See the future of large language models by delving into promising approaches, such as self-training, fact-checking, and sparse expertise that could address LLM limitations.
Success rate comparison of LLM’s
Claude 4.5 Sonnet and GPT-5.2 had the highest overall scores with the most consistent results across both API logic and UI integration. Gemini 3.1 Pro Preview and GPT-5.2 Codex followed, with functional backend logic but weaker frontend output. See more in our benchmark article.
Future trends of large language models
1- Real-Time Fact-Checking With Live Data
LLMs access external sources during conversations instead of relying only on training data. The model queries external databases, retrieves current information, and provides citations.
Limitation: Still makes errors. Citations don’t guarantee accuracy; models sometimes cite sources incorrectly or misinterpret cited content.
Microsoft Copilot: Integrates GPT-5.4 Thinking with live internet data, introducing “Quick Response” and “Think Deeper” modes for tailored reasoning across different task types.1 The Researcher agent combines GPT for initial research with Anthropic’s Claude reviewing outputs for accuracy and citation quality before delivery a 13.8% improvement on the DRACO deep-research benchmark over standalone systems.2
- ChatGPT: Searches the web when asked about recent events. Cites sources in responses.
- Perplexity: Built specifically for cited search. Every answer includes source links.
2- Synthetic training data
Models generate their own training datasets instead of requiring human-labeled data.
Google’s self-improving model (2023 research):
- The model creates questions
- Curates answers
- Fine-tunes itself on generated data
Performance improved: 74.2% to 82.1% on GSM8K math problems, 78.2% to 83.0% on DROP reading comprehension.
OpenAI, Anthropic, and Google are all using synthetic data to supplement human-labeled datasets. This reduces data labeling costs but introduces new bias risks; models can amplify their own mistakes.
Source: “Large Language Models Can Self-Improve”
A March 2026 survey found that 76% of AI researchers believe the gains from scaling compute and data have plateaued, with major labs reporting diminishing returns despite massive investments. The finding suggests the next leap in LLM capability is more likely to come from architectural innovation, such as improved training efficiency, sparse architectures, or reasoning improvements, than from simply scaling existing approaches further.3
3- Sparse Expert Models (Mixture of Experts)
Instead of activating the entire neural network for every input, only a relevant subset of parameters activates, depending on the task. The model routes input to specialized “experts” within the network. Only activated experts process the query.
Real-life examples:
- Llama 4 Scout: 109B total parameters, 17B active per token. The Mixture of Experts (MoE) architecture delivers a 10M-token context window on a single H100 GPU.
- Mistral Devstral 2: Purpose-built for software engineering tasks. 123B parameters, 256K token context window. Achieves 72.2% on SWE-bench Verified, establishing it as the leading open-weight coding model. A smaller variant, Devstral Small 2 (24B parameters), runs locally on consumer hardware under the Apache 2.0 license.4
- DeepSeek V4 (Preview): DeepSeek’s fourth-generation foundation model uses a 1-trillion-parameter MoE architecture approximately 50% larger than V3’s 671 billion parameters with multimodal capabilities covering text, image, and video alongside native agentic support. The V4 architecture retains the efficiency characteristics of the V3 series, activating only a fraction of parameters per token while adding Thinking in Tool-Use, which enables the model to reason within agentic workflows while calling external tools.5
4- Enterprise Workflow Integration
LLMs are embedded directly into business processes rather than used as standalone tools.
Real-life examples:
- Salesforce Agentforce (formerly Einstein Copilot): Integrates LLMs into CRM operations. Answers customer queries, generates content, and executes actions in Salesforce, grounded in the organization’s CRM data and metadata via the Einstein Trust Layer.6
- Microsoft 365 Copilot: Embedded across Word, Excel, PowerPoint, and Outlook. Drafts documents, analyzes spreadsheets, generates presentations, and summarizes email threads, drawing on company data through Microsoft Graph to ground responses in organizational context.7 The Researcher agent uses a multi-model architecture where GPT handles initial research and Claude reviews outputs before delivery the first confirmed commercial deployment of competing AI vendors inside a single enterprise product.
