Agentic
Like-for-like- Claude Sonnet 5
- 66.0
- Supported · #12/151
- DeepSeek V4 Pro 0813
- 56.1
- Supported · #37/151
- Basis
- BenchAlign lane · 6 vs 11 public rows
- Reading
- Claude Sonnet 5 leads · intervals overlap
Claude Sonnet 5 vs DeepSeek V4 Pro 0813
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Sonnet 5 has the higher public score estimate, 70.76 versus 66.35, 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.
Share or export
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.
Coding work
Code generation, repair, and software-engineering tasks
Claude Sonnet 5
Claude Sonnet 5 leads on the public coding lane, 64.2 to 52.2, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Claude Sonnet 5
Claude Sonnet 5 leads on the public agentic lane, 66 to 56.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
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
Show secondary and unsupported calls
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
Repository review cost
50K fresh input + 3K 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
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.
10 Claude Sonnet 5 only
13 Shared
26 DeepSeek V4 Pro 0813 only
- Shared results
- 13
- Claude Sonnet 5 only
- 10
- DeepSeek V4 Pro 0813 only
- 26
- Like-for-like categories
- 2 / 8
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign 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
Like-for-like- Claude Sonnet 5
- 64.2
- Supported · #13/183
- DeepSeek V4 Pro 0813
- 52.2
- Supported · #58/183
- Basis
- BenchAlign lane · 9 vs 14 public rows
- Reading
- Claude Sonnet 5 leads · intervals overlap
Knowledge
Directional only- Claude Sonnet 5
- 67.1
- Supported · #20/181
- DeepSeek V4 Pro 0813
- 60.9
- Estimated · #37/181
- Basis
- BenchAlign lane · 6 vs 8 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Sonnet 5
- 76.4
- Unranked · 2 rankable rows
- DeepSeek V4 Pro 0813
- 58.7
- #18/22
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 5
- Not ranked
- DeepSeek V4 Pro 0813
- 80.4
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 5
- Not ranked
- DeepSeek V4 Pro 0813
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Sonnet 5
- 77.5
- #13/48
- DeepSeek V4 Pro 0813
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 5
- Not ranked
- DeepSeek V4 Pro 0813
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 5 | DeepSeek V4 Pro 0813 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 66.0Supported · #12/151 | 56.1Supported · #37/151 | Like-for-likeBenchAlign lane · 6 vs 11 public rows | Claude Sonnet 5 leads · intervals overlap |
| Coding | 64.2Supported · #13/183 | 52.2Supported · #58/183 | Like-for-likeBenchAlign lane · 9 vs 14 public rows | Claude Sonnet 5 leads · intervals overlap |
| Knowledge | 67.1Supported · #20/181 | 60.9Estimated · #37/181 | Directional onlyBenchAlign lane · 6 vs 8 public rows | Directional only |
| Reasoning | 76.4Unranked · 2 rankable rows | 58.7#18/22 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 80.4Unranked · 4 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 |
| Multimodal | 77.5#13/48 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
HLE
Knowledge
Claude Sonnet 5: 57.4%DeepSeek V4 Pro 0813: 42.7%Normalized gap 14.7Claude Sonnet 5 source DeepSeek V4 Pro 0813 sourceTerminal-Bench 2.0
Agentic
Claude Sonnet 5: 80.4%DeepSeek V4 Pro 0813: 67.9%Normalized gap 12.5Claude Sonnet 5 source DeepSeek V4 Pro 0813 sourceSWE-bench Pro
Coding
Claude Sonnet 5: 63.2%DeepSeek V4 Pro 0813: 55.4%Normalized gap 7.8Claude Sonnet 5 source DeepSeek V4 Pro 0813 sourceLiveCodeBench (Vals)
Coding
Claude Sonnet 5: 82.4%DeepSeek V4 Pro 0813: 87.5%Normalized gap 5.1Claude Sonnet 5 source DeepSeek V4 Pro 0813 sourceSWE-bench Verified
Coding
Claude Sonnet 5: 85.2%DeepSeek V4 Pro 0813: 80.6%Normalized gap 4.6Claude Sonnet 5 source DeepSeek V4 Pro 0813 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
- Claude Sonnet 5
- $0.007
- Fits in one request
- DeepSeek V4 Pro 0813
- $0.00087
- 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
- Claude Sonnet 5
- $0.13
- Fits in one request
- DeepSeek V4 Pro 0813
- $0.02436
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Sonnet 5
- $0.18
- Fits in one request
- DeepSeek V4 Pro 0813
- $0.01812
- Fits in one request
DeepSeek V4 Pro 0813 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.
