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DeepSeek V3.2 vs Muse Spark 1.3

Updated October 21, 2026. 49 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V3.2 Replace Muse Spark 1.3
DeepSeek V3.2DeepSeek
Coding
54.4
Overall
21.5
Output $/M
$0.42
0
Muse Spark 1.3Meta
Coding
79.7
Overall
48.1
Output $/M
$4.25
49
vs boards won of 49 shared

Decision reading

Pick Muse Spark 1.3 for coding: it scores 79.7 on the Vibe Coding Index against 54.4 for DeepSeek V3.2.

Pick DeepSeek V3.2 when output tokens cost 10.1x less.

Model ADeepSeek V3.2DeepSeek54.4/1002 independent benchmarks$0.99 per solved taskModel BMuse Spark 1.3Meta79.7/1002 independent benchmarksNo cost per solved task
Shared results
49
DeepSeek V3.2 only
26
Muse Spark 1.3 only
43
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
DeepSeek V3.2 54.4 from 2 benchmarks, Muse Spark 1.3 79.7 from 2 benchmarks.
Widest category
Agentic work, 37.3 points apart on average over 1 shared benchmark.
Widest single gap
Vibe Code Bench (Vals AI): DeepSeek V3.2 5.1% against Muse Spark 1.3 82.9%, 77.8 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.42 against Muse Spark 1.3 $4.25 per million output tokens, 10.1x apart.
Context window
DeepSeek V3.2 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 V3.2BarsMuse Spark 1.3Ties
Coding07
Intelligence02
Agentic work01
Reasoning05
Math02
Knowledge04
Web design08
General020
All shared boards049
CodingLike-for-likeFour of these boards feed the Vibe Coding Index07
Coding benchmarks for DeepSeek V3.2 and Muse Spark 1.3
BenchmarkDeepSeek V3.2Muse Spark 1.3Leader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LiveBench codingNot measured90.2%Only Muse Spark 1.3 measured
LMArena WebDev13621659Muse Spark 1.3
AA Coding Index44.275.8Muse Spark 1.3
LiveCodeBench (Vals AI)80.7%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Verified (Vals AI)67.6%Not measuredOnly DeepSeek V3.2 measured
Terminal-Bench 4 (Vals AI)Not measured10.6%Only Muse Spark 1.3 measured
Vibe Code Bench (Vals AI)5.1%82.9%Muse Spark 1.3
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
LiveCodeBench (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
SciCodeNot measured59.7%Only Muse Spark 1.3 measured
Terminal-Bench Hard (Artificial Analysis run)35.6%Not measuredOnly DeepSeek V3.2 measured
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
FrontierSWENot measured25.8%Only Muse Spark 1.3 measured
ALE-BenchNot measured1688.3Only Muse Spark 1.3 measured
LMArena Text: coding14531510Muse Spark 1.3
LMArena Text: software and IT14491511Muse Spark 1.3
LMArena WebDev: React13501672Muse Spark 1.3
LMArena WebDev: HTML13711653Muse Spark 1.3
LMArena WebDev: from an imageNot measured1640Only Muse Spark 1.3 measured
Neither model has a result on the other 13 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V3.2 and Muse Spark 1.3
BenchmarkDeepSeek V3.2Muse Spark 1.3Leader
AA Intelligence Index21.548.1Muse Spark 1.3
Epoch Capabilities Index146.1157.2Muse Spark 1.3
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.2 and Muse Spark 1.3
BenchmarkDeepSeek V3.2Muse 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 measured1217Only Muse Spark 1.3 measured
Design Arena: agents building Godot gamesNot measured1241Only Muse Spark 1.3 measured
Design Arena: PowerPoint slidesNot measured1244Only Muse Spark 1.3 measured
Design Arena: spreadsheetsNot measured1048Only 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
τ²-Bench Telecom90.6%Not measuredOnly DeepSeek V3.2 measured
APEX-Agents21.3%58.6%Muse Spark 1.3
Vending-Bench 2$1,034Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 21 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for DeepSeek V3.2 and Muse Spark 1.3
BenchmarkDeepSeek V3.2Muse Spark 1.3Leader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
LiveBench reasoningNot measured89.7%Only Muse Spark 1.3 measured
GPQA Diamond (Artificial Analysis run)84.0%94.1%Muse Spark 1.3
Humanity's Last Exam (Artificial Analysis run)24.6%48.7%Muse Spark 1.3
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 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 prompts14341503Muse Spark 1.3
LMArena Text: expert14361519Muse Spark 1.3
LMArena Text: hard prompts in English14461500Muse Spark 1.3
LMArena Vision: diagramsNot measured1330Only Muse Spark 1.3 measured
LMArena Vision: homeworkNot measured1296Only Muse Spark 1.3 measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V3.2 and Muse Spark 1.3
BenchmarkDeepSeek V3.2Muse Spark 1.3Leader
AIME84.6%Not measuredOnly DeepSeek V3.2 measured
MGSM (Vals AI)86.0%Not measuredOnly DeepSeek V3.2 measured
AA Math Index92.0Not measuredOnly DeepSeek V3.2 measured
AIME 2025 (Artificial Analysis run)92.0%Not measuredOnly DeepSeek V3.2 measured
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: math14351497Muse Spark 1.3
LMArena Text: mathematical work14401491Muse Spark 1.3
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for DeepSeek V3.2 and Muse Spark 1.3
BenchmarkDeepSeek V3.2Muse Spark 1.3Leader
MMLU Pro84.9%Not measuredOnly DeepSeek V3.2 measured
LegalBench (Vals AI)76.1%Not measuredOnly DeepSeek V3.2 measured
CaseLaw (Vals AI)55.4%Not measuredOnly DeepSeek V3.2 measured
CorpFin (Vals AI)51.0%Not measuredOnly DeepSeek V3.2 measured
TaxEval (Vals AI)68.2%Not measuredOnly DeepSeek V3.2 measured
MedQA (Vals AI)93.9%Not measuredOnly DeepSeek V3.2 measured
MMLU Pro (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: science14461507Muse Spark 1.3
LMArena Text: medicine and healthcare14401493Muse Spark 1.3
LMArena Text: legal and government14251499Muse Spark 1.3
LMArena Text: business and finance14101490Muse Spark 1.3
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for DeepSeek V3.2 and Muse Spark 1.3
BenchmarkDeepSeek V3.2Muse Spark 1.3Leader
Design Arena: websites11751356Muse Spark 1.3
Design Arena: UI components11491352Muse Spark 1.3
Design Arena: game dev11321329Muse Spark 1.3
Design Arena: data viz11581343Muse Spark 1.3
Design Arena: 3D11331359Muse Spark 1.3
Design Arena: all code categories11641353Muse Spark 1.3
Design Arena: SVG10321300Muse Spark 1.3
Design Arena: ASCII art10821280Muse Spark 1.3
LiveBench designNot measured74.8%Only Muse Spark 1.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for DeepSeek V3.2 and Muse Spark 1.3
BenchmarkDeepSeek V3.2Muse Spark 1.3Leader
LiveBench data analysisNot measured79.6%Only Muse Spark 1.3 measured
IFBench60.7%Not measuredOnly DeepSeek V3.2 measured
Long context reasoning (AA-LCR)73.3%83.0%Muse Spark 1.3
DTBench85.6%96.5%Muse Spark 1.3
LMCA28.8%53.9%Muse Spark 1.3
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251490Muse Spark 1.3
LMArena Text: instruction following14131479Muse Spark 1.3
LMArena Text: creative writing14001462Muse Spark 1.3
LMArena VisionNot measured1308Only Muse Spark 1.3 measured
LMArena Text: English14391495Muse Spark 1.3
LMArena Text: non-English14091481Muse Spark 1.3
LMArena Text: Chinese14551532Muse Spark 1.3
LMArena Text: German14131513Muse Spark 1.3
LMArena Text: Japanese13741470Muse Spark 1.3
LMArena Text: Korean13701490Muse Spark 1.3
LMArena Text: French14291511Muse Spark 1.3
LMArena Text: Spanish14241496Muse Spark 1.3
LMArena Text: Russian14241491Muse Spark 1.3
LMArena Text: Polish14191490Muse Spark 1.3
LMArena Text: multi-turn14271487Muse Spark 1.3
LMArena Text: longer queries14281488Muse Spark 1.3
LMArena Text: writing and language14071470Muse Spark 1.3
LMArena Text: entertainment and media13951457Muse Spark 1.3
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 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 and Muse Spark 1.3
BenchmarkDeepSeek V3.2Muse 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 V3.2$0.0005Fits in one request
Muse Spark 1.3$0.0034Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.015Fits in one request
Muse Spark 1.3$0.075Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.060Exceeds the context window
Muse Spark 1.3$0.292Fits in one request