- Anthropic Claude for Enterprise: Project-based memory separation keeps work contexts distinct across teams. Claude Opus 4.6 introduced agent teams, allowing multiple Claude agents to split larger tasks into parallel workstreams, each owning a segment and coordinating with others simultaneously. The same release integrated Claude directly into PowerPoint as a native side panel (research preview), allowing presentations to be built and edited within the application without file transfers.8
5- Hybrid LLMs with multimodal capabilities
Large multimodal models integrate multiple forms of data, such as text, images, and audio, enabling them to understand and generate content across different media types.
- GPT-5.5: Processes text and images natively. Excels at agentic coding, computer use, and long-horizon task completion, with API pricing at $5 per million input tokens and $30 per million output tokens. Audio and video are not supported at the API level as a direct generation medium.9
- Gemini 2.5 Pro: Natively handles text, audio, images, video, and entire code repositories within a 1M token context window. Available across Google AI Studio, Vertex AI, and NotebookLM. Pricing starts at $1.25 per million input tokens and $10 per million output tokens via the API.10
- Llama 4 Scout and Maverick: Meta’s open-weight models use early-fusion multimodal text and vision tokens, trained together from the start rather than added as separate modules. The models were pretrained across 200 languages and provided specific fine-tuning support for 12 languages, including Arabic, Spanish, German, and Hindi.11
Multimodal capability is standard across frontier models. The remaining challenge is consistency: models perform well on common image-text combinations but degrade on rare visual contexts, low-resolution inputs, and cross-modal reasoning that requires connecting visual and textual evidence.
6- Reasoning models
Models that think through problems step by step rather than generating immediate responses.
This shift from prediction to reasoning is critical for enabling:
- Agentic behavior, where models plan, execute, and adapt tasks autonomously.
- Interpretable AI, where outputs are step-by-step and logically sound, not just plausible-sounding.
- Claude Opus 4.7: Anthropic’s most capable generally available model, delivering a step-change improvement in agentic coding over its predecessor. Uses adaptive thinking the model dynamically decides when and how much to think based on task complexity, without requiring manual mode switching. On XBOW’s visual-acuity benchmark, Opus 4.7 scores 98.5% versus 54.5% for the prior generation, effectively resolving one of the main limitations of earlier Opus models for computer-use tasks. Pricing starts at $5 per million input tokens and $25 per million output tokens.12
- Claude Sonnet 4.6: Brings adaptive thinking to a lower price point ($3/$15 per million tokens). Approaches Opus-level performance on coding and computer use benchmarks (79.6% vs 80.8% on SWE-bench Verified; 72.5% vs 72.7% on OSWorld-Verified), making extended reasoning practical at scale for enterprise deployments. A larger gap remains on novel reasoning tasks such as ARC-AGI-2.13
7- Domain-Specific Fine-Tuned Models
Models trained on specialized data for specific industries instead of general-purpose training.
Google, Microsoft, and Meta have all released major proprietary domain-specific and fine-tuned models targeting enterprise-specific use cases in addition to their general-purpose offerings.
These specialized LLMs can result in fewer hallucinations and higher accuracy by leveraging domain-specific pre-training, model alignment, and supervised fine-tuning.
Coding
GitHub Copilot: Fine-tuned on code repositories. As of July 2025, 20 million developers use GitHub Copilot, a 400% year-over-year increase, and 90% of Fortune 100 companies use it. It autocompletes code, generates functions, and suggests bug fixes.14
Finance
BloombergGPT: 50-billion-parameter LLM trained on a 363-billion-token dataset of Bloomberg financial documents, outperforming models of comparable size on financial NLP benchmarks, including sentiment analysis, named entity recognition, and question answering.15
Healthcare
Google’s Med-PaLM 2: Fine-tuned on medical datasets, reached 85%+ accuracy on U.S. Medical Licensing Examination (USMLE)-style questions, the first LLM to reach expert-level performance on this benchmark. It powers MedLM, Google Cloud’s family of healthcare foundation models.16
Law
ChatLAW: An open-source language model specifically trained on Chinese legal domain datasets.17
8- Ethical AI and bias mitigation
Companies are increasingly focusing on ethical AI and bias mitigation in the development and deployment of large language models.
