Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
DeepSeek V4 Pro 0813 vs Muse Spark 1.3
Updated October 17, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Share or export
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 5
- DeepSeek V4 Pro 0813 only
- 36
- Muse Spark 1.3 only
- 15
- Like-for-like categories
- 0 / 8
Estimated: DeepSeek V4 Pro 0813. Conditional ranges do not establish rank confidence.How the comparison works
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.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Muse Spark 1.3
Muse Spark 1.3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Muse Spark 1.3 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Muse Spark 1.3 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
47.2DeepSeek V4 Pro 0813—Muse Spark 1.3
Not comparable · BenchAlign v5.8
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
36 DeepSeek V4 Pro 0813 only
5 Shared
15 Muse Spark 1.3 only
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.
AutomationBenchAgentic
Normalized gap 17.6DeepSeek V4 Pro 0813:31.8%Muse Spark 1.3:49.4%
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.
Agentic
Not comparable- DeepSeek V4 Pro 0813
- 54.5
- Supported · #43/128
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 11 vs 9 public rows
- Reading
- Not comparable
Coding
Not comparable- DeepSeek V4 Pro 0813
- 47.2
- Supported · #49/150
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 15 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V4 Pro 0813
- 60.7
- Unranked · 4 rankable rows
- Muse Spark 1.3
- 82.9
- #6/28
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V4 Pro 0813
- 63.5
- Estimated · #41/184
- Muse Spark 1.3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 7 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- Muse Spark 1.3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V4 Pro 0813
- 87.4
- Unranked · 4 rankable rows
- Muse Spark 1.3
- 78.5
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 2 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V4 Pro 0813 | Muse Spark 1.3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 54.5Supported · #43/128 | Not ranked | Not comparableBenchAlign v5.8 lane · 11 vs 9 public rows | Not comparable |
| Coding | 47.2Supported · #49/150 | Not ranked | Not comparableBenchAlign v5.8 lane · 15 vs 6 public rows | Not comparable |
| Reasoning | 60.7Unranked · 4 rankable rows | 82.9#6/28 | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 63.5Estimated · #41/184 | Not ranked | Not comparableBenchAlign v5.8 lane · 7 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 87.4Unranked · 4 rankable rows | 78.5Unranked · 2 rankable rows | Not comparableProvisional lane · 1 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 V4 Pro 0813
- $0.0033
- Fits in one request
- Muse Spark 1.3
- $0.00338
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V4 Pro 0813
- $0.07788
- Fits in one request
- Muse Spark 1.3
- $0.07525
- Fits in one request
Muse Spark 1.3 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek V4 Pro 0813
- $0.0748
- Fits in one request
- Muse Spark 1.3
- $0.0975
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
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 V4 Pro 0813Muse Spark 1.3
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4 Pro 0813
Muse Spark 1.3
API model ID
DeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingMuse Spark 1.3
muse-spark-1.3
Meta Muse Spark 1.3 model pageCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V4 Pro 0813
$0.044 per 1M cached input tokens
DeepSeek: Models & PricingMuse Spark 1.3
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.3 model pageDocumented inputs
DeepSeek V4 Pro 0813
Muse Spark 1.3
text, image, video, document
Meta Muse Spark 1.3 model pageDocumented outputs
DeepSeek V4 Pro 0813
Muse Spark 1.3
Provider availability
DeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricing, V4 Pro continuation footnoteMuse Spark 1.3
Generally Available · Meta Model API, Muse Code
Meta AI Research Muse Spark 1.3 launchReasoning profile
DeepSeek V4 Pro 0813
Reasoning
Muse Spark 1.3
Reasoning
Weight access
DeepSeek V4 Pro 0813
Open Weight
Muse Spark 1.3
Proprietary
License
DeepSeek V4 Pro 0813
Open Weight
Muse Spark 1.3
Proprietary
Release date
DeepSeek V4 Pro 0813
2026-08-13
Muse Spark 1.3
2026-09-02
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.07788 vs $0.07525. Cache-heavy agent loop: $0.0748 vs $0.0975.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, DeepSeek V4 Pro 0813 or Muse Spark 1.3?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, DeepSeek V4 Pro 0813 or Muse Spark 1.3?
