Coding work
Code generation, repair, and software-engineering tasks
Muse Spark 1.1
Muse Spark 1.1 leads on the public coding lane, 56.3 to 50.3, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
DeepSeek V4 Pro 0813 vs Muse Spark 1.1
Updated September 27, 2026. Rank says Muse Spark 1.1 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
Share or export
Decision reading
Muse Spark 1.1 has the higher public score estimate, 65.92 versus 63.48, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 13 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 13
- DeepSeek V4 Pro 0813 only
- 29
- Muse Spark 1.1 only
- 13
- Like-for-like categories
- 2 / 8
Estimated: DeepSeek V4 Pro 0813 · Supported: Muse Spark 1.1How 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.
Agentic work
Tool use, computer use, and multi-step task completion
Muse Spark 1.1
Muse Spark 1.1 leads on the public agentic lane, 57.7 to 55, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Show secondary and unsupported calls
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository 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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
50.3DeepSeek V4 Pro 081356.3Muse Spark 1.1
Like-for-like · BenchAlign v5.7
Muse Spark 1.1 leads the like-for-like coding row, although the 90% intervals overlap.
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.
29 DeepSeek V4 Pro 0813 only
13 Shared
13 Muse Spark 1.1 only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
HLEKnowledge
Normalized gap 19.4DeepSeek V4 Pro 0813:42.7%Muse Spark 1.1:62.1%SWE-bench ProCoding
Normalized gap 6.1DeepSeek V4 Pro 0813:55.4%Muse Spark 1.1:61.5%MMLU-Pro (Vals)Knowledge
Normalized gap 1.7DeepSeek V4 Pro 0813:87.0%Muse Spark 1.1:88.7%LiveCodeBench (Vals)Coding
Normalized gap 1.6DeepSeek V4 Pro 0813:87.5%Muse Spark 1.1:85.9%
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 v5.7 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
Like-for-like- DeepSeek V4 Pro 0813
- 55.0
- Supported · #29/105
- Muse Spark 1.1
- 57.7
- Supported · #22/105
- Basis
- BenchAlign v5.7 lane · 11 vs 14 public rows
- Reading
- Muse Spark 1.1 leads · intervals overlap
Coding
Like-for-like- DeepSeek V4 Pro 0813
- 50.3
- Supported · #39/135
- Muse Spark 1.1
- 56.3
- Supported · #23/135
- Basis
- BenchAlign v5.7 lane · 15 vs 4 public rows
- Reading
- Muse Spark 1.1 leads · intervals overlap
Knowledge
Directional only- DeepSeek V4 Pro 0813
- 63.4
- Estimated · #29/158
- Muse Spark 1.1
- 68.2
- Supported · #15/158
- Basis
- BenchAlign v5.7 lane · 8 vs 5 public rows
- Reading
- Directional only
Reasoning
Not comparable- DeepSeek V4 Pro 0813
- 56.9
- Unranked · 4 rankable rows
- Muse Spark 1.1
- 75.5
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- Muse Spark 1.1
- 77.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4 Pro 0813
- Not ranked
- Muse Spark 1.1
- 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 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V4 Pro 0813
- 80.2
- Unranked · 4 rankable rows
- Muse Spark 1.1
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V4 Pro 0813 | Muse Spark 1.1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 55.0Supported · #29/105 | 57.7Supported · #22/105 | Like-for-likeBenchAlign v5.7 lane · 11 vs 14 public rows | Muse Spark 1.1 leads · intervals overlap |
| Coding | 50.3Supported · #39/135 | 56.3Supported · #23/135 | Like-for-likeBenchAlign v5.7 lane · 15 vs 4 public rows | Muse Spark 1.1 leads · intervals overlap |
| Knowledge | 63.4Estimated · #29/158 | 68.2Supported · #15/158 | Directional onlyBenchAlign v5.7 lane · 8 vs 5 public rows | Directional only |
| Reasoning | 56.9Unranked · 4 rankable rows | 75.5Unranked · 3 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 77.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 1 weighted 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 | 80.2Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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 1.1
- API rate not published
- Fits in one request
Muse Spark 1.1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V4 Pro 0813
- $0.07788
- Fits in one request
- Muse Spark 1.1
- API rate not published
- Fits in one request
Muse Spark 1.1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek V4 Pro 0813
- $0.0748
- Fits in one request
- Muse Spark 1.1
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Muse Spark 1.1 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 V4 Pro 0813Muse Spark 1.1
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4 Pro 0813
Muse Spark 1.1
1M
API model ID
DeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingMuse Spark 1.1
Not sourced
Cached-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 1.1
No comparable hosted API rate
Documented inputs
DeepSeek V4 Pro 0813
Muse Spark 1.1
Not sourced
Documented outputs
DeepSeek V4 Pro 0813
Muse Spark 1.1
Not sourced
Provider availability
DeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricing, V4 Pro continuation footnoteMuse Spark 1.1
Not sourced
Reasoning profile
DeepSeek V4 Pro 0813
Reasoning
Muse Spark 1.1
Reasoning
Weight access
DeepSeek V4 Pro 0813
Open Weight
Muse Spark 1.1
Proprietary
License
DeepSeek V4 Pro 0813
Open Weight
Muse Spark 1.1
Proprietary
Release date
DeepSeek V4 Pro 0813
2026-08-13
Muse Spark 1.1
2026-07-09
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 1.1 has the higher public score estimate, 65.92 versus 63.48, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- 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 1.1?
