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Model comparison

Claude Opus 4.7 (Adaptive) vs DeepSeek V4 Pro

Data verified

Head-to-head evidence from 9 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

66.27/100
Margin
5.6pts
← winning
60.66/100
3 category wins0 category wins

Public leaderboard positions: Claude Opus 4.7 (Adaptive) #27 (Estimated); DeepSeek V4 Pro #46 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Claude Opus 4.7 (Adaptive) and DeepSeek V4 Pro share 9 comparable benchmark results. 3 of 8 categories are comparable. 29 results are unique to Claude Opus 4.7 (Adaptive); 14 to DeepSeek V4 Pro.

Updated July 23, 2026
Shared results
9
Claude Opus 4.7 (Adaptive) only
29
DeepSeek V4 Pro only
14
Comparable categories
3 / 8

Pick Claude Opus 4.7 (Adaptive) if you want the stronger benchmark profile. DeepSeek V4 Pro only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.

Confidence note. This is a partial-evidence comparison with 9 shared benchmark results across 4 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

Claude Opus 4.7 (Adaptive) is clearly ahead on the BenchAlign aggregate, 66.27 to 60.66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Claude Opus 4.7 (Adaptive)'s sharpest advantage is in knowledge, where it averages 60 against 41.3. The single biggest benchmark swing on the page is HLE, 54.7% to 7.7%.

Claude Opus 4.7 (Adaptive) is also the more expensive model on tokens at $5.00 input / $25.00 output per 1M tokens, versus $0.43 input / $0.87 output per 1M tokens for DeepSeek V4 Pro. That is roughly 28.7x on output cost alone. Claude Opus 4.7 (Adaptive) is the reasoning model in the pair, while DeepSeek V4 Pro is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for Claude Opus 4.7 (Adaptive) and DeepSeek V4 Pro
CategoryClaude Opus 4.7 (Adaptive)ΔDeepSeek V4 Pro
KnowledgeClaude Opus 4.7 (Adaptive)60.0Margin← 18.7DeepSeek V4 Pro41.3
AgenticClaude Opus 4.7 (Adaptive)75.1Margin← 16.0DeepSeek V4 Pro59.1
CodingClaude Opus 4.7 (Adaptive)78.6Margin← 13.3DeepSeek V4 Pro65.3
ReasoningClaude Opus 4.7 (Adaptive)75.8MarginNo overlapDeepSeek V4 ProNot measured
MathClaude Opus 4.7 (Adaptive)Not measuredMarginNo overlapDeepSeek V4 Pro31.7
MultimodalClaude Opus 4.7 (Adaptive)65.1MarginNo overlapDeepSeek V4 ProNot measured

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · Claude Opus 4.7 (Adaptive)B · DeepSeek V4 Pro

  1. HLE

    Knowledge
    Source ↗
    A 54.7%B 7.7%
    Winner: Claude Opus 4.7 (Adaptive)Δ 47
    HLE: Claude Opus 4.7 (Adaptive) scored 54.7%; DeepSeek V4 Pro scored 7.7%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  2. GPQA

    Knowledge
    Source ↗
    A 94.2%B 72.9%
    Winner: Claude Opus 4.7 (Adaptive)Δ 21.3
    GPQA: Claude Opus 4.7 (Adaptive) scored 94.2%; DeepSeek V4 Pro scored 72.9%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  3. SWE-bench Verified

    Coding
    Source ↗
    A 87.6%B 73.6%
    Winner: Claude Opus 4.7 (Adaptive)Δ 14
    SWE-bench Verified: Claude Opus 4.7 (Adaptive) scored 87.6%; DeepSeek V4 Pro scored 73.6%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  4. SWE-bench Pro

    Coding
    Source ↗
    A 64.3%B 52.1%
    Winner: Claude Opus 4.7 (Adaptive)Δ 12.2
    SWE-bench Pro: Claude Opus 4.7 (Adaptive) scored 64.3%; DeepSeek V4 Pro scored 52.1%. Claude Opus 4.7 (Adaptive) wins this benchmark.

  5. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 69.4%B 59.1%
    Winner: Claude Opus 4.7 (Adaptive)Δ 10.3
    Terminal-Bench 2.0: Claude Opus 4.7 (Adaptive) scored 69.4%; DeepSeek V4 Pro scored 59.1%. Claude Opus 4.7 (Adaptive) wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricClaude Opus 4.7 (Adaptive)DeepSeek V4 ProComparison
Input / output priceUSD per 1M tokensClaude Opus 4.7 (Adaptive)$5 input / $25 outputDeepSeek V4 Pro$0.435 input / $0.87 outputDeepSeek V4 Pro has the lower combined listed price.
Generation speedtokens per secondClaude Opus 4.7 (Adaptive)Not availableDeepSeek V4 ProNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenClaude Opus 4.7 (Adaptive)Not availableDeepSeek V4 ProNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensClaude Opus 4.7 (Adaptive)1MDeepSeek V4 Pro1MListed context windows are equal.

