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DeepSeek V3.2 vs Muse Spark 1.2

Muse Spark 1.2 leads DeepSeek V3.2 on coding, 55.7 to 30.0 on the Vibe Coding Index, and DeepSeek V3.2 costs 4.8x less per output token ($0.88 vs $4.25 per million). Updated October 22, 2026; 51 benchmarks have a result for both.

Replace DeepSeek V3.2 Replace Muse Spark 1.2
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
0
Muse Spark 1.2Meta
Coding
55.7
Overall
60.4
Output $/M
$4.25
51
vs boards won of 51 shared

DeepSeek V3.2 or Muse Spark 1.2: which to pick

Pick Muse Spark 1.2 for coding: it scores 55.7 on the Vibe Coding Index against 30.0 for DeepSeek V3.2.

Pick DeepSeek V3.2 for output tokens 4.8x cheaper ($0.88 vs $4.25 per million).

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel BMuse Spark 1.2Meta55.7/1005 independent benchmarks$0.64 per solved task
Shared results
51
DeepSeek V3.2 only
24
Muse Spark 1.2 only
46
Like-for-like categories
8 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 V3.2 30.0 from 3 benchmarks, Muse Spark 1.2 55.7 from 5 benchmarks.
Widest category
Coding, 27.7 points apart on average over 8 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): DeepSeek V3.2 5.1% against Muse Spark 1.2 79.1%, 74.0 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against Muse Spark 1.2 $4.25 per million output tokens, 4.8x apart.
Context window
DeepSeek V3.2 takes 164K tokens, Muse Spark 1.2 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 1.2Ties
Coding08
Intelligence02
Agentic work01
Reasoning05
Math02
Knowledge08
Web design08
General017
All shared boards051
CodingLike-for-likeSix of these boards feed the Vibe Coding Index08
Coding benchmarks for DeepSeek V3.2 and Muse Spark 1.2
BenchmarkDeepSeek V3.2Muse Spark 1.2Leader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LiveBench codingNot measured78.2%Only Muse Spark 1.2 measured
LMArena WebDev13621534Muse Spark 1.2
AA Coding Index44.272.2Muse Spark 1.2
LiveCodeBench (Vals AI)80.7%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Verified (Vals AI)67.6%86.6%Muse Spark 1.2
Terminal-Bench 4 (Vals AI)Not measured6.1%Only Muse Spark 1.2 measured
Vibe Code Bench (Vals AI)5.1%79.1%Muse Spark 1.2
Code migration (Vals AI)Not measured29.9%Only Muse Spark 1.2 measured
ProgramBench (Vals AI)Not measured0.5%Only Muse Spark 1.2 measured
IOI (Vals AI)Not measured21.8%Only Muse Spark 1.2 measured
Terminal-Bench 2.1 (Vals AI)Not measured69.7%Only Muse Spark 1.2 measured
SkillsBench (Vals AI)Not measured53.0%Only Muse Spark 1.2 measured
CyberBench (Vals AI)Not measured69.5%Only Muse Spark 1.2 measured
LiveCodeBench (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
SciCodeNot measured57.4%Only Muse Spark 1.2 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 measured12.0%Only Muse Spark 1.2 measured
WeirdMLNot measured60.3%Only Muse Spark 1.2 measured
LMArena Text: coding14531505Muse Spark 1.2
LMArena Text: software and IT14491505Muse Spark 1.2
LMArena WebDev: React13501526Muse Spark 1.2
LMArena WebDev: HTML13711543Muse Spark 1.2
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V3.2 and Muse Spark 1.2
BenchmarkDeepSeek V3.2Muse Spark 1.2Leader
AA Intelligence Index21.539.6Muse Spark 1.2
Epoch Capabilities Index146.1155.1Muse Spark 1.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.2 and Muse Spark 1.2
BenchmarkDeepSeek V3.2Muse Spark 1.2Leader
AA Agentic IndexNot measured43.2Only Muse Spark 1.2 measured
Design Arena: agents building web appsNot measured1221Only Muse Spark 1.2 measured
Design Arena: agents building full-stack appsNot measured1210Only Muse Spark 1.2 measured
