DeepSeek Statistics 2025: 95+ Stats & Insights [Expert Analysis]

A. User Growth & Adoption

1. DeepSeek surpassed 90 million monthly active users in early 2025.

2. Daily active users crossed 20 million within the first month.

3. Weekly web visitors exceeded 15 million globally.

4. Total mobile app downloads passed 75 million after launch.

5. Peak launch-week daily downloads exceeded 3 million.

6. Over half of the user base comes from China, India, and Indonesia.

7. Nearly half of all users come from outside China.

8. Monthly user retention is above 40%.

9. The average user opens DeepSeek around 8–9 times per month.

10. Average session duration is around 6 minutes.

11. Mobile usage accounts for nearly 80% of total activity.

12. Android devices represent the majority of mobile installs.

13. iOS accounts for nearly one-third of installs.

14. Desktop usage represents under 10% of all traffic.

15. Developers make up around 2% of registered accounts.

16. Active developers send tens of thousands of monthly API requests.

17. India is among the fastest-growing user markets.

18. Southeast Asia represents one of the quickest adoption curves.

19. User growth exceeded 25% month-over-month at peak acceleration.

20. Around 6% of users adopt paid tiers in early monetization stages.

21. Chinese-language prompts represent the majority of usage.

22. English-language usage accounts for over one-quarter of prompts.

23. More than 60 languages are represented in user queries.

24. Developer registrations exceed 200,000 globally.

25. Enterprise customers account for several percent of the paying user base.

B. Model, Product & Technology Stats 

26. The flagship chatbot launched publicly on January 20, 2025.

27. DeepSeek’s core families include V3, R1, and Coder models.

28. The coding models support context windows up to 16,000 tokens.

29. V3 and Coder releases use a permissive source-available license.

30. DeepSeek’s GitHub repositories have accumulated over 70,000 stars.

31. The Coder family includes eight variants across Base and Instruct tiers.

32. Pretraining for the Coder series used more than a trillion tokens.

33. Long-context pretraining involved hundreds of billions of tokens.

34. Instruction tuning datasets contain billions of tokens.

35. V3’s benchmark results outperform multiple competing open models.

36. DeepSeek provides multiple model sizes for varied hardware.

37. Main use cases include chat, coding, writing, analysis, and search.

38. The platform supports SDKs across major programming languages.

39. Typical inference latency is under one second for standard prompts.

40. Token efficiency is optimized for lower inference cost than many competitors.

41. Sparse attention and mixed precision are core optimization techniques.

42. DeepSeek introduced a custom Sparse Attention mechanism in V3.2-Exp.

43. The licensing approach allows broad usage with minimal restrictions.

44. Model family performance compares favorably with Llama 3.1 and Qwen 2.5.

45. DeepSeek Coder models perform competitively with top-tier coding LLMs.

46. The company ships several major model updates each year.

47. The update cadence typically follows a quarterly release cycle.

48. Lightweight variants support edge and mobile inference.

49. More than 60 languages are supported natively by its models.

50. DeepSeek operates across web, iOS, Android, and developer API.

C. Financial, Cost, & Business Metrics 

51. Daily inference operating costs have been reported at under $100,000.

52. Theoretical daily revenue projections exceed half a million dollars.

53. The cost-profit ratio has been presented as over 500% in daily terms.

54. Model training costs were publicly highlighted as being under $6 million.

55. Analysts have projected that real training costs exceed the public figure.

56. Annual revenue potential at scale is projected in the hundreds of millions.

57. Analysts place the company’s implied valuation in the multi-billion range.

58. Revenue distribution is split between consumer and API monetization.

59. Gross margin is strengthened by aggressive model efficiency.

60. The platform’s token costs are positioned below many leading competitors.

61. Pricing includes free tiers, usage-based billing, and enterprise plans.

62. A significant portion of staff works in research and development.

63. Paid user lifetime value reaches into the triple-digit dollar range.

64. Customer acquisition payback occurs within several months.

65. GPU compute accounts for the majority of operational expenses.

66. DeepSeek operates with minimal traditional venture capital involvement.

67. The company maintains dozens of enterprise pilot programs.

68. Annualized inference costs reach tens of millions of dollars at scale.

69. Total operating expenses run in the high single-digit millions monthly.

70. A meaningful share of paid users are enterprise-level clients.

D. Security, Reliability & Compliance

71. DeepSeek experienced a major cyberattack shortly after launch.

72. User registrations were temporarily restricted during the incident.

73. The company restored full service within days.

74. Security programs include active monitoring and vulnerability reporting.

75. Enterprise offerings include a 99.9% uptime commitment.

76. Public vulnerability disclosures remain low.

77. Policy filters review a small fraction of overall user prompts.

78. The safety system uses layered model and rules-based moderation.

79. Analysts observed that outputs follow regionally aligned safety norms.

80. Human review is required for only a tiny percentage of user content.

E. Community, Ecosystem & Developer Activity 

81. DeepSeek’s open-source projects exceed tens of thousands of stars combined.

82. Repository forks number in the thousands.

83. Community developers maintain over a hundred integrations.

84. Model downloads across public hubs number in the tens of thousands.

85. Dozens of benchmark datasets now include DeepSeek submissions.

86. Research papers citing DeepSeek models continue to grow rapidly.

87. Developer community groups exceed a hundred thousand members.

88. Third-party API wrappers are available across major languages.

89. Hundreds of tutorial notebooks and guides reference DeepSeek.

90. Dozens of independent benchmark comparisons were released in 2025.

91. Community contributions account for a significant share of updates.

92. SDK support includes Python, JavaScript, Java, Go, and more.

93. The chat interface supports a growing library of community plug-ins.

94. Multiple universities collaborate with DeepSeek on AI research.

95. The company has hosted over a hundred public workshops and demos.

F. Performance, Benchmarks & Industry Impact

96. DeepSeek V3 outperforms several major open models in benchmark testing.

97. Performance comparisons place it near GPT-4-tier models in some tasks.

98. DeepSeek Coder achieves strong results on competitive coding benchmarks.

99. Inference efficiency delivers multiple-fold cost reductions over peers.

100. The model family significantly influenced public discussion on AI cost, openness, and global competition.