Agentic
Directional only- Claude Sonnet 5
- 65.9
- Supported · #11/153
- DeepSeek V3
- 36.7
- Estimated · #131/153
- Basis
- BenchAlign lane · 7 vs 0 public rows
- Reading
- Directional only
Claude Sonnet 5 vs DeepSeek V3
Updated September 14, 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, 69.92 versus 41.55, and the 90% score intervals do not overlap.
1 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.
Long documents
Prompts that approach the documented context limit
Claude Sonnet 5
Claude Sonnet 5 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V3
DeepSeek V3 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
DeepSeek V3
DeepSeek V3 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
DeepSeek V3 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 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
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 does not fit this workload in one request.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
25 Claude Sonnet 5 only
1 Shared
5 DeepSeek V3 only
- Shared results
- 1
- Claude Sonnet 5 only
- 25
- DeepSeek V3 only
- 5
- Like-for-like categories
- 0 / 8
3 categories rest 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
Directional only- Claude Sonnet 5
- 64.1
- Supported · #13/152
- DeepSeek V3
- 39.3
- Estimated · #119/152
- Basis
- BenchAlign lane · 11 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- Claude Sonnet 5
- 66.6
- Supported · #20/183
- DeepSeek V3
- 39.3
- Estimated · #139/183
- Basis
- BenchAlign lane · 6 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Sonnet 5
- 77.4
- Unranked · 2 rankable rows
- DeepSeek V3
- 41.2
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 5
- Not ranked
- DeepSeek V3
- 26.2
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 5
- Not ranked
- DeepSeek V3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Sonnet 5
- 77.5
- #13/48
- DeepSeek V3
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 5
- Not ranked
- DeepSeek V3
- 39.9
- #104/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 5 | DeepSeek V3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 65.9Supported · #11/153 | 36.7Estimated · #131/153 | Directional onlyBenchAlign lane · 7 vs 0 public rows | Directional only |
| Coding | 64.1Supported · #13/152 | 39.3Estimated · #119/152 | Directional onlyBenchAlign lane · 11 vs 2 public rows | Directional only |
| Knowledge | 66.6Supported · #20/183 | 39.3Estimated · #139/183 | Directional onlyBenchAlign lane · 6 vs 2 public rows | Directional only |
| Reasoning | 77.4Unranked · 2 rankable rows | 41.2Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 26.2Unranked · 1 rankable row | 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 | 39.9#104/123 | 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.
SWE-bench Verified
Coding
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 V3
- $0.00082
- Fits in one request
DeepSeek V3 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 V3
- $0.0168
- Fits in one request
DeepSeek V3 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 V3
- $0.0304
- Does not fit in one request
DeepSeek V3 does not fit this workload in one request.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Sonnet 5DeepSeek V3
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
DeepSeek V3
128K
API model ID
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewDeepSeek V3
Not sourced
Cached-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 V3
$0.07 per 1M cached input tokens
Documented inputs
Claude Sonnet 5
text, image
Anthropic model overviewDeepSeek V3
Not sourced
Documented outputs
Claude Sonnet 5
DeepSeek V3
Not sourced
Provider availability
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewDeepSeek V3
Not sourced
Reasoning profile
Claude Sonnet 5
Reasoning
DeepSeek V3
Non-Reasoning
Weight access
Claude Sonnet 5
Proprietary
DeepSeek V3
Open Weight
License
Claude Sonnet 5
Proprietary
DeepSeek V3
Open Weight
Release date
Claude Sonnet 5
2026-06-30
DeepSeek V3
2024-12-26
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, 69.92 versus 41.55, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.13 vs $0.0168. Cache-heavy agent loop: $0.18 vs $0.0304.
- Context tradeoff
- Claude Sonnet 5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Claude Sonnet 5
API / mo$9,000
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
DeepSeek V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
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
Terminal-Bench 3.0
Claude Sonnet 514.6%SourceDeepSeek V3—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceDeepSeek V3—Not directly comparable
BrowseComp
Claude Sonnet 584.7%SourceDeepSeek V3—Not directly comparable
HLE w/ tools
Claude Sonnet 557.4%SourceDeepSeek V3—Not directly comparable
OSWorld-Verified
Claude Sonnet 581.2%SourceDeepSeek V3—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 574.5%SourceDeepSeek V3—Not directly comparable
ApprenticeBench
Claude Sonnet 516%SourceDeepSeek V3—Not directly comparable
Coding
SWE-bench Verified
Claude Sonnet 585.2%SourceDeepSeek V342%SourceClaude Sonnet 5 leads this result
SWE-bench Pro
Claude Sonnet 563.2%SourceDeepSeek V3—Not directly comparable
SWE Multilingual
Claude Sonnet 578.3%SourceDeepSeek V3—Not directly comparable
SWE Multimodal
Claude Sonnet 528.1%SourceDeepSeek V3—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceDeepSeek V3—Not directly comparable
FrontierCode 1.1 Main
Claude Sonnet 542.7%SourceDeepSeek V3—Not directly comparable
cursorBench32
Claude Sonnet 561.5%SourceDeepSeek V3—Not directly comparable
VulcanBench CII v1
Claude Sonnet 589.2%SourceDeepSeek V3—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 582.4%SourceDeepSeek V3—Not directly comparable
SWE-bench (Vals)
Claude Sonnet 579.6%SourceDeepSeek V3—Not directly comparable
cursorBench40
Claude Sonnet 534.1%SourceDeepSeek V3—Not directly comparable
LiveCodeBench
Claude Sonnet 5—DeepSeek V337.6%SourceNot directly comparable
Knowledge
HLE
Claude Sonnet 557.4%SourceDeepSeek V3—Not directly comparable
HLE w/o tools
Claude Sonnet 543.2%SourceDeepSeek V3—Not directly comparable
HLE-Verified
Claude Sonnet 531.0%SourceDeepSeek V3—Not directly comparable
LABBench2
Claude Sonnet 580.1%SourceDeepSeek V3—Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 588.9%SourceDeepSeek V3—Not directly comparable
MMLU-Pro (Vals)
Claude Sonnet 587.5%SourceDeepSeek V3—Not directly comparable
GPQA
Claude Sonnet 5—DeepSeek V359.1%SourceNot directly comparable
MMLU-Pro
Claude Sonnet 5—DeepSeek V375.9%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Claude Sonnet 5—DeepSeek V31.724%SourceNot directly comparable
Multimodal
Instruction following
IFEval
Claude Sonnet 5—DeepSeek V386.1%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Sonnet 5 or DeepSeek V3?
Claude Sonnet 5 has the higher public score, 69.92 versus 41.55, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Claude Sonnet 5 or DeepSeek V3?
Claude Sonnet 5 scores higher for coding on the public lane, 64.1 to 39.3. DeepSeek V3 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, Claude Sonnet 5 or DeepSeek V3?
Claude Sonnet 5 scores higher for agentic tasks on the public lane, 65.9 to 36.7. DeepSeek V3 is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Claude Sonnet 5 or DeepSeek V3?
For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.00082 on DeepSeek V3; repository review costs $0.13 and $0.0168; the cache-heavy agent loop costs $0.18 and $0.0304. DeepSeek V3 does not fit this workload in one request.
Which has the larger context window, Claude Sonnet 5 or DeepSeek V3?
Claude Sonnet 5 has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 14, 2026
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