Design Arena: agents building mobile appsNot measured1181Only Muse Spark 1.2 measured
Design Arena: agents building Android appsNot measured1222Only Muse Spark 1.2 measured
Design Arena: agents building gamesNot measured1179Only Muse Spark 1.2 measured
Design Arena: HTML slidesNot measured1085Only Muse Spark 1.2 measured
Design Arena: PowerPoint slidesNot measured1172Only Muse Spark 1.2 measured
LiveBench researchNot measured67.7%Only Muse Spark 1.2 measured
LiveBench browserNot measured36.0%Only Muse Spark 1.2 measured
LiveBench long agenticNot measured21.3%Only Muse Spark 1.2 measured
SREBench (Vals AI)Not measured0.0%Only Muse Spark 1.2 measured
Finance Agent (Vals AI)Not measured60.6%Only Muse Spark 1.2 measured
Tax Agent Bench (Vals AI)Not measured32.8%Only Muse Spark 1.2 measured
Legal Research Bench (Vals AI)Not measured43.8%Only Muse Spark 1.2 measured
Excel Modeling Benchmark (Vals AI)Not measured57.0%Only Muse Spark 1.2 measured
BioMysteryBench (Vals AI)Not measured64.8%Only Muse Spark 1.2 measured
τ²-Bench Telecom90.6%Not measuredOnly DeepSeek V3.2 measured
APEX-Agents21.3%45.4%Muse Spark 1.2
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 1.2
BenchmarkDeepSeek V3.2Muse Spark 1.2Leader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
LiveBench reasoningNot measured90.0%Only Muse Spark 1.2 measured
MMMU Pro (Vals AI)Not measured86.1%Only Muse Spark 1.2 measured
GPQA Diamond (Artificial Analysis run)84.0%90.4%Muse Spark 1.2
Humanity's Last Exam (Artificial Analysis run)24.6%45.5%Muse Spark 1.2
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
SimpleBenchNot measured74.5%Only Muse Spark 1.2 measured
Chess puzzlesNot measured27.0%Only Muse Spark 1.2 measured
LMArena Text: hard prompts14341489Muse Spark 1.2
LMArena Text: expert14361475Muse Spark 1.2
LMArena Text: hard prompts in English14461487Muse Spark 1.2
LMArena Vision: diagramsNot measured1317Only Muse Spark 1.2 measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V3.2 and Muse Spark 1.2
BenchmarkDeepSeek V3.2Muse Spark 1.2Leader
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
ProofBenchNot measured43.0%Only Muse Spark 1.2 measured
LMArena Text: math14351484Muse Spark 1.2
LMArena Text: mathematical work14401480Muse Spark 1.2
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index08
Knowledge benchmarks for DeepSeek V3.2 and Muse Spark 1.2
BenchmarkDeepSeek V3.2Muse Spark 1.2Leader
MMLU Pro84.9%88.3%Muse Spark 1.2
LegalBench (Vals AI)76.1%85.3%Muse Spark 1.2
CaseLaw (Vals AI)55.4%Not measuredOnly DeepSeek V3.2 measured
CorpFin (Vals AI)51.0%70.9%Muse Spark 1.2
TaxEval (Vals AI)68.2%80.4%Muse Spark 1.2
MortgageTax (Vals AI)Not measured65.4%Only Muse Spark 1.2 measured
MedQA (Vals AI)93.9%Not measuredOnly DeepSeek V3.2 measured
MedCode (Vals AI)Not measured49.4%Only Muse Spark 1.2 measured
MMLU Pro (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
SimpleQA VerifiedNot measured60.3%Only Muse Spark 1.2 measured
LMArena Text: science14461500Muse Spark 1.2
LMArena Text: medicine and healthcare14401496Muse Spark 1.2
LMArena Text: legal and government14251517Muse Spark 1.2
LMArena Text: business and finance14101484Muse Spark 1.2
Neither model has a result on the other 4 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 1.2
BenchmarkDeepSeek V3.2Muse Spark 1.2Leader
Design Arena: websites11751312Muse Spark 1.2
Design Arena: UI components11461298Muse Spark 1.2
Design Arena: game dev11311301Muse Spark 1.2
Design Arena: data viz11581334Muse Spark 1.2
Design Arena: 3D11321301Muse Spark 1.2
Design Arena: all code categories11631312Muse Spark 1.2
Design Arena: SVG10321284Muse Spark 1.2
Design Arena: ASCII art10831284Muse Spark 1.2
