Agentic
Directional only- DeepSeek V4 Pro 0813
- 74.5
- Laguna S 2.1
- 70.2
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
DeepSeek V4 Pro 0813 vs Laguna S 2.1
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
6 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Chat turn cost
1K fresh input + 500 output tokens
Laguna S 2.1
Laguna S 2.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Laguna S 2.1
Laguna S 2.1 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
Laguna S 2.1
Laguna S 2.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
28 DeepSeek V4 Pro 0813 only
6 Shared
- Shared results
- 6
- DeepSeek V4 Pro 0813 only
- 28
- Laguna S 2.1 only
- 0
- Like-for-like categories
- 0 / 8
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Directional only- DeepSeek V4 Pro 0813
- 70.9
- Laguna S 2.1
- 59.4
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Reasoning
Not comparable- DeepSeek V4 Pro 0813
- Not measured
- Laguna S 2.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V4 Pro 0813
- 62.5
- Laguna S 2.1
- Not measured
- Weighted basis
- 4 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V4 Pro 0813
- 95.2
- Laguna S 2.1
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V4 Pro 0813
- Not measured
- Laguna S 2.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V4 Pro 0813
- Not measured
- Laguna S 2.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V4 Pro 0813
- Not measured
- Laguna S 2.1
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | DeepSeek V4 Pro 0813 | Laguna S 2.1 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 74.5 | 70.2 | Directional only2 vs 1 rows | Directional only |
| Coding | 70.9 | 59.4 | Directional only2 vs 1 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 62.5 | Not measured | Not comparable4 vs 0 rows | Not comparable |
| Math | 95.2 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
SWE-bench Pro
Coding
DeepSeek V4 Pro 0813: 55.4%Laguna S 2.1: 59.4%Normalized gap 4.0DeepSeek V4 Pro 0813 source Laguna S 2.1 sourceTerminal-Bench 2.0
Agentic
DeepSeek V4 Pro 0813: 67.9%Laguna S 2.1: 70.2%Normalized gap 2.3DeepSeek V4 Pro 0813 source Laguna S 2.1 source
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
- $0.00087
- Fits in one request
- Laguna S 2.1
- $0.0002
- Fits in one request
Laguna S 2.1 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V4 Pro 0813
- $0.02436
- Fits in one request
- Laguna S 2.1
- $0.0056
- Fits in one request
Laguna S 2.1 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
- $0.01812
- Fits in one request
- Laguna S 2.1
- $0.006
- Fits in one request
Laguna S 2.1 has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationDeepSeek V4 Pro 0813Laguna S 2.1
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V4 Pro 0813
Laguna S 2.1
1M
API model ID
DeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingLaguna S 2.1
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
$0.003625 per 1M cached input tokens
Laguna S 2.1
$0.01 per 1M cached input tokens
Documented inputs
DeepSeek V4 Pro 0813
Laguna S 2.1
Not sourced
Documented outputs
DeepSeek V4 Pro 0813
Laguna S 2.1
Not sourced
Provider availability
DeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricingLaguna S 2.1
Not sourced
Reasoning profile
DeepSeek V4 Pro 0813
Reasoning
Laguna S 2.1
Reasoning
Weight access
DeepSeek V4 Pro 0813
Proprietary
Laguna S 2.1
Open Weight
License
DeepSeek V4 Pro 0813
Proprietary
Laguna S 2.1
Open Weight
Release date
DeepSeek V4 Pro 0813
2026-08-13
Laguna S 2.1
2026-07-21
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: $0.02436 vs $0.0056. Cache-heavy agent loop: $0.01812 vs $0.006.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence34 rows