Muse Spark 1.1 has the higher public score estimate, 65.92 versus 63.48, 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 V4 Pro 0813 or Muse Spark 1.1?
Muse Spark 1.1 leads the public coding lane, 56.3 to 50.3, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, DeepSeek V4 Pro 0813 or Muse Spark 1.1?
Muse Spark 1.1 leads the public agentic tasks lane, 57.7 to 55, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, DeepSeek V4 Pro 0813 or Muse Spark 1.1?
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 V4 Pro 0813 or Muse Spark 1.1?
Both models list the same context window, 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence55 rows
Agentic
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceMuse Spark 1.1—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceMuse Spark 1.180.0%SourceDeepSeek V4 Pro 0813 leads this result
BrowseComp
DeepSeek V4 Pro 081383.4%SourceMuse Spark 1.1—Not directly comparable
HLE w/ tools
DeepSeek V4 Pro 081360.0%SourceMuse Spark 1.1—Not directly comparable
MCP Atlas
DeepSeek V4 Pro 081373.6%SourceMuse Spark 1.188.1%SourceMuse Spark 1.1 leads this result
Toolathlon
DeepSeek V4 Pro 081351.8%SourceMuse Spark 1.175.6%SourceMuse Spark 1.1 leads this result
CyberGym
DeepSeek V4 Pro 081383.3%SourceMuse Spark 1.159.0%SourceDeepSeek V4 Pro 0813 leads this result
Toolathlon-Verified
DeepSeek V4 Pro 081374.1%SourceMuse Spark 1.1—Not directly comparable
Agents' Last Exam
DeepSeek V4 Pro 081325.7%SourceMuse Spark 1.1—Not directly comparable
AutomationBench
DeepSeek V4 Pro 081331.8%SourceMuse Spark 1.1—Not directly comparable
Terminal-Bench 2.1 (Vals)
DeepSeek V4 Pro 081354.7%SourceMuse Spark 1.169.3%SourceMuse Spark 1.1 leads this result
OSWorld-Verified
DeepSeek V4 Pro 0813—Muse Spark 1.180.8%SourceNot directly comparable
WebArena-Verified
DeepSeek V4 Pro 0813—Muse Spark 1.169%SourceNot directly comparable
DeepSearchQA
DeepSeek V4 Pro 0813—Muse Spark 1.184.9%SourceNot directly comparable
Finance Agent v2
DeepSeek V4 Pro 0813—Muse Spark 1.157.2%SourceNot directly comparable
deepSwe
DeepSeek V4 Pro 0813—Muse Spark 1.153.3%SourceNot directly comparable
OSWorld 2.0
DeepSeek V4 Pro 0813—Muse Spark 1.114.2%SourceNot directly comparable
JobBench
DeepSeek V4 Pro 0813—Muse Spark 1.154.7%SourceNot directly comparable
Cybench
DeepSeek V4 Pro 0813—Muse Spark 1.192.9%SourceNot directly comparable
ExploitGym
DeepSeek V4 Pro 0813—Muse Spark 1.10.8%SourceNot directly comparable
Coding
LiveCodeBench Pass@1-COT
DeepSeek V4 Pro 081393.5%SourceMuse Spark 1.1—Not directly comparable
Codeforces
DeepSeek V4 Pro 08133206.0SourceMuse Spark 1.1—Not directly comparable
SWE-bench Verified
DeepSeek V4 Pro 081380.6%SourceMuse Spark 1.1—Not directly comparable
SWE-bench Pro
DeepSeek V4 Pro 081355.4%SourceMuse Spark 1.161.5%SourceMuse Spark 1.1 leads this result
