Model comparison

DeepSeek-V3 vs Muse Spark 1.3

Muse Spark 1.3 is the stronger model overall, scoring 54.8 to 39.5 on the Noometry Index. DeepSeek-V3 costs 4.9× less per token, which makes it the better buy when Muse Spark 1.3's lead doesn't matter for your workload.

Last verified October 10, 2026. 24 shared benchmarks.

DeepSeek-V3

DeepSeek

39.5

Rank #166 Confirmed

Muse Spark 1.3

Summary

They share 24 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and Muse Spark 1.3 in 8 categories; 8 gaps are clear of the uncertainty.
The widest gap is in math, where Muse Spark 1.3 leads 73.1 to 32.1.
The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 37.8% for DeepSeek-V3 and 99.2% for Muse Spark 1.3.
DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $1.25 / $4.25 for Muse Spark 1.3.
Muse Spark 1.3 accepts more context: 1.05M tokens versus 164K.
DeepSeek-V3 has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3 and Muse Spark 1.3 specifications
	DeepSeek-V3	Muse Spark 1.3
Provider	DeepSeek	Meta
Noometry Index	39.5	54.8
Released	2024-12-26	2026-09-02
Weights	Open	Proprietary
Context window	164K	1.05M
Max output	164K	131K
Input $ / M tokens	$0.24	$1.25
Output $ / M tokens	$0.90	$4.25
Results tracked	60	37

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Category by category

Coding Muse Spark 1.3 leads

DeepSeek-V3: 42.3 (#106), Muse Spark 1.3: 56.6 (#21)

Coding benchmarks
Benchmark	DeepSeek-V3	Muse Spark 1.3
SciCode	35.8%	59.7%
LMArena Coding	1368	1514
Aider Polyglot	55.1%	—
CursorBench	—	41.6%
LMArena WebDev	—	1657
WeirdML	36.1%	—
BigCodeBench Instruct	50%	—
LiveBench Coding	70.9%	—
BigCodeBench Complete	62.2%	—
HumanEval+	86.6%	—
MBPP+	73%	—

Agentic & Tool Use Not comparable

DeepSeek-V3: —, Muse Spark 1.3: 38.6 (#30)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3	Muse Spark 1.3
APEX-Agents	—	57.8%
GDP.pdf	—	27.6%
METR Time Horizons	49.6%	—

Reasoning Muse Spark 1.3 leads

DeepSeek-V3: 20.5 (#236), Muse Spark 1.3: 54.0 (#27)

Reasoning benchmarks
Benchmark	DeepSeek-V3	Muse Spark 1.3
CritPt	0%	26%
LMArena Hard Prompts	1365	1503
DTBench	64.8%	96.5%
LMCA	15.5%	53.9%
Epoch Capabilities Index	135.94	156.75
SimpleBench	27.2%	—
Kagi LLM Benchmark	52.3%	—
NYT Connections (extended)	—	85.1%
Chess Puzzles	—	38%
LiveBench Reasoning	65.8%	—
Mystery Game Puzzles	—	25%
LiveBench Data Analysis	60.9%	—
Bench to the Future 3	—	0.14
BIG-Bench Hard	87.5%	—
ForecastBench	59.1	—
HellaSwag	88.9%	—
LiveBench	66.9%	—
PIQA	84.7%	—
WinoGrande	85.2%	—

Math Muse Spark 1.3 leads

DeepSeek-V3: 32.1 (#219), Muse Spark 1.3: 73.1 (#21)

Math benchmarks
Benchmark	DeepSeek-V3	Muse Spark 1.3
OTIS Mock AIME 2024-2025	37.8%	99.2%
LMArena Math	1373	1494
FrontierMath (Tiers 1-3)	—	74.4%
FrontierMath Tier 4	—	46.3%
ProofBench	—	58%
Omni-MATH	40.3%	—
LiveBench Math	73.5%	—
MATH Level 5	75.5%	—
FrontierMath (Feb 2025 set)	1.7%	—

Knowledge Muse Spark 1.3 leads

DeepSeek-V3: 37.5 (#155), Muse Spark 1.3: 42.6 (#95)

Knowledge benchmarks
Benchmark	DeepSeek-V3	Muse Spark 1.3
LMArena Expert	1351	1516
GPQA Diamond	67.6%	—
MMLU-Pro	72.3%	—
Confabulations	26.1%	—
Vectara Hallucination Rate	6.1%	—
GPQA (HELM)	53.8%	—
ARC (AI2) Challenge	95.3%	—
MMLU	87.2%	—
TriviaQA	82.9%	—

Multimodal Not comparable

DeepSeek-V3: —, Muse Spark 1.3: 43.7 (#22)

Multimodal benchmarks
Benchmark	DeepSeek-V3	Muse Spark 1.3
LMArena Vision	—	1309
LMArena Document	—	1471

Multilingual Muse Spark 1.3 leads

DeepSeek-V3: 48.5 (#143), Muse Spark 1.3: 57.4 (#8)

Multilingual benchmarks
Benchmark	DeepSeek-V3	Muse Spark 1.3
LMArena Non-English	1358	1481
LMArena Chinese	1391	1529
LMArena French	1385	1524
LMArena German	1374	1515
LMArena Japanese	1333	1474
LMArena Korean	1319	1501
LMArena Russian	1373	1490
LMArena Spanish	1358	1490

Instruction Following Muse Spark 1.3 leads

DeepSeek-V3: 72.8 (#130), Muse Spark 1.3: 77.5 (#22)

Instruction Following benchmarks
Benchmark	DeepSeek-V3	Muse Spark 1.3
LMArena Instruction Following	1345	1477
LiveBench Instruction Following	81.5%	—
IFEval	83.2%	—

Long Context Muse Spark 1.3 leads

DeepSeek-V3: 34.0 (#253), Muse Spark 1.3: 45.6 (#32)

Long Context benchmarks
Benchmark	DeepSeek-V3	Muse Spark 1.3
LMArena Longer Query	1352	1488
Fiction.LiveBench	50%	—

Writing & Preference Muse Spark 1.3 leads

DeepSeek-V3: 57.4 (#130), Muse Spark 1.3: 73.6 (#9)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3	Muse Spark 1.3
LMArena Text	1375	1490
LMArena Creative Writing	1364	1455
EQ-Bench Creative Writing	1472	1906
LMArena Multi-Turn	1389	1482
Short-Story Creative Writing	77%	—
WildBench	83%	—
LiveBench Language	49.1%	—

Frequently asked questions

Is DeepSeek-V3 better than Muse Spark 1.3?

Which is cheaper, DeepSeek-V3 or Muse Spark 1.3?

DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; Muse Spark 1.3 lists at $1.25 and $4.25.

Is DeepSeek-V3 or Muse Spark 1.3 better for coding?

Muse Spark 1.3 scores higher on coding benchmarks: 56.6 versus 42.3 in the Noometry coding category.

Which has the bigger context window?

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

How many benchmarks do DeepSeek-V3 and Muse Spark 1.3 share?

24 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Muse Spark 1.3 has 37.