Agentic
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceLaguna S 2.170.2%SourceLaguna S 2.1 leads this result
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceLaguna S 2.1—Not directly comparable
BrowseComp
DeepSeek V4 Pro 081383.4%SourceLaguna S 2.1—Not directly comparable
HLE w/ tools
DeepSeek V4 Pro 081360.0%SourceLaguna S 2.1—Not directly comparable
MCP Atlas
DeepSeek V4 Pro 081373.6%SourceLaguna S 2.1—Not directly comparable
Toolathlon
DeepSeek V4 Pro 081351.8%SourceLaguna S 2.1—Not directly comparable
CyberGym
DeepSeek V4 Pro 081383.3%SourceLaguna S 2.1—Not directly comparable
Toolathlon-Verified
DeepSeek V4 Pro 081374.1%SourceLaguna S 2.149.7%SourceDeepSeek V4 Pro 0813 leads this result
Agents' Last Exam
DeepSeek V4 Pro 081325.7%SourceLaguna S 2.1—Not directly comparable
AutomationBench
DeepSeek V4 Pro 081331.8%SourceLaguna S 2.1—Not directly comparable
Coding
LiveCodeBench Pass@1-COT
DeepSeek V4 Pro 081393.5%SourceLaguna S 2.1—Not directly comparable
Codeforces
DeepSeek V4 Pro 08133206.0SourceLaguna S 2.1—Not directly comparable
SWE-bench Verified
DeepSeek V4 Pro 081380.6%SourceLaguna S 2.1—Not directly comparable
SWE-bench Pro
DeepSeek V4 Pro 081355.4%SourceLaguna S 2.159.4%SourceLaguna S 2.1 leads this result
SWE Multilingual
DeepSeek V4 Pro 081376.2%SourceLaguna S 2.178.5%SourceLaguna S 2.1 leads this result
Terminal-Bench 2.0
DeepSeek V4 Pro 081367.9%SourceLaguna S 2.170.2%SourceLaguna S 2.1 leads this result
Vibe Code Bench
DeepSeek V4 Pro 081349.93%SourceLaguna S 2.1—Not directly comparable
Terminal-Bench 2.1
DeepSeek V4 Pro 081387.9%SourceLaguna S 2.1—Not directly comparable
NL2Repo
DeepSeek V4 Pro 081361.5%SourceLaguna S 2.1—Not directly comparable
deepSwe
DeepSeek V4 Pro 081362.7%SourceLaguna S 2.140.4%SourceDeepSeek V4 Pro 0813 leads this result
DSBench-FullStack
DeepSeek V4 Pro 081371.1%SourceLaguna S 2.1—Not directly comparable
DSBench-Hard
DeepSeek V4 Pro 081367.2%SourceLaguna S 2.1—Not directly comparable
Reasoning
Knowledge
MMLU-Pro
DeepSeek V4 Pro 081387.5%SourceLaguna S 2.1—Not directly comparable
SimpleQA
DeepSeek V4 Pro 081357.9%SourceLaguna S 2.1—Not directly comparable
Chinese-SimpleQA
DeepSeek V4 Pro 081384.4%SourceLaguna S 2.1—Not directly comparable
GPQA
DeepSeek V4 Pro 081390.1%SourceLaguna S 2.1—Not directly comparable
GPQA-D
DeepSeek V4 Pro 081390.1%SourceLaguna S 2.1—Not directly comparable
HLE
DeepSeek V4 Pro 081342.7%SourceLaguna S 2.1—Not directly comparable
Math
HMMT Feb 2026
DeepSeek V4 Pro 081395.2%SourceLaguna S 2.1—Not directly comparable
IMOAnswerBench
DeepSeek V4 Pro 081389.8%SourceLaguna S 2.1—Not directly comparable
Apex
DeepSeek V4 Pro 081338.3%SourceLaguna S 2.1—Not directly comparable
Apex Shortlist
DeepSeek V4 Pro 081390.2%SourceLaguna S 2.1—Not directly comparable
Frequently asked questions
Which is better, DeepSeek V4 Pro 0813 or Laguna S 2.1?
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 Laguna S 2.1?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, DeepSeek V4 Pro 0813 or Laguna S 2.1?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, DeepSeek V4 Pro 0813 or Laguna S 2.1?
For the stated presets, chat costs $0.00087 on DeepSeek V4 Pro 0813 and $0.0002 on Laguna S 2.1; repository review costs $0.02436 and $0.0056; the cache-heavy agent loop costs $0.01812 and $0.006. Costs use the listed standard API rates.
Which has the larger context window, DeepSeek V4 Pro 0813 or Laguna S 2.1?
Both models list the same context window, 1M.
Related comparisons
Last updated August 13, 2026
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