SWE Multilingual
DeepSeek V4 Pro 081376.2%SourceMuse Spark 1.1—Not directly comparable
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceMuse Spark 1.1—Not directly comparable
Vibe Code Bench
DeepSeek V4 Pro 081349.93%SourceMuse Spark 1.1—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceMuse Spark 1.180.0%SourceDeepSeek V4 Pro 0813 leads this result
NL2Repo
DeepSeek V4 Pro 081361.5%SourceMuse Spark 1.1—Not directly comparable
DeepSWE
DeepSeek V4 Pro 081362.7%SourceMuse Spark 1.1—Not directly comparable
DSBench-FullStack
DeepSeek V4 Pro 081371.1%SourceMuse Spark 1.1—Not directly comparable
DSBench-Hard
DeepSeek V4 Pro 081367.2%SourceMuse Spark 1.1—Not directly comparable
OpenHarmony Bench
DeepSeek V4 Pro 081359.0%SourceMuse Spark 1.1—Not directly comparable
LiveCodeBench (Vals)
DeepSeek V4 Pro 081387.5%SourceMuse Spark 1.185.9%SourceDeepSeek V4 Pro 0813 leads this result
SWE-bench (Vals)
DeepSeek V4 Pro 081396.4%SourceMuse Spark 1.182.0%SourceDeepSeek V4 Pro 0813 leads this result
Reasoning
MRCR 1M
DeepSeek V4 Pro 081383.5%SourceMuse Spark 1.154.1%SourceDeepSeek V4 Pro 0813 leads this result
CorpusQA 1M
DeepSeek V4 Pro 081362.0%SourceMuse Spark 1.1—Not directly comparable
ARC-AGI-1
DeepSeek V4 Pro 081390.00%SourceMuse Spark 1.1—Not directly comparable
ARC-AGI-2
DeepSeek V4 Pro 081361.3%SourceMuse Spark 1.1—Not directly comparable
Multimodal
Knowledge
MMLU-Pro
DeepSeek V4 Pro 081387.5%SourceMuse Spark 1.1—Not directly comparable
SimpleQA
DeepSeek V4 Pro 081357.9%SourceMuse Spark 1.1—Not directly comparable
Chinese-SimpleQA
DeepSeek V4 Pro 081384.4%SourceMuse Spark 1.1—Not directly comparable
GPQA
DeepSeek V4 Pro 081390.1%SourceMuse Spark 1.1—Not directly comparable
GPQA-D
DeepSeek V4 Pro 081390.1%SourceMuse Spark 1.1—Not directly comparable
HLE
DeepSeek V4 Pro 081342.7%SourceMuse Spark 1.162.1%SourceMuse Spark 1.1 leads this result
GPQA Diamond (Vals)
DeepSeek V4 Pro 081392.4%SourceMuse Spark 1.191.2%SourceDeepSeek V4 Pro 0813 leads this result
MMLU-Pro (Vals)
DeepSeek V4 Pro 081387.0%SourceMuse Spark 1.188.7%SourceMuse Spark 1.1 leads this result
HLE w/o tools
DeepSeek V4 Pro 0813—Muse Spark 1.152.2%SourceNot directly comparable
HealthBench Professional
DeepSeek V4 Pro 0813—Muse Spark 1.159.3%SourceNot directly comparable
Math
HMMT Feb 2026
DeepSeek V4 Pro 081395.2%SourceMuse Spark 1.1—Not directly comparable
IMOAnswerBench
DeepSeek V4 Pro 081389.8%SourceMuse Spark 1.1—Not directly comparable
Apex
DeepSeek V4 Pro 081338.3%SourceMuse Spark 1.1—Not directly comparable
Apex Shortlist
DeepSeek V4 Pro 081390.2%SourceMuse Spark 1.1—Not directly comparable
55 public results · 13 shared
Watch DeepSeek V4 Pro 0813 vs Muse Spark 1.1
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.
Last updated September 27, 2026