Benchmark Deep Dive

AgenticClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)DeepSeek V4 ProResult
Terminal-Bench 2.0Source 69.4%59.1%Claude Opus 4.7 (Adaptive) leads
BrowseCompSource 79.3%—Not comparable
MCP AtlasSource 77.3%69.4%Claude Opus 4.7 (Adaptive) leads
OSWorld-VerifiedSource 78%—Not comparable
CyberGymSource 73.1%—Not comparable
AA Agentic IndexSource 44.4%—Not comparable
τ²-bench resultsSource 88.6%—Not comparable
GDPval-AASource 49.8%—Not comparable
GDPval-AASource 1495—Not comparable
OSWorld 2.0Source 18.2%—Not comparable
JobBenchSource 45.9%—Not comparable
AA ITBenchSource 46.7%—Not comparable
ToolathlonSource —46.3%Not comparable
Claw-EvalSource —59.8%Not comparable
Gert LabsSource —50.28%Not comparable
ResearchClawBenchSource —17.1%Not comparable
CodingClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)DeepSeek V4 ProResult
SWE-bench VerifiedSource 87.6%73.6%Claude Opus 4.7 (Adaptive) leads
SWE-bench ProSource 64.3%52.1%Claude Opus 4.7 (Adaptive) leads
Terminal-Bench 2.0Source 69.4%59.1%Claude Opus 4.7 (Adaptive) leads
AA Coding IndexSource 73.6%—Not comparable
AA-SciCodeSource 54.5%—Not comparable
SWE MultilingualSource —69.8%Not comparable
Reasoning
BenchmarkClaude Opus 4.7 (Adaptive)DeepSeek V4 ProResult
MRCR v2 128K-256KSource 59.2%—Not comparable
ARC-AGI-2Source 75.8%—Not comparable
AA-LCRSource 70.3%—Not comparable
CritPtSource 12.0%—Not comparable
MRCR 1MSource —44.7%Not comparable
CorpusQA 1MSource —35.6%Not comparable
KnowledgeClaude Opus 4.7 (Adaptive) wins
BenchmarkClaude Opus 4.7 (Adaptive)DeepSeek V4 ProResult
GPQASource 94.2%72.9%Claude Opus 4.7 (Adaptive) leads
GPQA-DSource 94.2%72.9%Claude Opus 4.7 (Adaptive) leads
HLESource 54.7%7.7%Claude Opus 4.7 (Adaptive) leads
HLE w/o toolsSource 46.9%—Not comparable
Artificial Analysis Intelligence IndexSource 53.5%—Not comparable
AA-GPQA DiamondSource 91.4%—Not comparable
AA-HLESource 39.6%—Not comparable
AA-Omniscience IndexSource 26.2%—Not comparable
AA-Omniscience AccuracySource 45.8%—Not comparable
AA-Omniscience Hallucination RateSource 36.2%—Not comparable
MMLU-ProSource —82.9%Not comparable
SimpleQASource —45%Not comparable
Chinese-SimpleQASource —75.8%Not comparable
Math
BenchmarkClaude Opus 4.7 (Adaptive)DeepSeek V4 ProResult
FrontierMath (legacy)Source 43.8%—Not comparable
HMMT Feb 2026Source —31.7%Not comparable
IMOAnswerBenchSource —35.3%Not comparable
ApexSource —0.4%Not comparable
Apex ShortlistSource —9.2%Not comparable
Multimodal
BenchmarkClaude Opus 4.7 (Adaptive)DeepSeek V4 ProResult
OfficeQA ProSource 43.6%—Not comparable
CharXivSource 91%—Not comparable
CharXiv w/o toolsSource 82.1%—Not comparable
AA-MMMU-ProSource 78.8%—Not comparable
Design Arena WebsiteSource 13251264Claude Opus 4.7 (Adaptive) leads
Inst. Following
BenchmarkClaude Opus 4.7 (Adaptive)DeepSeek V4 ProResult
AA-IFBenchSource 58.6%—Not comparable
Frequently Asked Questions (4)

Which is better, Claude Opus 4.7 (Adaptive) or DeepSeek V4 Pro?

Claude Opus 4.7 (Adaptive) is ahead on BenchLM's BenchAlign leaderboard, 66.27 to 60.66. The biggest single separator in this matchup is HLE, where the scores are 54.7% and 7.7%.

Which is better for knowledge tasks, Claude Opus 4.7 (Adaptive) or DeepSeek V4 Pro?

Claude Opus 4.7 (Adaptive) has the edge for knowledge tasks in this comparison, averaging 60 versus 41.3. Inside this category, HLE is the benchmark that creates the most daylight between them.

Which is better for coding, Claude Opus 4.7 (Adaptive) or DeepSeek V4 Pro?

Claude Opus 4.7 (Adaptive) has the edge for coding in this comparison, averaging 78.6 versus 65.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, Claude Opus 4.7 (Adaptive) or DeepSeek V4 Pro?

Claude Opus 4.7 (Adaptive) has the edge for agentic tasks in this comparison, averaging 75.1 versus 59.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.

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