Model comparison

Claude Opus 4.5 vs DeepSeek-R1

Claude Opus 4.5 is the stronger model overall, scoring 50.5 to 42.3 on the Noometry Index. DeepSeek-R1 costs 11× less per token, which makes it the better buy when Claude Opus 4.5's lead doesn't matter for your workload.

Last verified October 10, 2026. 33 shared benchmarks.

Claude Opus 4.5 Anthropic

50.5

Rank #47 Confirmed

DeepSeek-R1

DeepSeek

42.3

Rank #115 Confirmed

Summary

They share 33 benchmarks with published results for both. Claude Opus 4.5 scores higher in 8 categories and DeepSeek-R1 in 1 category; 9 gaps are clear of the uncertainty.
The widest gap is in reasoning, where Claude Opus 4.5 leads 42.6 to 18.6.
The biggest single-benchmark swing is ARC-AGI-1: 80% for Claude Opus 4.5 and 21.2% for DeepSeek-R1.
DeepSeek-R1 is cheaper at $0.50 / $2.15 per million input/output tokens, against $5 / $25 for Claude Opus 4.5.
Claude Opus 4.5 accepts more context: 200K tokens versus 164K.

Side by side

Claude Opus 4.5 and DeepSeek-R1 specifications
	Claude Opus 4.5	DeepSeek-R1
Provider	Anthropic	DeepSeek
Noometry Index	50.5	42.3
Released	2025-11-01	2025-01-20
Weights	Proprietary	Proprietary
Context window	200K	164K
Max output	64K	64K
Input $ / M tokens	$5	$0.50
Output $ / M tokens	$25	$2.15
Results tracked	69	52

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Category by category

Coding Claude Opus 4.5 leads

Claude Opus 4.5: 54.8 (#27), DeepSeek-R1: 46.3 (#68)

Coding benchmarks
Benchmark	Claude Opus 4.5	DeepSeek-R1
WeirdML	63.7%	41.6%
LMArena Coding	1504	1427
ALE-Bench	1,025	804.12
AlgoTune	1.77	1.7
SWE-bench Verified	76.7%	—
SWE-bench Verified (bash only)	76.8%	—
Aider Polyglot	—	71.4%
LMArena WebDev	1494	—
SWE-bench Multilingual	70.7%	—
SciCode	—	35.7%
GSO	26.5%	—
LiveBench Coding	—	66.7%

Agentic & Tool Use Claude Opus 4.5 leads

Claude Opus 4.5: 47.3 (#12), DeepSeek-R1: 30.7 (#75)

Agentic & Tool Use benchmarks
Benchmark	Claude Opus 4.5	DeepSeek-R1
DeepResearch Bench	54.8%	35.1%
BALROG	43.5%	34.9%
METR Time Horizons	75%	53.8%
Terminal-Bench	63.1%	—
Berkeley Function Calling Leaderboard	77.5%	—
GDPval	45.5%	—
Remote Labor Index	3.8%	—
τ²-bench Airline	84%	—
τ²-bench Banking	24.7%	—
τ²-bench Retail	79.6%	—
τ²-bench Telecom	92.3%	—
Cybench	82%	—
OSWorld	66.3%	—
LMArena Search	1180	—
Vending-Bench 2	4,967	—

Reasoning Claude Opus 4.5 leads

Claude Opus 4.5: 42.6 (#51), DeepSeek-R1: 18.6 (#278)

Reasoning benchmarks
Benchmark	Claude Opus 4.5	DeepSeek-R1
ARC-AGI-2	37.6%	1.3%
SimpleBench	62%	40.8%
Kagi LLM Benchmark	80.2%	69.4%
ARC-AGI-1	80%	21.2%
LMArena Hard Prompts	1476	1416
Epoch Capabilities Index	150.09	141.29
ForecastBench	60.7	60
NYT Connections (extended)	52.5%	—
CritPt	—	1.1%
Chess Puzzles	12%	—
EnigmaEval	11.9%	—
EBR-Bench	14.3%	—
LiveBench Reasoning	—	83.2%
Mystery Game Puzzles	22%	—
DTBench	89.9%	—
LiveBench Data Analysis	—	69.8%
LMCA	44.5%	—
LiveBench	—	71.6%