DeepSeek V3.2 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 V3.2 and Muse Spark 1.3
SpecificationDeepSeek V3.2Meta Muse Spark 1.3
MakerDeepSeekMeta
WeightsOpen weightsNo public weights listed
ReleasedDecember 1, 2025September 2, 2026
Context window164K1.05M
Input $/Mtok$0.28$1.25
Output $/Mtok$0.42$4.25
Coding index54.479.7
Independent measurements behind it22
Cost per solved task$0.99Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than Muse Spark 1.3 for coding?

Pick Muse Spark 1.3 for coding: it scores 79.7 on the Vibe Coding Index against 54.4 for DeepSeek V3.2. Pick DeepSeek V3.2 when output tokens cost 10.1x less.

Which is cheaper, DeepSeek V3.2 or Muse Spark 1.3?

DeepSeek V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; Muse Spark 1.3 costs $1.25 and $4.25. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, DeepSeek V3.2 or Muse Spark 1.3?

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

How many benchmarks is the DeepSeek V3.2 vs Muse Spark 1.3 comparison based on?

49 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 2 independent benchmarks and Muse Spark 1.3's on 2. Where only one model has a result, the page says so instead of naming a winner.

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<iframe src="https://llmbenchmark.ai/embed/compare/deepseek-v3-2-vs-meta-muse-spark-1-3" title="DeepSeek V3.2 vs Muse Spark 1.3, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/deepseek-v3-2-vs-meta-muse-spark-1-3">LLMBenchmark.ai, DeepSeek V3.2 vs Muse Spark 1.3</a></p>