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27/01/2026
Generative artificial intelligence is transforming practices and digital strategies worldwide. At the heart of this revolution, llm statistics provide a quantified overview of the dynamics, performance and adoption challenges facing large language models. This reference article draws on up-to-date, verified data to offer a complete guide for professionals seeking to understand, compare and harness the potential of LLMs as we enter 2026.
Essential LLM statistics 2026: market overview
Large language models have established themselves as pillars of the AI ecosystem. The year 2026 marks a turning point: LLMs are no longer merely conversational or text-generation tools, but engines of automation, analysis and value creation across every sector. Usage is soaring, competition is intensifying, investment is reaching record levels and technical sophistication is advancing at an unprecedented pace.
Key LLM figures for 2026
💡 What these figures reveal
- ✓ Mass adoption of LLMs means rapid integration into digital strategies is essential to avoid falling behind competitors.
- ✓ The majority of the web is now generated or influenced by AI, making LLM optimisation vital for maintaining online visibility.
- ✓ Zero-click search demands a rethink of customer journeys and investment in structured content to secure citations.
- ✓ Exponential growth in queries and users is driving increased demand for infrastructure and energy optimisation.
- ✓ Training and operational costs are now key factors in model selection for businesses.
- ✓ Technical advances (context window, accuracy, cost) are enabling increasingly complex use cases.
- ✓ Ethical expectations, relevance and user satisfaction have become major differentiators.
- ✓ The proliferation of benchmarks and evaluated models enables detailed comparison, but complicates decision-making for leaders.
LLM market overview in 2026
💡 What these figures reveal
- ✓ Competition for visibility via LLMs is intensifying, making an adapted and flexible content strategy essential.
- ✓ Community platforms are becoming key entry points for influencing AI responses.
- ✓ Brands must demonstrate resilience to remain cited in a volatile and ultra-competitive environment.
Market and investment in generative AI
💡 What these figures reveal
- ✓ Record investment in generative AI is accelerating competition and innovation amongst providers.
- ✓ Exponential energy consumption is driving the adoption of optimisation and digital sobriety strategies.
- ✓ Training and inference costs are becoming strategic criteria for model selection in production.
- ✓ Regular monitoring of benchmarks and leaderboards is essential to remain at the cutting edge of AI performance.
- ✓ Open-source models offer new optimisation levers (cost, flexibility, local deployment).
Enterprise adoption and usage
💡 What these figures reveal
- ✓ LLM integration in French SMEs and mid-caps is accelerating technological catch-up and competitiveness in the global market.
- ✓ LLM adoption is spreading across all company sizes, with strong B2B and SME momentum.
- ✓ Younger generations are embracing AI tools en masse and driving innovative uses, accelerating internal digital transformation.
- ✓ Paid solutions are gaining traction, fostering the development of advanced features and bespoke solutions.
Model performance and technical capabilities
💡 What these figures reveal
- ✓ Record context capabilities enable analysis of massive corpora and management of complex IT projects.
- ✓ Significant variation in usage costs drives precise model comparison according to target use case.
- ✓ Specialised benchmarks and Elo scores help target the right model according to business use (code, data, multimodality, etc.).
- ✓ The rise of open source fosters innovation and accessibility of advanced models.
Training resource requirements
💡 What these figures reveal
- ✓ The energy and environmental cost of generative AI demands reflection on the sustainability of uses and model selection.
- ✓ The environmental footprint of LLMs must be integrated into purchasing and deployment strategies.
- ✓ Businesses must incorporate carbon footprint and water consumption into their AI strategy.
- ✓ Resource optimisation (energy, water) is becoming a key competitive factor.
Benchmarks and comparative evaluations
💡 What these figures reveal
- ✓ Varied benchmarks enable fine-grained evaluation of model strengths and weaknesses according to each use case.
- ✓ Benchmark granularity enables precise selection according to specific business needs.
- ✓ Anonymous voting rankings reflect user perception and the practical value of models.
- ✓ The rise of open-source models in rankings fosters diversity of offerings.
Impact on productivity and professions
💡 What these figures reveal
- ✓ LLMs deliver significant time and performance gains in professions with a strong editorial or repetitive component.
