs $4.25 per million).

Shared results
10
DeepSeek V3.1 only
1
Muse Spark 1.3 only
82
Like-for-like categories
3 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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
DeepSeek V3.1 not benchmarked, Muse Spark 1.3 57.4 from 4 benchmarks.
Widest category
Intelligence, 34.4 points apart on average over 1 shared benchmark.
Widest single gap
Design Arena: SVG: DeepSeek V3.1 967 against Muse Spark 1.3 1301, 41.8 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.1 $1.50 against Muse Spark 1.3 $4.25 per million output tokens, 2.8x apart.
Context window
DeepSeek V3.1 takes 164K tokens, Muse Spark 1.3 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
CategoryDeepSeek V3.1BarsMuse Spark 1.3Ties
Intelligence01
Web design07
General02
All shared boards010
CodingDirectional onlySix of these boards feed the Vibe Coding Index18 boards, none shared
Coding benchmarks for DeepSeek V3.1 and Muse Spark 1.3
BenchmarkDeepSeek V3.1Muse Spark 1.3Leader
LiveBench codingNot measured90.2%Only Muse Spark 1.3 measured
LMArena WebDevNot measured1659Only Muse Spark 1.3 measured
AA Coding IndexNot measured75.8Only Muse Spark 1.3 measured
Terminal-Bench 4 (Vals AI)Not measured10.6%Only Muse Spark 1.3 measured
Vibe Code Bench (Vals AI)Not measured82.9%Only Muse Spark 1.3 measured
Code migration (Vals AI)Not measured27.6%Only Muse Spark 1.3 measured
ProgramBench (Vals AI)Not measured0.5%Only Muse Spark 1.3 measured
IOI (Vals AI)Not measured43.9%Only Muse Spark 1.3 measured
Terminal-Bench 2.1 (Vals AI)Not measured72.3%Only Muse Spark 1.3 measured
CyberBench (Vals AI)Not measured69.4%Only Muse Spark 1.3 measured
SciCodeNot measured59.7%Only Muse Spark 1.3 measured
FrontierSWENot measured25.8%Only Muse Spark 1.3 measured
ALE-BenchNot measured1688.3Only Muse Spark 1.3 measured
LMArena Text: codingNot measured1510Only Muse Spark 1.3 measured
LMArena Text: software and ITNot measured1511Only Muse Spark 1.3 measured
LMArena WebDev: ReactNot measured1672Only Muse Spark 1.3 measured
LMArena WebDev: HTMLNot measured1653Only Muse Spark 1.3 measured
LMArena WebDev: from an imageNot measured1640Only Muse Spark 1.3 measured
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for DeepSeek V3.1 and Muse Spark 1.3
BenchmarkDeepSeek V3.1Muse Spark 1.3Leader
AA Intelligence Index13.748.1Muse Spark 1.3
Epoch Capabilities IndexNot measured157.2Only Muse Spark 1.3 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index16 boards, none shared
Agentic work benchmarks for DeepSeek V3.1 and Muse Spark 1.3
BenchmarkDeepSeek V3.1Muse Spark 1.3Leader
AA Agentic IndexNot measured55.5Only Muse Spark 1.3 measured
Design Arena: agents building web appsNot measured1297Only Muse Spark 1.3 measured
Design Arena: agents building full-stack appsNot measured1295Only Muse Spark 1.3 measured
Design Arena: agents building mobile appsNot measured1216Only Muse Spark 1.3 measured
Design Arena: agents building Godot gamesNot measured1241Only Muse Spark 1.3 measured
Design Arena: PowerPoint slidesNot measured1220Only Muse Spark 1.3 measured
Design Arena: spreadsheetsNot measured1076Only Muse Spark 1.3 measured
LiveBench researchNot measured68.6%Only Muse Spark 1.3 measured
LiveBench browserNot measured80.0%Only Muse Spark 1.3 measured
LiveBench long agenticNot measured40.4%Only Muse Spark 1.3 measured
Finance Agent (Vals AI)Not measured58.9%Only Muse Spark 1.3 measured
Tax Agent Bench (Vals AI)Not measured36.0%Only Muse Spark 1.3 measured
Legal Research Bench (Vals AI)Not measured40.9%Only Muse Spark 1.3 measured
Excel Modeling Benchmark (Vals AI)Not measured62.7%Only Muse Spark 1.3 measured
Terminal-Bench-Science (Vals AI)Not measured4.3%Only Muse Spark 1.3 measured
APEX-AgentsNot measured58.6%Only Muse Spark 1.3 measured
Neither model has a result on the other 25 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
Reasoning benchmarks for DeepSeek V3.1 and Muse Spark 1.3
BenchmarkDeepSeek V3.1Muse Spark 1.3Leader
LiveBench reasoningNot measured89.7%Only Muse Spark 1.3 measured
GPQA Diamond (Artificial Analysis run)Not measured94.1%Only Muse Spark 1.3 measured
Humanity's Last Exam (Artificial Analysis run)Not measured48.7%Only Muse Spark 1.3 measured
Chess puzzlesNot measured37.0%Only Muse Spark 1.3 measured