Muse Spark 1.3 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, DeepSeek V4 Pro 0813 or Muse Spark 1.3?
Muse Spark 1.3 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek V4 Pro 0813 or Muse Spark 1.3?
For the stated presets, chat costs $0.0033 on DeepSeek V4 Pro 0813 and $0.00337 on Muse Spark 1.3; repository review costs $0.07788 and $0.07525; the cache-heavy agent loop costs $0.0748 and $0.0975. Costs use the listed standard API rates.
Which has the larger context window, DeepSeek V4 Pro 0813 or Muse Spark 1.3?
Both models list the same context window, 1M.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
DeepSeek V4 Pro 0813
API / mo$0
Self-host / mo$37,843
Break-even—
Muse Spark 1.3
API / mo$4,125
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence56 rows
Agentic
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceMuse Spark 1.388.8%SourceMuse Spark 1.3 leads this result
BrowseComp
DeepSeek V4 Pro 081383.4%SourceMuse Spark 1.3—Not directly comparable
HLE w/ tools
DeepSeek V4 Pro 081360.0%SourceMuse Spark 1.3—Not directly comparable
MCP Atlas
DeepSeek V4 Pro 081373.6%SourceMuse Spark 1.3—Not directly comparable
Toolathlon
DeepSeek V4 Pro 081351.8%SourceMuse Spark 1.3—Not directly comparable
CyberGym
DeepSeek V4 Pro 081383.3%SourceMuse Spark 1.3—Not directly comparable
Toolathlon-Verified
DeepSeek V4 Pro 081374.1%SourceMuse Spark 1.3—Not directly comparable
Agents' Last Exam
DeepSeek V4 Pro 081325.7%SourceMuse Spark 1.3—Not directly comparable
AutomationBench
DeepSeek V4 Pro 081331.8%SourceMuse Spark 1.349.4%SourceMuse Spark 1.3 leads this result
Terminal-Bench 2.1 (Vals)
DeepSeek V4 Pro 081354.7%SourceMuse Spark 1.372.3%SourceMuse Spark 1.3 leads this result
JobBench
DeepSeek V4 Pro 0813—Muse Spark 1.364.9%SourceNot directly comparable
OSWorld 2.0
DeepSeek V4 Pro 0813—Muse Spark 1.366.9%SourceNot directly comparable
DeepSearchQA
DeepSeek V4 Pro 0813—Muse Spark 1.389.4%SourceNot directly comparable
ApprenticeBench
DeepSeek V4 Pro 0813—Muse Spark 1.319%SourceNot directly comparable
CWE-bench v1
DeepSeek V4 Pro 0813—Muse Spark 1.355.0%SourceNot directly comparable
MazeBench
DeepSeek V4 Pro 0813—Muse Spark 1.30%SourceNot directly comparable
Coding
LiveCodeBench Pass@1-COT
DeepSeek V4 Pro 081393.5%SourceMuse Spark 1.3—Not directly comparable
Codeforces
DeepSeek V4 Pro 08133206.0SourceMuse Spark 1.3—Not directly comparable
SWE-bench Verified
DeepSeek V4 Pro 081380.6%SourceMuse Spark 1.3—Not directly comparable
SWE-bench Pro
DeepSeek V4 Pro 081355.4%SourceMuse Spark 1.3—Not directly comparable
SWE Multilingual
DeepSeek V4 Pro 081376.2%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceMuse Spark 1.3—Not directly comparable
Vibe Code Bench
DeepSeek V4 Pro 081349.93%SourceMuse Spark 1.3—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceMuse Spark 1.388.8%SourceMuse Spark 1.3 leads this result