- ✓ Automation and personalisation are becoming accessible to businesses of all sizes.
- ✓ Marketing, customer support and IT professions benefit most from productivity gains.
Sector applications and use cases
Content generation and marketing
💡 What these figures reveal
- ✓ Automating content creation enables marketing teams to multiply their presence.
- ✓ Lack of AI integration now constitutes a competitive disadvantage.
- ✓ The majority of marketing professionals have adopted generative AI in their daily work.
Development and programming
💡 What these figures reveal
- ✓ AI copilots and specialised coding LLMs accelerate software delivery whilst reducing code errors.
- ✓ Automating testing and documentation improves software quality and security.
- ✓ Code benchmarks are becoming essential references for choosing your AI model.
- ✓ Open-source and proprietary solutions coexist, offering alternatives depending on context and budget.
Customer service and support
💡 What these figures reveal
- ✓ Support automation improves customer satisfaction and team responsiveness.
- ✓ SMEs gain access to service levels previously reserved for large corporations.
Limitations, bias and ethical issues
Hallucinations and reliability
💡 What these figures reveal
- ✓ Monitoring hallucinations is becoming a differentiating criterion for businesses concerned with reliability and compliance.
- ✓ User education and transparency about model limitations are essential to limit the risk of error or misinformation.
Energy consumption and environmental impact
💡 What these figures reveal
- ✓ Energy sobriety and eco-design of models are becoming essential to limit the environmental footprint of generative AI.
- ✓ Businesses must integrate energy and water impact into their LLM selection criteria.
- ✓ Anticipating future environmental regulations is a lever for competitiveness and brand image.
User perception and expectations
💡 What these figures reveal
- ✓ User satisfaction remains high provided personalisation, security and transparency are guaranteed.
- ✓ Expectations are evolving towards multimodal AI integrated into business tools, which is guiding AI innovation.
- ✓ Security and environmental issues are increasingly influencing selection criteria.
- ✓ Businesses must anticipate integration and security needs to maintain engagement.
ROI and financial performance
💡 What these figures reveal
- ✓ Productivity gains generated by LLMs directly influence the profitability of digital strategies.
- ✓ Training and inference costs must be compared with expected ROI according to usage volume.
- ✓ The generative AI solutions market is segmented by sector and financial performance level.
Issues and future outlook
💡 What these figures reveal
- ✓ GEO, LLMO and AEO optimisation is becoming essential to influence visibility in AI responses.
- ✓ Regular updates, clear structure and community participation are major levers for being cited.
- ✓ Traditional SEO strategies must be adapted to account for zero-click search and AI volatility.
Conclusion and strategic recommendations
Analysis of llm statistics at the start of 2026 highlights explosive growth in adoption, unprecedented technical sophistication and major impacts on productivity, content and business uses. LLMs have established themselves as catalysts for innovation and performance, redefining digital visibility, productivity and value creation. The statistics reveal mass adoption, unprecedented technical capabilities, clear productivity gains and growing impact on the SEO ecosystem. Businesses must navigate issues of automation, personalisation and energy management, whilst meeting challenges of quality, ethics and security. To remain competitive, it is essential to integrate personalisation, ethics, energy sobriety and sector-specific optimisation into your AI strategy.
Priority actions for 2026
- Evaluate and analyse business needs precisely to select the most suitable LLM in terms of performance, cost and ethics.
- Integrate and implement monitoring tools to limit hallucinations, bias and ensure regulatory compliance.
- Optimise energy consumption and environmental footprint by favouring efficient, sustainable models.
- Train teams in advanced uses, ethics and LLM security to maximise adoption and added value.
- Monitor innovations in multimodality and sector integration to remain at the cutting edge of generative AI.
FAQ
LLM adoption and usage
How many ChatGPT users are there in 2026?
ChatGPT has 900 million weekly active users worldwide at the start of 2026, with 2.5 billion queries processed daily. The chatgpt.com site records 5.6 billion monthly visits. In France, 18.3 million unique users use the tool, representing 25–30% of the French population.
What proportion of businesses are using generative AI?
65% of global organisations use generative AI tools in 2026 according to McKinsey. In France, 32% of SMEs and mid-caps use ChatGPT. In July 2025, ChatGPT had 35 million paying users, with a prediction of 220 million paying users by 2030.
