Updated October 25, 2026. Rank says Claude Opus 4.7 (Adaptive) is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Claude Opus 4.7 (Adaptive) has the higher public point estimate, 68.67 versus 65.78. Their conditional score ranges overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Anthropic
68.67/100
Estimated · Public rank #23
Conditional range 54.3–83.0
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.
Tool use, computer use, and multi-step task completion
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 has the higher public agentic point estimate, 61.7 to 60.4, 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. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.
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.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Opus 4.7 (Adaptive) is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
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.
Directional only · BenchAlign v5.8
DeepSeek V4.1 Flash 1017 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.
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.
HLEKnowledge
Normalized gap 12.1GPQAKnowledge
Normalized gap 3.3Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
| Category | Claude Opus 4.7 (Adaptive) | DeepSeek V4.1 Flash 1017 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 60.4Supported · #27/132 | 61.7Supported · #24/132 | Like-for-likeBenchAlign v5.8 lane · 7 vs 11 public rows | DeepSeek V4.1 Flash 1017 leads · intervals overlap |
| Coding | 55.7Estimated · #31/153 | 61.1Supported · #18/153 | Directional onlyBenchAlign v5.8 lane · 3 vs 8 public rows | Directional only |
| Knowledge | 65.5Estimated · #30/187 | 64.4Supported · #37/187 | Directional onlyBenchAlign v5.8 lane · 4 vs 3 public rows | Directional only |
| Reasoning | 57.4Unranked · 3 rankable rows | 82.5Unranked · 4 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 51.3#50/56 | 78.8Unranked · 1 rankable row | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| 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 | Not ranked | 70.1Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, 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
Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.
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 Opus 4.7 (Adaptive)
1M
DeepSeek V4.1 Flash 1017
Claude Opus 4.7 (Adaptive)
Not sourced
DeepSeek 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 Opus 4.7 (Adaptive)
Not published
DeepSeek V4.1 Flash 1017
$0.006 per 1M cached input tokens
DeepSeek: Models & PricingClaude Opus 4.7 (Adaptive)
Not sourced
DeepSeek V4.1 Flash 1017
text, image
DeepSeek vision guideClaude Opus 4.7 (Adaptive)
Not sourced
DeepSeek V4.1 Flash 1017
Claude Opus 4.7 (Adaptive)
Not sourced
DeepSeek V4.1 Flash 1017
Generally Available · DeepSeek API, open weights
DeepSeek-V4.1-Flash-1017 API changelogClaude Opus 4.7 (Adaptive)
Reasoning
DeepSeek V4.1 Flash 1017
Reasoning
Claude Opus 4.7 (Adaptive)
Proprietary
DeepSeek V4.1 Flash 1017
Open Weight
Claude Opus 4.7 (Adaptive)
Proprietary
DeepSeek V4.1 Flash 1017
Open Weight
Claude Opus 4.7 (Adaptive)
2026-04-16
DeepSeek V4.1 Flash 1017
2026-10-17
Claude Opus 4.7 (Adaptive) has the higher public point estimate, 68.67 versus 65.78. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
DeepSeek V4.1 Flash 1017 scores higher for coding on the public lane, 61.1 to 55.7. Claude Opus 4.7 (Adaptive) 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.
DeepSeek V4.1 Flash 1017 has the higher public agentic tasks point estimate, 61.7 to 60.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0175 on Claude Opus 4.7 (Adaptive) and $0.0009 on DeepSeek V4.1 Flash 1017; repository review costs $0.325 and $0.0186; the cache-heavy agent loop costs $1.35 and $0.0192. Claude Opus 4.7 (Adaptive) has no published cached-input rate, so cached tokens use its listed input rate.
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 2.0
Not directly comparable
BrowseComp
Not directly comparable
MCP Atlas
Not directly comparable
OSWorld-Verified
Not directly comparable
CyberGym
DeepSeek V4.1 Flash 1017 leads this result
OSWorld 2.0
Not directly comparable
JobBench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
Terminal-Bench 4.0
Not directly comparable
ExploitGym
Not directly comparable
HLE w/ tools
Not directly comparable
AutomationBench
Not directly comparable
Agents' Last Exam
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
CWE-bench v1
Not directly comparable
MazeBench
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Codeforces
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
terminalBench3
Not directly comparable
DeepSWE
Not directly comparable
ProgramBench
Not directly comparable
NL2Repo
Not directly comparable
OpenHarmony Bench
Not directly comparable
Bug Hunt Bench
Not directly comparable
MRCR v2 128K-256K
Not directly comparable
ARC-AGI-2
Not directly comparable
ARC-AGI-3
Not directly comparable
STEMBench
Not directly comparable
JevBench 1.4
Not directly comparable
JevBench 1.5
Not directly comparable
OfficeQA Pro
Not directly comparable
CharXiv
Not directly comparable
CharXiv w/o tools
Not directly comparable
ExtractBench (LlamaIndex)
Not directly comparable
ParseBench
Not directly comparable
Chartography (tools)
Not directly comparable
BabyVision w/ Python
Not directly comparable
ZeroBench w/ Python
Not directly comparable
GPQA
Claude Opus 4.7 (Adaptive) leads this result
GPQA-D
Claude Opus 4.7 (Adaptive) leads this result
HLE
Claude Opus 4.7 (Adaptive) leads this result
HLE w/o tools
Not directly comparable
FrontierMath (legacy)
Not directly comparable
Apex
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
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Last updated October 25, 2026
