Noometry

Model comparison

Claude Opus 4.7 vs DeepSeek-R1

Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 42.3 on the Noometry Index. DeepSeek-R1 costs 11× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.

Last verified . 31 shared benchmarks.

Claude Opus 4.7 Anthropic

58.3

Rank #19 Confirmed

DeepSeek-R1 DeepSeek

42.3

Rank #115 Confirmed

Summary

  • They share 31 benchmarks with published results for both. Claude Opus 4.7 scores higher in 9 categories and DeepSeek-R1 in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where Claude Opus 4.7 leads 53.8 to 18.6.
  • The biggest single-benchmark swing is ARC-AGI-2: 75.8% for Claude Opus 4.7 and 1.3% for DeepSeek-R1.
  • DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $5 / $25 for Claude Opus 4.7.
  • Claude Opus 4.7 accepts more context: 1M tokens versus 164K.

Side by side

Claude Opus 4.7DeepSeek-R1
ProviderAnthropicDeepSeek
Noometry Index58.342.3
Released2026-04-142025-01-20
WeightsProprietaryProprietary
Context window1M164K
Max output128K64K
Input $ / M tokens$5$0.50
Output $ / M tokens$25$2.15
Results tracked6652

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Category by category

Coding Claude Opus 4.7 leads

Claude Opus 4.7: 59.6 (#13), DeepSeek-R1: 46.3 (#68)

BenchmarkClaude Opus 4.7DeepSeek-R1
SciCode54.5%35.7%
WeirdML76.4%41.6%
LMArena Coding15181427
ALE-Bench1,323804.12
SWE-bench Verified83.5%—
FrontierCode38.5%—
Aider Polyglot—71.4%
LMArena WebDev1558—
GSO44.1%—
LiveBench Coding—66.7%
MirrorCode31.1%—
AlgoTune—1.7

Agentic & Tool Use Claude Opus 4.7 leads

Claude Opus 4.7: 47.9 (#10), DeepSeek-R1: 30.7 (#75)

BenchmarkClaude Opus 4.7DeepSeek-R1
Terminal-Bench80.2%—
APEX-Agents49.2%—
OSWorld 2.018.2%—
τ²-bench Banking40.2%—
DeepResearch Bench—35.1%
PostTrainBench28.6%—
BALROG—34.9%
ExploitBench26.5%—
GBAEval43.8%—
GDP.pdf21%—
LMArena Search1233—
METR Time Horizons—53.8%
Vending-Bench 210,937—

Reasoning Claude Opus 4.7 leads

Claude Opus 4.7: 53.8 (#29), DeepSeek-R1: 18.6 (#278)

BenchmarkClaude Opus 4.7DeepSeek-R1
ARC-AGI-275.8%1.3%
SimpleBench61.7%40.8%
Kagi LLM Benchmark80.7%69.4%
ARC-AGI-193.5%21.2%
CritPt12%1.1%
LMArena Hard Prompts15061416
Epoch Capabilities Index156.25141.29
ForecastBench60.360
NYT Connections (extended)39%—
Chess Puzzles30%—
Thematic Generalization72.8%—
EBR-Bench19%—
LiveBench Reasoning—83.2%
Mystery Game Puzzles28%—
DTBench94.7%—
LiveBench Data Analysis—69.8%
LMCA52.2%—
LiveBench—71.6%

Math Claude Opus 4.7 leads

Claude Opus 4.7: 66.7 (#26), DeepSeek-R1: 43.8 (#79)

BenchmarkClaude Opus 4.7DeepSeek-R1
OTIS Mock AIME 2024-202597.8%66.4%
LMArena Math14991400
FrontierMath (Tiers 1-3)70.2%—
FrontierMath Tier 431.7%—
MathArena Final-Answer Competitions73.6%—
ProofBench54%—
Omni-MATH—42.4%
LiveBench Math—80.7%
MATH Level 5—96.6%
FrontierMath (Feb 2025 set)43.8%—
FrontierMath Tier 4 (v1)22.9%—

Knowledge Claude Opus 4.7 leads

Claude Opus 4.7: 62.6 (#23), DeepSeek-R1: 44.5 (#87)

BenchmarkClaude Opus 4.7DeepSeek-R1
GPQA Diamond90.2%76.3%
Vectara Hallucination Rate12%11.3%
LMArena Expert15211394
Humanity's Last Exam36.2%—
SimpleQA Verified51.7%—
MMLU-Pro—79.3%
Confabulations—12.7%
GPQA (HELM)—66.6%

Multimodal Not comparable

Claude Opus 4.7: 41.2 (#38), DeepSeek-R1: —

BenchmarkClaude Opus 4.7DeepSeek-R1
LMArena Vision1316—
Blueprint-Bench 224.5%—
Furniture Assembly33.3%—
LMArena Document1495—

Multilingual Claude Opus 4.7 leads

Claude Opus 4.7: 57.3 (#10), DeepSeek-R1: 52.4 (#85)

BenchmarkClaude Opus 4.7DeepSeek-R1
LMArena Non-English14801412
LMArena Chinese15311442
LMArena French15031417
LMArena German14951404
LMArena Japanese14721391
LMArena Korean14641360
LMArena Russian14941423
LMArena Spanish14951411

Instruction Following Claude Opus 4.7 leads

Claude Opus 4.7: 78.4 (#10), DeepSeek-R1: 72.0 (#143)

BenchmarkClaude Opus 4.7DeepSeek-R1
LMArena Instruction Following14981382
LiveBench Instruction Following—80.5%
IFEval—78.4%

Long Context Too close to call

Claude Opus 4.7: 46.2 (#25), DeepSeek-R1: 45.4 (#36)

BenchmarkClaude Opus 4.7DeepSeek-R1
LMArena Longer Query15051391
Fiction.LiveBench—75%

Writing & Preference Claude Opus 4.7 leads

Claude Opus 4.7: 75.1 (#8), DeepSeek-R1: 61.4 (#88)

BenchmarkClaude Opus 4.7DeepSeek-R1
LMArena Text14901428
LMArena Creative Writing14861405
EQ-Bench Creative Writing19141500
LMArena Multi-Turn15051405
Short-Story Creative Writing—83%
WildBench—82.8%
EQ-Bench 41311—
LiveBench Language—48.5%

Frequently asked questions

Is Claude Opus 4.7 better than DeepSeek-R1?

Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 42.3 on the Noometry Index. DeepSeek-R1 costs 11× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.

Which is cheaper, Claude Opus 4.7 or DeepSeek-R1?

DeepSeek-R1 is cheaper. It lists at $0.50 per million input tokens and $2.15 per million output tokens; Claude Opus 4.7 lists at $5 and $25.

Is Claude Opus 4.7 or DeepSeek-R1 better for coding?

Claude Opus 4.7 scores higher on coding benchmarks: 59.6 versus 46.3 in the Noometry coding category.

Which has the bigger context window?

Claude Opus 4.7 does, with 1M tokens against 164K.

How many benchmarks do Claude Opus 4.7 and DeepSeek-R1 share?

31 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and DeepSeek-R1 has 52.

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