Math DeepSeek-R1 leads

Claude Opus 4.5: 38.6 (#132), DeepSeek-R1: 43.8 (#79)

Math benchmarks
Benchmark	Claude Opus 4.5	DeepSeek-R1
OTIS Mock AIME 2024-2025	86.1%	66.4%
LMArena Math	1463	1400
FrontierMath (Tiers 1-3)	34.4%	—
FrontierMath Tier 4	4.9%	—
ProofBench	36%	—
Omni-MATH	—	42.4%
LiveBench Math	—	80.7%
MATH Level 5	—	96.6%
FrontierMath (Feb 2025 set)	20.7%	—
FrontierMath Tier 4 (v1)	4.2%	—

Knowledge Claude Opus 4.5 leads

Claude Opus 4.5: 56.5 (#44), DeepSeek-R1: 44.5 (#87)

Knowledge benchmarks
Benchmark	Claude Opus 4.5	DeepSeek-R1
GPQA Diamond	86%	76.3%
Vectara Hallucination Rate	10.9%	11.3%
LMArena Expert	1487	1394
Humanity's Last Exam	25.2%	—
SimpleQA Verified	45.7%	—
MMLU-Pro	—	79.3%
Confabulations	—	12.7%
GPQA (HELM)	—	66.6%

Multimodal Not comparable

Claude Opus 4.5: 31.4 (#107), DeepSeek-R1: —

Multimodal benchmarks
Benchmark	Claude Opus 4.5	DeepSeek-R1
GeoBench	75%	—
VPCT	40%	—
Furniture Assembly	28.3%	—
LMArena Document	1462	—

Multilingual Claude Opus 4.5 leads

Claude Opus 4.5: 54.3 (#47), DeepSeek-R1: 52.4 (#85)

Multilingual benchmarks
Benchmark	Claude Opus 4.5	DeepSeek-R1
LMArena Non-English	1438	1412
LMArena Chinese	1470	1442
LMArena French	1471	1417
LMArena German	1449	1404
LMArena Japanese	1416	1391
LMArena Korean	1424	1360
LMArena Russian	1447	1423
LMArena Spanish	1458	1411

Instruction Following Claude Opus 4.5 leads

Claude Opus 4.5: 77.5 (#19), DeepSeek-R1: 72.0 (#143)

Instruction Following benchmarks
Benchmark	Claude Opus 4.5	DeepSeek-R1
LMArena Instruction Following	1478	1382
LiveBench Instruction Following	—	80.5%
IFEval	—	78.4%

Long Context Claude Opus 4.5 leads

Claude Opus 4.5: 46.5 (#22), DeepSeek-R1: 45.4 (#36)

Long Context benchmarks
Benchmark	Claude Opus 4.5	DeepSeek-R1
LMArena Longer Query	1480	1391
Fiction.LiveBench	—	75%
CL-bench	21.1%	—

Writing & Preference Claude Opus 4.5 leads

Claude Opus 4.5: 68.1 (#28), DeepSeek-R1: 61.4 (#88)

Writing & Preference benchmarks
Benchmark	Claude Opus 4.5	DeepSeek-R1
LMArena Text	1451	1428
LMArena Creative Writing	1445	1405
EQ-Bench Creative Writing	1687	1500
LMArena Multi-Turn	1466	1405
Short-Story Creative Writing	—	83%
WildBench	—	82.8%
LiveBench Language	—	48.5%

Frequently asked questions

Is Claude Opus 4.5 better than DeepSeek-R1?

Which is cheaper, Claude Opus 4.5 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 4.5 lists at $5 and $25.

Is Claude Opus 4.5 or DeepSeek-R1 better for coding?

Claude Opus 4.5 scores higher on coding benchmarks: 54.8 versus 46.3 in the Noometry coding category.

Which has the bigger context window?

Claude Opus 4.5 does, with 200K tokens against 164K.

How many benchmarks do Claude Opus 4.5 and DeepSeek-R1 share?

33 benchmarks have published results for both models. Claude Opus 4.5 has 69 scored results on Noometry and DeepSeek-R1 has 52.