DeepSeek V3.2 vs Muse Spark

Updated October 5, 2026. Rank says Muse Spark is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Muse Spark has the higher public score estimate, 59.99 versus 49.4, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

DeepSeek V3.2

DeepSeek

49.4/100

Supported · Public rank #88

90% interval 36.3–62.5

Model B

Muse Spark

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Long documents
Prompts that approach the documented context limit
Muse Spark
Muse Spark has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
DeepSeek V3.2 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek V3.2 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. DeepSeek V3.2 does not fit this workload in one request. Muse Spark has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

30.6DeepSeek V3.251.1Muse Spark

Directional only · BenchAlign v5.8

Muse Spark has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

FrontierMath v2 (Tiers 1-3)Math
Normalized gap 16.9
DeepSeek V3.2:22.100%
Muse Spark:39.000%
Shared source
FrontierMath v2 (Tier 4)Math
Normalized gap 12.5
DeepSeek V3.2:2.100%
Muse Spark:14.600%
Shared source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Coding

Directional only

DeepSeek V3.2: 30.6; Estimated · #90/144
Muse Spark: 51.1; Supported · #39/144
Basis: BenchAlign v5.8 lane · 2 vs 5 public rows
Reading: Directional only

Knowledge

Directional only

DeepSeek V3.2: 41.7; Estimated · #98/171
Muse Spark: 60.3; Supported · #40/171
Basis: BenchAlign v5.8 lane · 0 vs 7 public rows
Reading: Directional only

Instruction following

Directional only

DeepSeek V3.2: 56.7; #75/125
Muse Spark: 91.9; #8/125
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Agentic

Not comparable

DeepSeek V3.2: Not ranked
Muse Spark: 49.8; Supported · #43/119
Basis: BenchAlign v5.8 lane · 3 vs 5 public rows
Reading: Not comparable

Reasoning

Not comparable

DeepSeek V3.2: 53.6; Unranked · 2 rankable rows
Muse Spark: 53.4; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

DeepSeek V3.2: Not ranked
Muse Spark: 78.5; #14/49
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Multilingual

Not comparable

DeepSeek V3.2: Not ranked
Muse Spark: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

DeepSeek V3.2: 40.0; Unranked · 2 rankable rows
Muse Spark: 55.1; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for DeepSeek V3.2 and Muse Spark
Category	DeepSeek V3.2	Muse Spark	Basis	Reading
Coding	30.6Estimated · #90/144	51.1Supported · #39/144	Directional onlyBenchAlign v5.8 lane · 2 vs 5 public rows	Directional only
Knowledge	41.7Estimated · #98/171	60.3Supported · #40/171	Directional onlyBenchAlign v5.8 lane · 0 vs 7 public rows	Directional only
Instruction following	56.7#75/125	91.9#8/125	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Agentic	Not ranked	49.8Supported · #43/119	Not comparableBenchAlign v5.8 lane · 3 vs 5 public rows	Not comparable
Reasoning	53.6Unranked · 2 rankable rows	53.4Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	Not ranked	78.5#14/49	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	40.0Unranked · 2 rankable rows	55.1Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

DeepSeek V3.2: $0.00049; Fits in one request
Muse Spark: API rate not published; Fits in one request

Muse Spark has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3.2: $0.01526; Fits in one request
Muse Spark: API rate not published; Fits in one request

Muse Spark has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

DeepSeek V3.2: $0.0154; Does not fit in one request
Muse Spark: API rate not published; Fits in one request; Cached-input rate unavailable

DeepSeek V3.2 does not fit this workload in one request. Muse Spark has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationDeepSeek V3.2Muse Spark

Context window

Maximum documented context; output-token limits may be lower.

DeepSeek V3.2

128K

Muse Spark

262K

API model ID

DeepSeek V3.2

Not sourced

Muse Spark

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

DeepSeek V3.2

$0.028 per 1M cached input tokens

Muse Spark

No comparable hosted API rate

Documented inputs

DeepSeek V3.2

Not sourced

Muse Spark

Not sourced

Documented outputs

DeepSeek V3.2

Not sourced

Muse Spark

Not sourced

Provider availability

DeepSeek V3.2

Not sourced

Muse Spark

Not sourced

Reasoning profile

DeepSeek V3.2

Non-Reasoning

Muse Spark

Reasoning

Weight access

DeepSeek V3.2

Open Weight

Muse Spark

Proprietary

License

DeepSeek V3.2

Open Weight

Muse Spark

Proprietary

Release date

DeepSeek V3.2

2025-12-01

Muse Spark

2026-04-08

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: Muse Spark has the higher public score estimate, 59.99 versus 49.4, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Muse Spark has the larger documented window (262K).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, DeepSeek V3.2 or Muse Spark?