What is the demographic profile of LLM users?
42% of ChatGPT users are under 25 years old, showing strong adoption by younger generations. This dynamic is accelerating digital transformation and innovation in businesses.
Who are the main LLM providers in 2026?
The market has 7 major LLM publishers: OpenAI, Anthropic, Google DeepMind, Meta, DeepSeek, xAI and Mistral. In total, 239 LLM models are evaluated on major benchmarks at the start of 2026.
Performance and technical capabilities
What is the maximum context window of LLMs in 2026?
The maximum context window reaches 10 million tokens with Meta Llama 4 Scout. Gemini 2.5 Pro and Gemini 3 Pro offer 1 million tokens, whilst Claude Opus 4.5 provides 200,000 tokens. These capabilities enable analysis of very large documents and management of complex projects.
What are the performance scores of the best models?
The best models achieve scores above 0.85 on GPQA, MMLU, MMMU and AIME 2025 benchmarks. GPT-5 achieves 74.9% on the SWE Verified benchmark for software development. On HumanEval (code), some models reach up to 95% accuracy. Gemini 3 Pro and Grok 4.1 "thinking" dominate the LMArena ranking in January 2026.
How many benchmarks are used to evaluate LLMs?
6 main benchmarks are used (GPQA, MMLU, AIME 2025, LiveCodeBench, MMMU, TAU-bench Retail), but some platforms use up to 15. For code specifically, 6 benchmarks are used: HumanEval, SWE-bench, LiveCodeBench, Aider Polyglot, MATH and MBPP. There are 5 major leaderboard platforms: Vellum, LLM-Stats, LiveBench, SEAL and Chatbot Arena.
Impact on productivity and professions
What are the productivity gains with LLMs?
Productivity gains vary by profession: 30–45% for content creators and marketers, 20–35% for software development, and an acceleration of x2 to x4 for marketing content production. Users save an average of 2 hours per day on repetitive tasks. 90% of marketing professionals use generative AI daily.
What is the impact on customer service?
Over 60% of support tickets are now handled automatically by AI. This automation improves customer satisfaction whilst reducing operational costs and freeing up time for support teams.
How are developers using LLMs?
53% of senior developers believe LLMs code better than humans, and over 70% prefer GPT-5 to o3 for user interface development. The average productivity gain on generated code is between 20 and 35%. Maximum context capacity for coding reaches 10 million tokens with Llama 4 Scout.
What proportion of web content is generated by AI?
Over 50% of web content is now generated by AI at the start of 2026. This proportion makes optimisation for LLMs (LLMO, GEO, AEO) essential to maintain online visibility.
Costs, investment and ROI
How much does it cost to train a large language model?
The Gemini Ultra model training cost amounts to $191 million, whilst the GPT-4 hardware cost reaches $78 million. These massive investments reflect the complexity and resources required to develop cutting-edge models.
What are LLM usage costs?
Costs vary considerably depending on models. DeepSeek V3.1 offers one of the lowest rates at $0.07/million input tokens (cache hit), and DeepSeek-V3.2-Speciale at $0.28/M tokens. At the other end, Claude Opus 4.1 costs $75.00 per million output tokens. Open-source models generally offer the most competitive rates.
What is the valuation of the generative AI market?
OpenAI is valued at $500 billion in 2025, with estimated revenue of $12.7 billion. The market is extremely dynamic with strong growth expected in coming years.
What is the return on investment (ROI) of LLMs?
Estimated ROI for automated content generation is between 30 and 45% productivity gain. For software development, the gain is between 20 and 35%. Time saved on repetitive tasks reaches an average of 2 hours per day. Training and inference costs must be compared with expected ROI according to usage volume.
Environment and ethics
What is the energy consumption of LLMs?
A ChatGPT query consumes 30 times more energy than a Google search (0.34 Wh). AI is expected to represent between 35 and 50% of data centre electricity consumption by 2030, compared with around 5–15% currently. This growth demands urgent reflection on digital sobriety.
What is the water consumption of LLMs?
AI consumes approximately 1,200 billion litres of water annually for cooling in 2026. This massive water consumption for data centre cooling is becoming a major environmental issue, particularly in regions facing water stress.
