LLMs & Assistants

Claude vs DeepSeek.

Both sit in LLMs & Assistants, scored on the same six pillars from the same published methodology. Here is where they actually differ.

The short answer

Claude scores higher — 90.5 against 83.6, a margin of 6.9 points. That is the overall answer, not the whole one: the pillar breakdown below is where the decision usually actually gets made.

Claude leads on security & compliance, maturity & reliability and capability; DeepSeek leads on value for money.

Claude

Anthropic

90.5

Exceptional

Frontier assistant known for reasoning depth, long context and reliability.

From
Free / $20 per month (Pro)
Pricing
Freemium
Maturity
Mature
Founded
2023
DeepSeek

DeepSeek AI

83.6

Excellent

Open-weight frontier models at a fraction of the usual cost.

From
Free (open weights) / low-cost API
Pricing
Open Source
Maturity
Emerging
Founded
2023

Pillar by pillar

The same six pillars and fixed weights used for every tool on the site. A lead of fewer than 5 points is not called for either side — these are evidence-backed judgements, not measurements. Read the methodology.

Capability

Claude by 7

Claude95
DeepSeek88

Value for Money

DeepSeek by 8

Claude87
DeepSeek95

Security & Compliance

Claude by 18

Claude90
DeepSeek72

Integrations & Ecosystem

Claude by 6

Claude84
DeepSeek78

Maturity & Reliability

Claude by 16

Claude88
DeepSeek72

Momentum

Claude by 6

Claude94
DeepSeek88

Which one, and when

Pick Claude if

  • compliance and data control decide it — it leads Security & Compliance by 18 points.
  • it has to hold up in production from day one — it leads Maturity & Reliability by 16 points.
  • the hardest end of the work is what you are buying for — it leads Capability by 7 points.
  • Software engineering
  • Long-document analysis
  • Enterprises with strict data policies

The catch

  • No native image generation
  • Consumer tier usage caps can be restrictive for heavy users
Full Claude profile →

Pick DeepSeek if

  • cost per unit of output is the binding constraint — it leads Value for Money by 8 points.
  • Cost-sensitive AI workloads
  • Self-hosted frontier reasoning
  • Teams fine-tuning open models

The catch

  • Hosted service raises data-residency questions for regulated buyers
  • Enterprise support and certifications lag Western labs
Full DeepSeek profile →

What each is good at

Claude

  • Consistently top-tier on coding and complex reasoning benchmarks
  • 1M-token context windows (Opus 5) handle entire codebases and long documents
  • Model Context Protocol (MCP) created an open integration standard

DeepSeek

  • Frontier-class reasoning and coding at a small fraction of the cost
  • Open weights allow self-hosting, fine-tuning and full data control
  • API pricing undercuts most closed competitors substantially

Other comparisons in LLMs & Assistants

Comparing something else? Build your own side-by-side across any tools in the catalog.