LiveBench designNot measured67.1%Only Muse Spark 1.2 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for DeepSeek V3.2 and Muse Spark 1.2
BenchmarkDeepSeek V3.2Muse Spark 1.2Leader
LiveBench data analysisNot measured76.5%Only Muse Spark 1.2 measured
Public Benefits Bench (Vals AI)Not measured68.5%Only Muse Spark 1.2 measured
SAGE (Vals AI)Not measured47.7%Only Muse Spark 1.2 measured
MedScribe (Vals AI)Not measured90.1%Only Muse Spark 1.2 measured
Vals Multimodal IndexNot measured69.8%Only Muse Spark 1.2 measured
IFBench60.7%Not measuredOnly DeepSeek V3.2 measured
Long context reasoning (AA-LCR)73.3%79.0%Muse Spark 1.2
DTBench85.6%94.7%Muse Spark 1.2
LMCA28.8%48.4%Muse Spark 1.2
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251482Muse Spark 1.2
LMArena Text: instruction following14131462Muse Spark 1.2
LMArena Text: creative writing14001436Muse Spark 1.2
LMArena VisionNot measured1304Only Muse Spark 1.2 measured
LMArena Text: English14391483Muse Spark 1.2
LMArena Text: non-English14091476Muse Spark 1.2
LMArena Text: Chinese14551509Muse Spark 1.2
LMArena Text: German1413Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Japanese1374Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Korean13701462Muse Spark 1.2
LMArena Text: French14291495Muse Spark 1.2
LMArena Text: Spanish14241484Muse Spark 1.2
LMArena Text: Russian14241483Muse Spark 1.2
LMArena Text: Polish1419Not measuredOnly DeepSeek V3.2 measured
LMArena Text: multi-turn14271502Muse Spark 1.2
LMArena Text: longer queries14281473Muse Spark 1.2
LMArena Text: writing and language14071453Muse Spark 1.2
LMArena Text: entertainment and media13951443Muse Spark 1.2
LMArena Vision: EnglishNot measured1291Only Muse Spark 1.2 measured
LMArena Vision: reading textNot measured1313Only Muse Spark 1.2 measured
Neither model has a result on the other 15 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 1.2
BenchmarkDeepSeek V3.2Muse Spark 1.2Leader
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$0.0007Fits in one request
Muse Spark 1.2$0.0034Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.017Fits in one request
Muse Spark 1.2$0.075Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.067Exceeds the context window
Muse Spark 1.2$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 1.2
SpecificationDeepSeek V3.2Meta Muse Spark 1.2
MakerDeepSeekMeta
WeightsOpen weightsNo public weights listed
ReleasedDecember 1, 2025August 5, 2026
Context window164K1.05M
Input $/Mtok$0.29$1.25
Output $/Mtok$0.88$4.25
Coding index30.055.7
Independent measurements behind it35
Cost per solved task$0.99$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than Muse Spark 1.2 for coding?

Pick Muse Spark 1.2 for coding: it scores 55.7 on the Vibe Coding Index against 30.0 for DeepSeek V3.2. Pick DeepSeek V3.2 for output tokens 4.8x cheaper ($0.88 vs $4.25 per million).

Which is cheaper, DeepSeek V3.2 or Muse Spark 1.2?

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Muse Spark 1.2 costs $1.25 and $4.25. Per solved SWE-bench Verified task, DeepSeek V3.2 costs $0.99 and Muse Spark 1.2 $0.64.

Which has the bigger context window, DeepSeek V3.2 or Muse Spark 1.2?

Muse Spark 1.2, with 1.05M tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs Muse Spark 1.2 comparison based on?

51 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and Muse Spark 1.2's on 5. Where only one model has a result, the page says so instead of naming a winner.

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<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-1-2">LLMBenchmark.ai, DeepSeek V3.2 vs Muse Spark 1.2</a></p>