Mystery game puzzlesNot measured25.0%Only Muse Spark 1.3 measured
LMArena Text: hard promptsNot measured1503Only Muse Spark 1.3 measured
LMArena Text: expertNot measured1519Only Muse Spark 1.3 measured
LMArena Text: hard prompts in EnglishNot measured1500Only Muse Spark 1.3 measured
LMArena Vision: diagramsNot measured1330Only Muse Spark 1.3 measured
LMArena Vision: homeworkNot measured1296Only Muse Spark 1.3 measured
Kagi LLM Benchmark53.2%Not measuredOnly DeepSeek V3.1 measured
Neither model has a result on the other 11 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for DeepSeek V3.1 and Muse Spark 1.3
BenchmarkDeepSeek V3.1Muse Spark 1.3Leader
OTIS Mock AIMENot measured99.2%Only Muse Spark 1.3 measured
FrontierMath, tiers 1 to 3Not measured74.4%Only Muse Spark 1.3 measured
FrontierMath, tier 4Not measured46.3%Only Muse Spark 1.3 measured
ProofBenchNot measured58.0%Only Muse Spark 1.3 measured
LMArena Text: mathNot measured1497Only Muse Spark 1.3 measured
LMArena Text: mathematical workNot measured1491Only Muse Spark 1.3 measured
Neither model has a result on the other 7 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Knowledge benchmarks for DeepSeek V3.1 and Muse Spark 1.3
BenchmarkDeepSeek V3.1Muse Spark 1.3Leader
LMArena Text: scienceNot measured1507Only Muse Spark 1.3 measured
LMArena Text: medicine and healthcareNot measured1493Only Muse Spark 1.3 measured
LMArena Text: legal and governmentNot measured1499Only Muse Spark 1.3 measured
LMArena Text: business and financeNot measured1490Only Muse Spark 1.3 measured
Neither model has a result on the other 14 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for DeepSeek V3.1 and Muse Spark 1.3
BenchmarkDeepSeek V3.1Muse Spark 1.3Leader
Design Arena: websites11221356Muse Spark 1.3
Design Arena: UI components10831350Muse Spark 1.3
Design Arena: game dev10841328Muse Spark 1.3
Design Arena: data viz10961344Muse Spark 1.3
Design Arena: 3D10771358Muse Spark 1.3
Design Arena: all code categories11121352Muse Spark 1.3
Design Arena: SVG9671301Muse Spark 1.3
Design Arena: ASCII artNot measured1280Only Muse Spark 1.3 measured
LiveBench designNot measured74.8%Only Muse Spark 1.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for DeepSeek V3.1 and Muse Spark 1.3
BenchmarkDeepSeek V3.1Muse Spark 1.3Leader
LiveBench data analysisNot measured79.6%Only Muse Spark 1.3 measured
Long context reasoning (AA-LCR)Not measured83.0%Only Muse Spark 1.3 measured
DTBench82.7%96.5%Muse Spark 1.3
LMCA24.3%53.9%Muse Spark 1.3
LMArena TextNot measured1490Only Muse Spark 1.3 measured
LMArena Text: instruction followingNot measured1479Only Muse Spark 1.3 measured
LMArena Text: creative writingNot measured1462Only Muse Spark 1.3 measured
LMArena VisionNot measured1308Only Muse Spark 1.3 measured
LMArena Text: EnglishNot measured1495Only Muse Spark 1.3 measured
LMArena Text: non-EnglishNot measured1481Only Muse Spark 1.3 measured
LMArena Text: ChineseNot measured1532Only Muse Spark 1.3 measured
LMArena Text: GermanNot measured1513Only Muse Spark 1.3 measured
LMArena Text: JapaneseNot measured1470Only Muse Spark 1.3 measured
LMArena Text: KoreanNot measured1490Only Muse Spark 1.3 measured
LMArena Text: FrenchNot measured1511Only Muse Spark 1.3 measured
LMArena Text: SpanishNot measured1496Only Muse Spark 1.3 measured
LMArena Text: RussianNot measured1491Only Muse Spark 1.3 measured
LMArena Text: PolishNot measured1490Only Muse Spark 1.3 measured
LMArena Text: multi-turnNot measured1487Only Muse Spark 1.3 measured
LMArena Text: longer queriesNot measured1488Only Muse Spark 1.3 measured
LMArena Text: writing and languageNot measured1470Only Muse Spark 1.3 measured
LMArena Text: entertainment and mediaNot measured1457Only Muse Spark 1.3 measured
LMArena Vision: EnglishNot measured1298Only Muse Spark 1.3 measured
LMArena Vision: ChineseNot measured1384Only Muse Spark 1.3 measured
LMArena Vision: reading textNot measured1319Only Muse Spark 1.3 measured
LMArena Vision: humorNot measured1293Only Muse Spark 1.3 measured
LMArena Vision: creative writingNot measured1296Only Muse Spark 1.3 measured
Neither model has a result on the other 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.1 and Muse Spark 1.3
BenchmarkDeepSeek V3.1Muse Spark 1.3Leader
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

