LLMs & Assistants
Grok 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
Too close to call on score alone — Grok sits at 82.7 and Qwen at 84.1. A gap that size is inside the noise of any honest scoring model, so pick on fit rather than rank.
Grok leads on security & compliance; Qwen leads on integrations & ecosystem, value for money and maturity & reliability.
xAI
82.7
Excellent
Real-time assistant wired directly into X, with a candid personality.
- From
- Free / $30 per month (SuperGrok)
- Pricing
- Freemium
- Maturity
- Emerging
- 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
Level
Value for Money
Qwen by 7
Security & Compliance
Grok by 6
Integrations & Ecosystem
Qwen by 11
Maturity & Reliability
Qwen by 6
Momentum
Level
Which one, and when
Pick Grok if
- →compliance and data control decide it — it leads Security & Compliance by 6 points.
- →Real-time social research
- →Teams already living on X
- →Cost-efficient agentic coding
The catch
- Enterprise security and compliance story is younger than rivals
- Personality tuning has drawn scrutiny over output controls
Pick Qwen if
- →it has to fit the stack you already run — it leads Integrations & Ecosystem by 11 points.
- →cost per unit of output is the binding constraint — it leads Value for Money by 7 points.
- →it has to hold up in production from day one — it leads Maturity & Reliability by 6 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
Grok
- ✓Live access to X gives it a real-time edge on news and sentiment
- ✓Fast-improving reasoning and coding across recent model releases
- ✓Generous context and image understanding on paid tiers
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.