What are the problems of hallucinations and bias?
17% of users have encountered at least one AI hallucination. The response rejection rate for ethical non-compliance stands at 1.2% for Claude (Anthropic). Monitoring hallucinations and transparency about model limitations are essential to limit risks.
User satisfaction and expectations
What is the level of user satisfaction?
83% of users are satisfied with the relevance of LLM responses, and 79% believe generative AI improves their efficiency. However, 41% of users are concerned about the environmental impact of LLMs.
What are businesses' priority expectations?
62% of businesses expect advanced personalisation, 54% want reduced bias and increased reliability, 68% are interested in multimodal features (text, image, voice), 56% demand integration with business tools (CRM, ERP), and 61% consider data security their main concern for the future.
Visibility and online search
How are LLMs transforming online search?
Nearly 60% of queries are now resolved without external click (zero-click search). Zero-click searches have multiplied by 2.5 since the introduction of AI Overviews. This major transformation is forcing businesses to rethink their digital visibility strategies.
How can you optimise your visibility in AI responses?
70% of pages cited by AI have been updated in the last 12 months (83% for commercial queries). Having a sequential heading hierarchy multiplies the probability of being cited by 2.8. 85% of AI citations come from third-party sources, and 48% come from community platforms. E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness) have a strong correlation with AI visibility.
What is the volatility of visibility in AI responses?
Only 30% of brands remain visible from one AI response to another, indicating high volatility. 60% of AI citations come from URLs outside the organic top 20, disrupting traditional SEO strategies. This volatility demands an adapted and flexible content strategy.
Outlook and future predictions
How will AI traffic evolve compared with Google?
According to Semrush, generative AI traffic should exceed Google's by 2028. This major prediction underlines the profound transformation of search behaviours and online information consumption.
What is the growth projection for paying users?
The number of paying ChatGPT users, which stood at 35 million in July 2025, should reach 220 million by 2030 according to Reuters. This exponential growth demonstrates the perceived value of advanced features.
How will energy consumption evolve?
AI's share of data centre electricity consumption should reach 35–50% by 2030. This massive increase requires digital sobriety and energy optimisation strategies.
Strategic recommendations
What are the 5 priority actions for 2026?
- Evaluate and analyse business needs precisely to select the most suitable LLM in terms of performance, cost and ethics.
- Integrate and implement monitoring tools to limit hallucinations, bias and ensure regulatory compliance.
- Optimise energy consumption and environmental footprint by favouring efficient, sustainable models.
- Train teams in advanced uses, ethics and LLM security to maximise adoption and added value.
- Monitor innovations in multimodality and sector integration to remain at the cutting edge of generative AI.
How do you choose the right LLM for your business?
The choice must take several criteria into account: the context window required, scores on benchmarks relevant to your use, inference cost according to your volume, multimodality if necessary, open-source vs proprietary capabilities, environmental impact, and ethical compliance. The 239 evaluated models offer a wide choice, and the 5 leaderboard platforms (Vellum, LLM-Stats, LiveBench, SEAL, Chatbot Arena) facilitate comparison.
Why has optimisation for LLMs become essential?
With over 50% of web content generated by AI, nearly 60% of queries without external click, and the prediction that AI traffic will exceed Google in 2028, not optimising for LLMs means becoming invisible. GEO, LLMO and AEO optimisation is now as important as traditional SEO.
Sources:
https://llm-stats.com/
https://botpress.com/fr/blog/best-large-language-models
https://vlad-cerisier.fr/statistiques-intelligence-artificielle-ia/
https://www.agence-coherence.fr/llms-expliques-les-10-modeles-dia-incontournables-en-2026/
https://www.blogdumoderateur.com/top-20-modeles-ia-performants-janvier-2026/
https://zencoder.ai/fr/blog/best-llm-for-coding
https://www.siliconflow.com/articles/fr/best-open-source-LLM-for-data-analysis
https://palmer-consulting.com/leaderboards-des-benchmarks-llm/
https://www.natural-net.fr/blog-agence-web/2025/12/05/seo-et-recherche-ia-sur-les-llm-en-2026-les-tendances-et-ruptures-de-l-annee-a-venir-.html
Concrete example
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