Updated November 12, 2026. Rank says Claude Sonnet 5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Sonnet 5.5 has the higher public score, 83.07 versus 64.51, and the 90% score intervals do not overlap. 15 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Claude Sonnet 5.5: $2 input / $10 output
DeepSeek V4.1 Flash 1017: $0.30 input / $1.20 output
Claude Sonnet 5.5: 1M
DeepSeek V4.1 Flash 1017: 1M
Claude Sonnet 5.5: September 28, 2026
DeepSeek V4.1 Flash 1017: October 17, 2026
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.
Code generation, repair, and software-engineering tasks
Claude Sonnet 5.5
Claude Sonnet 5.5 has the higher public coding point estimate, 81 to 59.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Claude Sonnet 5.5
Claude Sonnet 5.5 has the higher public agentic point estimate, 72.7 to 61.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
1K fresh input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
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.
Like-for-like · BenchAlign v5.8
Claude Sonnet 5.5 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
FrontierMath v2 (Tier 4)Math
Normalized gap 53.7FrontierMath v2 (Tiers 1-3)Math
Normalized gap 21.4Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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.
| Category | Claude Sonnet 5.5 | DeepSeek V4.1 Flash 1017 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 72.7Supported · #9/135 | 61.1Supported · #25/135 | Like-for-likeBenchAlign v5.8 lane · 19 vs 12 public rows | Claude Sonnet 5.5 leads · intervals overlap |
| Coding | 81.0Supported · #4/162 | 59.7Supported · #18/162 | Like-for-likeBenchAlign v5.8 lane · 12 vs 12 public rows | Claude Sonnet 5.5 leads |
| Knowledge | 82.1Supported · #6/180 | 65.1Supported · #29/180 | Like-for-likeBenchAlign v5.8 lane · 17 vs 3 public rows | Claude Sonnet 5.5 leads · intervals overlap |
| Reasoning | 77.0Supported · #6/160 | 59.5Estimated · #35/160 | Directional onlyBenchAlign v5.8 lane · 0 vs 3 public rows | Directional only |
| Multimodal | 78.9Estimated · #7/67 | 64.7Supported · #21/67 | Directional onlyBenchAlign v5.8 lane · 10 vs 5 public rows | Directional only |
| 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 | 89.7Unranked · 4 rankable rows | 65.6Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) 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.
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.
1K fresh input + 500 output tokens
DeepSeek V4.1 Flash 1017 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
DeepSeek V4.1 Flash 1017 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4.1 Flash 1017 has the lower modeled cost
Costs use the listed standard API rates.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5.5
DeepSeek V4.1 Flash 1017
Claude Sonnet 5.5
claude-sonnet-5-5
Anthropic Claude Sonnet 5.5 model documentationDeepSeek V4.1 Flash 1017
deepseek-flash
DeepSeek models and pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 5.5
$0.1 per 1M cached input tokens
Claude API pricingDeepSeek V4.1 Flash 1017
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingClaude Sonnet 5.5
DeepSeek V4.1 Flash 1017
text, image
DeepSeek vision guideClaude Sonnet 5.5
DeepSeek V4.1 Flash 1017
Claude Sonnet 5.5
Generally Available · Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, Claude Platform on AWS
Anthropic Claude Sonnet 5.5 model documentationDeepSeek V4.1 Flash 1017
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash-1017 API changelogClaude Sonnet 5.5
Reasoning
DeepSeek V4.1 Flash 1017
Reasoning
Claude Sonnet 5.5
Proprietary
DeepSeek V4.1 Flash 1017
Open Weight
Claude Sonnet 5.5
Proprietary
DeepSeek V4.1 Flash 1017
Open Weight
Claude Sonnet 5.5
2026-09-28
DeepSeek V4.1 Flash 1017
2026-10-17
Claude Sonnet 5.5 has the higher public score, 83.07 versus 64.51, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Claude Sonnet 5.5 has the higher public coding point estimate, 81 to 59.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Claude Sonnet 5.5 has the higher public agentic tasks point estimate, 72.7 to 61.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.007 on Claude Sonnet 5.5 and $0.0009 on DeepSeek V4.1 Flash 1017; repository review costs $0.13 and $0.0186; the cache-heavy agent loop costs $0.16 and $0.0192. Costs use the listed standard API rates.