SpecificationClaude Sonnet 5DeepSeek V4 Pro 0813
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
DeepSeek V4 Pro 0813
API model ID
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewDeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 5
$0.2 per 1M cached input tokens
Claude API pricingDeepSeek V4 Pro 0813
$0.003625 per 1M cached input tokens
Documented inputs
Claude Sonnet 5
text, image
Anthropic model overviewDeepSeek V4 Pro 0813
Documented outputs
Claude Sonnet 5
DeepSeek V4 Pro 0813
Provider availability
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewDeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricingReasoning profile
Claude Sonnet 5
Reasoning
DeepSeek V4 Pro 0813
Reasoning
Weight access
Claude Sonnet 5
Proprietary
DeepSeek V4 Pro 0813
Proprietary
License
Claude Sonnet 5
Proprietary
DeepSeek V4 Pro 0813
Proprietary
Release date
Claude Sonnet 5
2026-06-30
DeepSeek V4 Pro 0813
2026-08-13
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
- Claude Sonnet 5 has the higher public score estimate, 70.76 versus 66.35, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.13 vs $0.02436. Cache-heavy agent loop: $0.18 vs $0.01812.
- 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 evidence49 rows
Agentic
Terminal-Bench 3.0
Claude Sonnet 514.6%SourceDeepSeek V4 Pro 0813—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceDeepSeek V4 Pro 081367.9%SourceClaude Sonnet 5 leads this result
BrowseComp
Claude Sonnet 584.7%SourceDeepSeek V4 Pro 081383.4%SourceClaude Sonnet 5 leads this result
HLE w/ tools
Claude Sonnet 557.4%SourceDeepSeek V4 Pro 081360.0%SourceDeepSeek V4 Pro 0813 leads this result
OSWorld-Verified
Claude Sonnet 581.2%SourceDeepSeek V4 Pro 0813—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 574.5%SourceDeepSeek V4 Pro 081354.7%SourceClaude Sonnet 5 leads this result
Terminal-Bench 2.1
Claude Sonnet 5—DeepSeek V4 Pro 081387.9%SourceNot directly comparable
MCP Atlas
Claude Sonnet 5—DeepSeek V4 Pro 081373.6%SourceNot directly comparable
Toolathlon
Claude Sonnet 5—DeepSeek V4 Pro 081351.8%SourceNot directly comparable
CyberGym
Claude Sonnet 5—DeepSeek V4 Pro 081383.3%SourceNot directly comparable
Toolathlon-Verified
Claude Sonnet 5—DeepSeek V4 Pro 081374.1%SourceNot directly comparable
Agents' Last Exam
Claude Sonnet 5—DeepSeek V4 Pro 081325.7%SourceNot directly comparable
AutomationBench
Claude Sonnet 5—DeepSeek V4 Pro 081331.8%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Sonnet 585.2%SourceDeepSeek V4 Pro 081380.6%SourceClaude Sonnet 5 leads this result
SWE-bench Pro
Claude Sonnet 563.2%SourceDeepSeek V4 Pro 081355.4%SourceClaude Sonnet 5 leads this result
SWE Multilingual
Claude Sonnet 578.3%SourceDeepSeek V4 Pro 081376.2%SourceClaude Sonnet 5 leads this result
SWE Multimodal
Claude Sonnet 528.1%SourceDeepSeek V4 Pro 0813—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceDeepSeek V4 Pro 081367.9%SourceClaude Sonnet 5 leads this result
FrontierCode 1.1 Main
Claude Sonnet 542.7%SourceDeepSeek V4 Pro 0813—Not directly comparable
cursorBench32
Claude Sonnet 561.5%SourceDeepSeek V4 Pro 0813—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 582.4%SourceDeepSeek V4 Pro 081387.5%SourceDeepSeek V4 Pro 0813 leads this result