NL2Repo
DeepSeek V4 Pro 081361.5%SourceMuse Spark 1.3—Not directly comparable
DeepSWE
DeepSeek V4 Pro 081362.7%SourceMuse Spark 1.375.4%SourceMuse Spark 1.3 leads this result
DSBench-FullStack
DeepSeek V4 Pro 081371.1%SourceMuse Spark 1.3—Not directly comparable
DSBench-Hard
DeepSeek V4 Pro 081367.2%SourceMuse Spark 1.3—Not directly comparable
OpenHarmony Bench
DeepSeek V4 Pro 081359.0%SourceMuse Spark 1.3—Not directly comparable
LiveCodeBench (Vals)
DeepSeek V4 Pro 081387.5%SourceMuse Spark 1.3—Not directly comparable
SWE-bench (Vals)
DeepSeek V4 Pro 081396.4%SourceMuse Spark 1.3—Not directly comparable
SWE-Atlas Codebase QnA
DeepSeek V4 Pro 0813—Muse Spark 1.359.4%SourceNot directly comparable
CursorBench 4.0
DeepSeek V4 Pro 0813—Muse Spark 1.341.6%SourceNot directly comparable
Bug Hunt Bench
DeepSeek V4 Pro 0813—Muse Spark 1.332.2 fixesSourceNot directly comparable
FrontierSWE v2
DeepSeek V4 Pro 0813—Muse Spark 1.325.8%SourceNot directly comparable
Reasoning
MRCR 1M
DeepSeek V4 Pro 081383.5%SourceMuse Spark 1.3—Not directly comparable
CorpusQA 1M
DeepSeek V4 Pro 081362.0%SourceMuse Spark 1.3—Not directly comparable
ARC-AGI-1
DeepSeek V4 Pro 081390.00%SourceMuse Spark 1.3—Not directly comparable
ARC-AGI-2
DeepSeek V4 Pro 081361.3%SourceMuse Spark 1.3—Not directly comparable
MRCR v2 256K-512K
DeepSeek V4 Pro 0813—Muse Spark 1.398.5%SourceNot directly comparable
MRCR v2 512K-1M
DeepSeek V4 Pro 0813—Muse Spark 1.398.1%SourceNot directly comparable
Knowledge
MMLU-Pro
DeepSeek V4 Pro 081387.5%SourceMuse Spark 1.3—Not directly comparable
Chinese-SimpleQA
DeepSeek V4 Pro 081384.4%SourceMuse Spark 1.3—Not directly comparable
GPQA
DeepSeek V4 Pro 081390.1%SourceMuse Spark 1.3—Not directly comparable
GPQA-D
DeepSeek V4 Pro 081390.1%SourceMuse Spark 1.3—Not directly comparable
HLE
DeepSeek V4 Pro 081342.7%SourceMuse Spark 1.3—Not directly comparable
GPQA Diamond (Vals)
DeepSeek V4 Pro 081392.4%SourceMuse Spark 1.3—Not directly comparable
MMLU-Pro (Vals)
DeepSeek V4 Pro 081387.0%SourceMuse Spark 1.3—Not directly comparable
Instruction following
Gray Swan IPI (15 attempts)
DeepSeek V4 Pro 0813—Muse Spark 1.315.9%SourceNot directly comparable
Math
HMMT Feb 2026
DeepSeek V4 Pro 081395.2%SourceMuse Spark 1.3—Not directly comparable
IMOAnswerBench
DeepSeek V4 Pro 081389.8%SourceMuse Spark 1.3—Not directly comparable
Apex
DeepSeek V4 Pro 081338.3%SourceMuse Spark 1.3—Not directly comparable
Apex Shortlist
DeepSeek V4 Pro 081390.2%SourceMuse Spark 1.3—Not directly comparable
FrontierMath v2 (Tiers 1-3)
DeepSeek V4 Pro 0813—Muse Spark 1.374.035%SourceNot directly comparable
FrontierMath v2 (Tier 4)
DeepSeek V4 Pro 0813—Muse Spark 1.346.341%SourceNot directly comparable
56 public results · 5 shared
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Last updated October 17, 2026