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DeepSeek V4 Flash 0423 vs Muse Spark 1.3

Updated October 21, 2026. 46 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V4 Flash 0423 Replace Muse Spark 1.3
DeepSeek V4 Flash 0423DeepSeek
Coding
54.6
Overall
24.4
Output $/M
$0.182
0
Muse Spark 1.3Meta
Coding
79.7
Overall
48.1
Output $/M
$4.25
46
vs boards won of 46 shared

Decision reading

Pick Muse Spark 1.3 for coding: it scores 79.7 on the Vibe Coding Index against 54.6 for DeepSeek V4 Flash 0423.

Pick DeepSeek V4 Flash 0423 when output tokens cost 23.4x less.

Model ADeepSeek V4 Flash 0423DeepSeek54.6/1001 independent benchmarkNo cost per solved taskModel BMuse Spark 1.3Meta79.7/1002 independent benchmarksNo cost per solved task
Shared results
46
DeepSeek V4 Flash 0423 only
2
Muse Spark 1.3 only
46
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 V4 Flash 0423 54.6 from 1 benchmark, Muse Spark 1.3 79.7 from 2 benchmarks.
Widest category
Agentic work, 29.2 points apart on average over 1 shared benchmark.
Widest single gap
ALE-Bench: DeepSeek V4 Flash 0423 678.2 against Muse Spark 1.3 1688.3, 33.7 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.182 against Muse Spark 1.3 $4.25 per million output tokens, 23.4x apart.
Context window
Both take 1.05M tokens.

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 V4 Flash 0423BarsMuse Spark 1.3Ties
Coding07
Intelligence02
Agentic work01
Reasoning03
Math02
Knowledge04
Web design08
General019
All shared boards046
CodingLike-for-likeFour of these boards feed the Vibe Coding Index07
Coding benchmarks for DeepSeek V4 Flash 0423 and Muse Spark 1.3
BenchmarkDeepSeek V4 Flash 0423Muse Spark 1.3Leader
LiveBench codingNot measured90.2%Only Muse Spark 1.3 measured
LMArena WebDev15821659Muse Spark 1.3
AA Coding Index52.075.8Muse Spark 1.3
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
WeirdML45.6%Not measuredOnly DeepSeek V4 Flash 0423 measured
ALE-Bench678.21688.3Muse Spark 1.3
LMArena Text: coding14571510Muse Spark 1.3
LMArena Text: software and IT14531511Muse Spark 1.3
LMArena WebDev: React15851672Muse Spark 1.3
LMArena WebDev: HTML15581653Muse Spark 1.3
LMArena WebDev: from an imageNot measured1640Only Muse Spark 1.3 measured
Neither model has a result on the other 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V4 Flash 0423 and Muse Spark 1.3
BenchmarkDeepSeek V4 Flash 0423Muse Spark 1.3Leader
AA Intelligence Index24.448.1Muse 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 V4 Flash 0423 and Muse Spark 1.3
BenchmarkDeepSeek V4 Flash 0423Muse Spark 1.3Leader
AA Agentic Index26.355.5Muse Spark 1.3
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
APEX-AgentsNot measured58.6%Only Muse Spark 1.3 measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for DeepSeek V4 Flash 0423 and Muse Spark 1.3
BenchmarkDeepSeek V4 Flash 0423Muse 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
SimpleBench46.3%Not measuredOnly DeepSeek V4 Flash 0423 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 prompts14441503Muse Spark 1.3
LMArena Text: expert14411519Muse Spark 1.3
LMArena Text: hard prompts in English14491500Muse 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 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V4 Flash 0423 and Muse Spark 1.3
BenchmarkDeepSeek V4 Flash 0423Muse 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: math14261497Muse Spark 1.3
LMArena Text: mathematical work14321491Muse Spark 1.3
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for DeepSeek V4 Flash 0423 and Muse Spark 1.3
BenchmarkDeepSeek V4 Flash 0423Muse Spark 1.3Leader
LMArena Text: science14461507Muse Spark 1.3
LMArena Text: medicine and healthcare14481493Muse Spark 1.3
LMArena Text: legal and government14391499Muse Spark 1.3
LMArena Text: business and finance14251490Muse Spark 1.3
Neither model has a result on the other 13 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for DeepSeek V4 Flash 0423 and Muse Spark 1.3
BenchmarkDeepSeek V4 Flash 0423Muse Spark 1.3Leader
Design Arena: websites12081356Muse Spark 1.3
Design Arena: UI components11641352Muse Spark 1.3
Design Arena: game dev11971329Muse Spark 1.3
Design Arena: data viz11271343Muse Spark 1.3
Design Arena: 3D11891359Muse Spark 1.3
Design Arena: all code categories12051353Muse Spark 1.3
Design Arena: SVG11551300Muse Spark 1.3
Design Arena: ASCII art11091280Muse Spark 1.3
LiveBench designNot measured74.8%Only Muse Spark 1.3 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for DeepSeek V4 Flash 0423 and Muse Spark 1.3
BenchmarkDeepSeek V4 Flash 0423Muse 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
DTBench86.4%96.5%Muse Spark 1.3
LMCA35.9%53.9%Muse Spark 1.3
LMArena Text14321490Muse Spark 1.3
LMArena Text: instruction following14211479Muse Spark 1.3
LMArena Text: creative writing14031462Muse Spark 1.3
LMArena VisionNot measured1308Only Muse Spark 1.3 measured
LMArena Text: English14411495Muse Spark 1.3
LMArena Text: non-English14191481Muse Spark 1.3
LMArena Text: Chinese14631532Muse Spark 1.3
LMArena Text: German14191513Muse Spark 1.3
LMArena Text: Japanese13891470Muse Spark 1.3
LMArena Text: Korean13831490Muse Spark 1.3
LMArena Text: French14391511Muse Spark 1.3
LMArena Text: Spanish14361496Muse Spark 1.3
LMArena Text: Russian14271491Muse Spark 1.3
LMArena Text: Polish14361490Muse Spark 1.3
LMArena Text: multi-turn14491487Muse Spark 1.3
LMArena Text: longer queries14341488Muse Spark 1.3
LMArena Text: writing and language14101470Muse Spark 1.3
LMArena Text: entertainment and media14001457Muse 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 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V4 Flash 0423 and Muse Spark 1.3
BenchmarkDeepSeek V4 Flash 0423Muse 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 V4 Flash 0423$0.0002Fits in one request
Muse Spark 1.3$0.0034Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0423$0.0051Fits in one request
Muse Spark 1.3$0.075Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.020Fits in one request
Muse Spark 1.3$0.292Fits in one request

DeepSeek V4 Flash 0423 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 V4 Flash 0423 and Muse Spark 1.3
SpecificationDeepSeek V4 Flash 0423Meta Muse Spark 1.3
MakerDeepSeekMeta
WeightsOpen weightsNo public weights listed
ReleasedApril 24, 2026September 2, 2026
Context window1.05M1.05M
Input $/Mtok$0.091$1.25
Output $/Mtok$0.182$4.25
Coding index54.679.7
Independent measurements behind it12
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 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 54.6 for DeepSeek V4 Flash 0423. Pick DeepSeek V4 Flash 0423 when output tokens cost 23.4x less.

Which is cheaper, DeepSeek V4 Flash 0423 or Muse Spark 1.3?

DeepSeek V4 Flash 0423 costs $0.091 per million input tokens and $0.182 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 V4 Flash 0423 or Muse Spark 1.3?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Flash 0423 vs Muse Spark 1.3 comparison based on?

46 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark 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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