SWE-bench (Vals)
Claude Sonnet 579.6%SourceDeepSeek V4 Pro 081396.4%SourceDeepSeek V4 Pro 0813 leads this result
LiveCodeBench Pass@1-COT
Claude Sonnet 5—DeepSeek V4 Pro 081393.5%SourceNot directly comparable
Codeforces
Claude Sonnet 5—DeepSeek V4 Pro 08133206.0SourceNot directly comparable
Vibe Code Bench
Claude Sonnet 5—DeepSeek V4 Pro 081349.93%SourceNot directly comparable
Terminal-Bench 2.1
Claude Sonnet 5—DeepSeek V4 Pro 081387.9%SourceNot directly comparable
NL2Repo
Claude Sonnet 5—DeepSeek V4 Pro 081361.5%SourceNot directly comparable
deepSwe
Claude Sonnet 5—DeepSeek V4 Pro 081362.7%SourceNot directly comparable
DSBench-FullStack
Claude Sonnet 5—DeepSeek V4 Pro 081371.1%SourceNot directly comparable
DSBench-Hard
Claude Sonnet 5—DeepSeek V4 Pro 081367.2%SourceNot directly comparable
Reasoning
Knowledge
HLE
Claude Sonnet 557.4%SourceDeepSeek V4 Pro 081342.7%SourceClaude Sonnet 5 leads this result
HLE w/o tools
Claude Sonnet 543.2%SourceDeepSeek V4 Pro 0813—Not directly comparable
HLE-Verified
Claude Sonnet 531.0%SourceDeepSeek V4 Pro 0813—Not directly comparable
LABBench2
Claude Sonnet 580.1%SourceDeepSeek V4 Pro 0813—Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 588.9%SourceDeepSeek V4 Pro 081392.4%SourceDeepSeek V4 Pro 0813 leads this result
MMLU-Pro (Vals)
Claude Sonnet 587.5%SourceDeepSeek V4 Pro 081387.0%SourceClaude Sonnet 5 leads this result
MMLU-Pro
Claude Sonnet 5—DeepSeek V4 Pro 081387.5%SourceNot directly comparable
SimpleQA
Claude Sonnet 5—DeepSeek V4 Pro 081357.9%SourceNot directly comparable
Chinese-SimpleQA
Claude Sonnet 5—DeepSeek V4 Pro 081384.4%SourceNot directly comparable
GPQA
Claude Sonnet 5—DeepSeek V4 Pro 081390.1%SourceNot directly comparable
GPQA-D
Claude Sonnet 5—DeepSeek V4 Pro 081390.1%SourceNot directly comparable
Math
HMMT Feb 2026
Claude Sonnet 5—DeepSeek V4 Pro 081395.2%SourceNot directly comparable
IMOAnswerBench
Claude Sonnet 5—DeepSeek V4 Pro 081389.8%SourceNot directly comparable
Apex
Claude Sonnet 5—DeepSeek V4 Pro 081338.3%SourceNot directly comparable
Apex Shortlist
Claude Sonnet 5—DeepSeek V4 Pro 081390.2%SourceNot directly comparable
Multimodal
Frequently asked questions
Which is better, Claude Sonnet 5 or DeepSeek V4 Pro 0813?
Claude Sonnet 5 has the higher public score estimate, 70.76 versus 66.35, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Sonnet 5 or DeepSeek V4 Pro 0813?
Claude Sonnet 5 leads the public coding lane, 64.2 to 52.2, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Claude Sonnet 5 or DeepSeek V4 Pro 0813?
Claude Sonnet 5 leads the public agentic tasks lane, 66 to 56.1, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Sonnet 5 or DeepSeek V4 Pro 0813?
For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.00087 on DeepSeek V4 Pro 0813; repository review costs $0.13 and $0.02436; the cache-heavy agent loop costs $0.18 and $0.01812. Costs use the listed standard API rates.
Which has the larger context window, Claude Sonnet 5 or DeepSeek V4 Pro 0813?
Both models list the same context window, 1M.
Related comparisons
Last updated September 4, 2026
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