LLMs & Assistants
DeepSeek vs Kimi.
Both are LLMs & Assistants tools, scored on the same six pillars by the same published method. Here's where they part company.
The short answer
Too close to call on score alone — DeepSeek sits at 83.6 and Kimi at 84.0. A gap that size is inside the noise of any honest scoring model, so pick on fit rather than rank.
Kimi leads on maturity & reliability.
DeepSeek AI
83.6
Excellent
Open-weight frontier models at a fraction of the usual cost.
- From
- Free (open weights) / API from $0.15 per M input tokens off-peak (V4.1-Flash)
- Pricing
- Open Source
- Maturity
- Emerging
- Founded
- 2023
Moonshot AI
84.0
Excellent
Open-weight frontier assistant with standout agentic coding at rock-bottom cost.
- From
- Free chat / from $0.60 per 1M tokens (API)
- Pricing
- Open Source
- 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
Level
Security & Compliance
Level
Integrations & Ecosystem
Level
Maturity & Reliability
Kimi by 6
Momentum
Level
Which one, and when
Pick DeepSeek if
- →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
Pick Kimi if
- →it has to hold up in production from day one — it leads Maturity & Reliability by 6 points.
- →Cost-sensitive frontier LLM workloads
- →Self-hosted agentic coding
- →Teams fine-tuning open models
The catch
- China-based hosting raises data-residency and compliance questions for regulated buyers
- Trails the very top on some pure-reasoning benchmarks (GPQA-Diamond, AIME) versus GPT-5.4
What each is good at
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
Kimi
- ✓Frontier-class agentic coding — K2.7-Code leads Opus 4.8 on MCP-Mark Verified
- ✓Trillion-parameter MoE with open weights for self-hosting and fine-tuning
- ✓256K context and API pricing that undercuts most closed competitors many times over
Other comparisons in LLMs & Assistants
Comparing something else? Build your own side-by-side across any tools in the catalog.