Muse Spark has the higher public score estimate, 59.99 versus 49.4, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, DeepSeek V3.2 or Muse Spark?

Muse Spark scores higher for coding on the public lane, 51.1 to 30.6. DeepSeek V3.2 is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, DeepSeek V3.2 or Muse Spark?

DeepSeek V3.2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, DeepSeek V3.2 or Muse Spark?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

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

Muse Spark has the larger documented context window: 262K, compared with 128K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence31 rows

Agentic

Claw-Eval
Shared source
DeepSeek V3.240.2%
Muse Spark63.8%
Muse Spark leads this result
VITA-Bench
DeepSeek V3.218.5%
Source
Muse Spark—
Not directly comparable
Gert Labs
DeepSeek V3.229.57%
Source
Muse Spark—
Not directly comparable
Terminal-Bench 2.0
DeepSeek V3.2—
Muse Spark59%
Source
Not directly comparable
τ²-bench results
DeepSeek V3.2—
Muse Spark91.5%
Source
Not directly comparable
DeepSearchQA
DeepSeek V3.2—
Muse Spark74.8%
Source
Not directly comparable
CyberGym
DeepSeek V3.2—
Muse Spark43.5%
Source
Not directly comparable

Coding

SWE-Rebench
DeepSeek V3.260.9%
Source
Muse Spark—
Not directly comparable
React Native Evals
DeepSeek V3.271.5%
Source
Muse Spark—
Not directly comparable
SWE-bench Verified
DeepSeek V3.2—
Muse Spark77.4%
Source
Not directly comparable
SWE-bench Pro
DeepSeek V3.2—
Muse Spark52.4%
Source
Not directly comparable
LiveCodeBench Pro
DeepSeek V3.2—
Muse Spark80.0%
Source
Not directly comparable
Vibe Code Bench
DeepSeek V3.2—
Muse Spark19.67%
Source
Not directly comparable
SWE-bench (Vals)
DeepSeek V3.2—
Muse Spark74.4%
Source
Not directly comparable

Reasoning

ARC-AGI-2
DeepSeek V3.2—
Muse Spark42.5%
Source
Not directly comparable

Multimodal

CharXiv
DeepSeek V3.2—
Muse Spark86.4%
Source
Not directly comparable
MMMU-Pro
DeepSeek V3.2—
Muse Spark80.4%
Source
Not directly comparable
ERQA
DeepSeek V3.2—
Muse Spark64.7%
Source
Not directly comparable
SimpleVQA
DeepSeek V3.2—
Muse Spark71.3%
Source
Not directly comparable
ScreenSpot Pro
DeepSeek V3.2—
Muse Spark84.1%
Source
Not directly comparable
ZeroBench
DeepSeek V3.2—
Muse Spark33.0%
Source
Not directly comparable
MedXpertQA (MM)
DeepSeek V3.2—
Muse Spark78.4%
Source
Not directly comparable

Knowledge

GPQA-D
DeepSeek V3.2—
Muse Spark89.5%
Source
Not directly comparable
HLE
DeepSeek V3.2—
Muse Spark50.4%
Source
Not directly comparable
HLE w/o tools
DeepSeek V3.2—
Muse Spark42.8%
Source
Not directly comparable
HealthBench Hard
DeepSeek V3.2—
Muse Spark42.8%
Source
Not directly comparable
MedXpertQA (Text)
DeepSeek V3.2—
Muse Spark52.6%
Source
Not directly comparable
GPQA Diamond (Vals)
DeepSeek V3.2—
Muse Spark89.6%
Source
Not directly comparable
MMLU-Pro (Vals)
DeepSeek V3.2—
Muse Spark87.3%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Shared source
DeepSeek V3.222.100%
Muse Spark39.000%
Muse Spark leads this result
FrontierMath v2 (Tier 4)
Shared source
DeepSeek V3.22.100%
Muse Spark14.600%
Muse Spark leads this result

31 public results · 3 shared

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Last updated October 5, 2026

DeepSeek V3.2 vs Muse Spark

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Instruction following

Agentic

Reasoning

Multimodal

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, DeepSeek V3.2 or Muse Spark?

Which is better for coding, DeepSeek V3.2 or Muse Spark?

Which is better for agentic tasks, DeepSeek V3.2 or Muse Spark?

Which costs less, DeepSeek V3.2 or Muse Spark?

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

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch DeepSeek V3.2 vs Muse Spark