Both models list the same context window, 1M.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 4.1
Not directly comparable
Terminal-Bench 4.0
Claude Sonnet 5.5 leads this result
HLE w/ tools
Claude Sonnet 5.5 leads this result
DRACO
Not directly comparable
Terminal-Bench-Science 0.1
Not directly comparable
LAB all-pass (Harvey held-out)
Not directly comparable
LAB criterion-pass (Harvey held-out)
Not directly comparable
Toolathlon Verified Pass@3
Not directly comparable
Toolathlon Verified Pass³
Not directly comparable
Toolathlon Verified avg. turns
Not directly comparable
AutomationBench (Zapier 1.0.6)
Not directly comparable
Toolathlon-Verified
Not directly comparable
Hermes Index
Not directly comparable
Hermes Bench
Not directly comparable
MazeBench
Claude Sonnet 5.5 leads this result
OSWorld 2.1
Not directly comparable
OSWorld 2.1 Offline
Not directly comparable
Binary Exploitation Benchmark
Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 5.5 leads this result
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
Not directly comparable
AutomationBench
Not directly comparable
Agents' Last Exam
Not directly comparable
CWE-bench v1
Not directly comparable
CWE-bench
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
CursorBench 4.0
Shared sourceClaude Sonnet 5.5 leads this result
SWE-bench Pro
Not directly comparable
SWE Multilingual
Not directly comparable
SWE Multimodal
Not directly comparable
DeepSWE
DeepSeek V4.1 Flash 1017 leads this result
FrontierCode 1.1 Extended
Not directly comparable
ProgramBench
Claude Sonnet 5.5 leads this result
FrontierSWE v2
Not directly comparable
Bug Hunt Bench
Shared sourceClaude Sonnet 5.5 leads this result
FrontierCode 2.0
Shared sourceClaude Sonnet 5.5 leads this result
VulcanBench Frontier v4
Shared sourceClaude Sonnet 5.5 leads this result
ScarfBench
Not directly comparable
Codeforces
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
NL2Repo
Not directly comparable
OpenHarmony Bench
Not directly comparable
ExtractBench Pro
Not directly comparable
ExtractBench (LlamaIndex)
Shared sourceClaude Sonnet 5.5 leads this result
ParseBench
Shared sourceClaude Sonnet 5.5 leads this result
Chartography (no tools)
Not directly comparable
Chartography (tools)
Claude Sonnet 5.5 leads this result
BenchCAD Vision2Code (no tools)
Not directly comparable
BenchCAD Vision2Code (tools)
Not directly comparable
Biomedical image analysis
Not directly comparable
OfficeQA
Not directly comparable
OfficeQA Pro
Not directly comparable
BabyVision w/ Python
Not directly comparable
ZeroBench w/ Python
Not directly comparable
HealthBench (raw)
Not directly comparable
HealthBench (length-adjusted)
Not directly comparable
HealthBench Professional (raw)
Not directly comparable
HealthBench Professional
Not directly comparable
BioMysteryBench (human-solvable)
Not directly comparable
BioMysteryBench (human-difficult)
Not directly comparable
SpatialBench Verified
Not directly comparable
SingleCellBench
Not directly comparable
Protein Design
Not directly comparable
Morphology-to-molecule matching
Not directly comparable
Medicinal chemistry
Not directly comparable
Protein Design library ranking
Not directly comparable
De novo protein-binder design
Not directly comparable
Protocols (troubleshooting)
Not directly comparable
Protocols (understanding)
Not directly comparable
HLE w/o tools
Not directly comparable
PhysicianBench
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HLE
Not directly comparable
ArXivMath Aug. 2026 (no tools)
Not directly comparable
ArXivMath Aug. 2026 (tools)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Sonnet 5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Sonnet 5.5 leads this result
Apex
Not directly comparable
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Last updated November 12, 2026
