LLMs & Assistants
Claude vs Qwen.
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 84.1, a margin of 6.4 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; Qwen leads on value for money.
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
Alibaba Cloud
84.1
Excellent
Alibaba's open-weight LLM family, strong on code, math, and agentic tasks
- From
- Free (open-weight, self-hosted) / API from ~$0.05 per 1M input tokens
- Pricing
- Freemium
- Maturity
- Established
- 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 8
Value for Money
Qwen by 5
Security & Compliance
Claude by 22
Integrations & Ecosystem
Level
Maturity & Reliability
Claude by 10
Momentum
Level
Which one, and when
Pick Claude if
- →compliance and data control decide it — it leads Security & Compliance by 22 points.
- →it has to hold up in production from day one — it leads Maturity & Reliability by 10 points.
- →the hardest end of the work is what you are buying for — it leads Capability by 8 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
Pick Qwen if
- →cost per unit of output is the binding constraint — it leads Value for Money by 5 points.
- →Developers wanting low-cost, self-hostable open-weight LLMs for coding and agentic workflows
- →Enterprises needing multilingual, multimodal models via a single unified API
- →Cost-sensitive production deployments seeking frontier-adjacent performance at a fraction of Western model pricing
The catch
- Newer flagship tiers (Qwen3.6/3.7-Max) have shifted to closed weights, limiting self-hosting for the most capable models
- Some large models use the more restrictive Tongyi Qianwen License rather than Apache 2.0, with commercial caps tied to MAU thresholds
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
Qwen
- ✓Aggressive price-to-performance with open-weight Apache 2.0 models that can be self-hosted at zero per-token cost
- ✓Strong, frequently-updated coding and agentic performance, including high SWE-bench Verified scores
- ✓Broad model catalogue spanning text, vision, audio, coding and embeddings under one API
Other comparisons in LLMs & Assistants
Comparing something else? Build your own side-by-side across any tools in the catalog.