Knowledge
Directional only- Claude Sonnet 5
- 66.6
- Supported · #20/183
- DeepSeek-R1
- 47.6
- Estimated · #96/183
- Basis
- BenchAlign lane · 6 vs 0 public rows
- Reading
- Directional only
Claude Sonnet 5 vs DeepSeek-R1
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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-R1
DeepSeek-R1 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-R1
DeepSeek-R1 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-R1 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
DeepSeek-R1 is not ranked on the public lane for agentic, so no winner is named for agentic.
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-R1 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.
24 Claude Sonnet 5 only
- Shared results
- 0
- Claude Sonnet 5 only
- 24
- DeepSeek-R1 only
- 0
- Like-for-like categories
- 0 / 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.
Agentic
Not comparable- Claude Sonnet 5
- 65.8
- Supported · #11/152
- DeepSeek-R1
- Not ranked
- Basis
- BenchAlign lane · 6 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- Claude Sonnet 5
- 64.0
- Supported · #13/151
- DeepSeek-R1
- Not ranked
- Basis
- BenchAlign lane · 10 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Sonnet 5
- 77.4
- Unranked · 2 rankable rows
- DeepSeek-R1
- 59.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 5
- Not ranked
- DeepSeek-R1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 5
- Not ranked
- DeepSeek-R1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Sonnet 5
- 77.5
- #13/48
- DeepSeek-R1
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 5
- Not ranked
- DeepSeek-R1
- 46.1
- #90/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 5 | DeepSeek-R1 | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 66.6Supported · #20/183 | 47.6Estimated · #96/183 | Directional onlyBenchAlign lane · 6 vs 0 public rows | Directional only |
| Agentic | 65.8Supported · #11/152 | Not ranked | Not comparableBenchAlign lane · 6 vs 0 public rows | Not comparable |
| Coding | 64.0Supported · #13/151 | Not ranked | Not comparableBenchAlign lane · 10 vs 0 public rows | Not comparable |
| Reasoning | 77.4Unranked · 2 rankable rows | 59.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 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 | 46.1#90/123 | 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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-R1
- $0.00165
- Fits in one request
DeepSeek-R1 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-R1
- $0.03407
- Fits in one request
DeepSeek-R1 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-R1
- $0.0609
- Does not fit in one request
DeepSeek-R1 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-R1
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
DeepSeek-R1
128K
API model ID
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewDeepSeek-R1
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-R1
$0.14 per 1M cached input tokens
Documented inputs
Claude Sonnet 5
text, image
Anthropic model overviewDeepSeek-R1
Not sourced
Documented outputs
Claude Sonnet 5
DeepSeek-R1
Not sourced
Provider availability
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewDeepSeek-R1
Not sourced
Reasoning profile
Claude Sonnet 5
Reasoning
DeepSeek-R1
Reasoning
Weight access
Claude Sonnet 5
Proprietary
DeepSeek-R1
Open Weight
License
Claude Sonnet 5
Proprietary
DeepSeek-R1
Open Weight
Release date
Claude Sonnet 5
2026-06-30
DeepSeek-R1
2025-01-20
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.13 vs $0.03407. Cache-heavy agent loop: $0.18 vs $0.0609.
- 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-R1
API / mo$2,055
Self-host / mo$18,221
Break-even583M/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 evidence24 rows
Agentic
Terminal-Bench 3.0
Claude Sonnet 514.6%SourceDeepSeek-R1—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceDeepSeek-R1—Not directly comparable
BrowseComp
Claude Sonnet 584.7%SourceDeepSeek-R1—Not directly comparable
HLE w/ tools
Claude Sonnet 557.4%SourceDeepSeek-R1—Not directly comparable
OSWorld-Verified
Claude Sonnet 581.2%SourceDeepSeek-R1—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 574.5%SourceDeepSeek-R1—Not directly comparable
Coding
SWE-bench Verified
Claude Sonnet 585.2%SourceDeepSeek-R1—Not directly comparable
SWE-bench Pro
Claude Sonnet 563.2%SourceDeepSeek-R1—Not directly comparable
SWE Multilingual
Claude Sonnet 578.3%SourceDeepSeek-R1—Not directly comparable
SWE Multimodal
Claude Sonnet 528.1%SourceDeepSeek-R1—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceDeepSeek-R1—Not directly comparable
FrontierCode 1.1 Main
Claude Sonnet 542.7%SourceDeepSeek-R1—Not directly comparable
cursorBench32
Claude Sonnet 561.5%SourceDeepSeek-R1—Not directly comparable
VulcanBench CII v1
Claude Sonnet 589.2%SourceDeepSeek-R1—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 582.4%SourceDeepSeek-R1—Not directly comparable
SWE-bench (Vals)
Claude Sonnet 579.6%SourceDeepSeek-R1—Not directly comparable
Knowledge
HLE
Claude Sonnet 557.4%SourceDeepSeek-R1—Not directly comparable
HLE w/o tools
Claude Sonnet 543.2%SourceDeepSeek-R1—Not directly comparable
HLE-Verified
Claude Sonnet 531.0%SourceDeepSeek-R1—Not directly comparable
LABBench2
Claude Sonnet 580.1%SourceDeepSeek-R1—Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 588.9%SourceDeepSeek-R1—Not directly comparable
MMLU-Pro (Vals)
Claude Sonnet 587.5%SourceDeepSeek-R1—Not directly comparable
Multimodal
Frequently asked questions
Which is better, Claude Sonnet 5 or DeepSeek-R1?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Claude Sonnet 5 or DeepSeek-R1?
DeepSeek-R1 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Claude Sonnet 5 or DeepSeek-R1?
DeepSeek-R1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Sonnet 5 or DeepSeek-R1?
For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.00164 on DeepSeek-R1; repository review costs $0.13 and $0.03407; the cache-heavy agent loop costs $0.18 and $0.0609. DeepSeek-R1 does not fit this workload in one request.
Which has the larger context window, Claude Sonnet 5 or DeepSeek-R1?
Claude Sonnet 5 has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 10, 2026
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