DeepSeek V3.1$0.0013Fits in one request
Muse Spark 1.3$0.0034Fits in one request

DeepSeek V3.1 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.1$0.032Fits in one request
Muse Spark 1.3$0.075Fits in one request

DeepSeek V3.1 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.1$0.125Exceeds the context window
Muse Spark 1.3$0.292Fits in one request

DeepSeek V3.1 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 DeepSeek V3.1 and Muse Spark 1.3
SpecificationDeepSeek V3.1Meta Muse Spark 1.3
MakerDeepSeekMeta
WeightsOpen weightsNo public weights listed
ReleasedAugust 21, 2025September 2, 2026
Context window164K1.05M
Input $/Mtok$0.55$1.25
Output $/Mtok$1.50$4.25
Coding indexNot benchmarked57.4
Independent measurements behind it04
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.1 better than Muse Spark 1.3 for coding?

Only Muse Spark 1.3 has a coding score, 57.4 on the Vibe Coding Index; DeepSeek V3.1 has none, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for output tokens 2.8x cheaper ($1.50 vs $4.25 per million).

Which is cheaper, DeepSeek V3.1 or Muse Spark 1.3?

DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; Muse Spark 1.3 costs $1.25 and $4.25. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V3.1 or Muse Spark 1.3?

Muse Spark 1.3, with 1.05M tokens against 164K.

How many benchmarks is the DeepSeek V3.1 vs Muse Spark 1.3 comparison based on?

10 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and Muse Spark 1.3's on 4. Where only one model has a result, the page says so instead of naming a winner.

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