- Anthropic and OpenAI conducted a mutual alignment evaluation in mid-2025, testing each other’s public models for sycophancy, whistleblowing tendencies, and self-preservation behaviors. The exercise found sycophancy in all models tested, including cases where models validated harmful decisions from simulated users exhibiting delusional beliefs. Anthropic subsequently developed the Bloom testing framework specifically to benchmark this behavior in new models.
- Anthropic also released Claude Mythos Preview (Project Glasswing) an invitation-only model made available to a small set of organizations specifically to find and fix cybersecurity vulnerabilities in major operating systems and web browsers. Anthropic has stated it does not plan to make this model generally available. The controlled-access approach represents a new framework for deploying highly capable specialist models where the risk profile requires restricted rollout.18
- Google DeepMind: Published “The Ethics of Advanced AI Assistants,” offering the first systematic treatment of ethical and societal questions raised by AI agents, covering value alignment, manipulation risks, anthropomorphism, privacy, and equity. The company’s Responsible AI evaluation included over 350 adversarial red-team exercises and introduced a new Critical Capability Level specifically for harmful manipulation, treating it as a frontier-level risk alongside cyberattacks and CBRN threats.
Limitations of large language models (LLMs)
1- Hallucinations
Models generate plausible-sounding but incorrect information.
The Vectara hallucination leaderboard is the industry’s most widely referenced grounded summarization benchmark. On the original Vectara dataset, Google’s Gemini models consistently occupy the top positions, with Gemini Flash variants achieving under 1% hallucination rates. OpenAI’s GPT family clusters between 0.8% and 2.0%.
Figure: Hallucination benchmark for popular LLMs
Source: Vectara Hallucination Leaderboard19
Vectara launched a significantly harder benchmark in late 2025 7,700 articles (up from 1,000), longer documents up to 32K tokens, and content spanning law, medicine, finance, and technology. The findings on the new dataset reveal a counterintuitive pattern: reasoning and thinking models that excel at complex tasks frequently hallucinate more on grounded summarization than smaller, faster models. Most thinking-class models show hallucination rates above 10% on the harder dataset, while lighter models like Gemini Flash variants maintain lower rates.20
Note: No single benchmark gives a definitive “hallucination rate” for any model. A responsible evaluation cross-references at least two benchmarks measuring different things: one grounded task (Vectara), one open-ended knowledge task, and specifies the exact model version and calling conditions.
All models hallucinate. Frequency has reduced substantially from approximately 21% in 2021 to under 5% for the best performers on standard benchmarks, but is not eliminated. Critical applications still require human verification.
2- Bias
Models absorb and amplify social biases from training data.
Figure: Overall bias scores by models and size
Source: Arxiv21
Types of bias observed:
- Gender bias in occupation suggestions
- Racial bias in resume screening simulations
- Age bias in healthcare recommendations
- Socioeconomic bias in educational content
3- Toxicity
Models may generate harmful, offensive, or toxic content despite safety measures.
Figure: LLMs’ toxicity map
Source: UCLA, UC Berkeley Researchers22
*GPT-4-turbo-2024-04-09*, Llama-3-70b*, and Gemini-1.5-pro* are used as the moderator, thus the results could be biased on these 3 models.
Strict safety measures reduce toxicity but increase false positives (refusing harmless requests). Loose measures allow toxicity through.
4- Context Window Limitations
Every model has a fixed memory capacity the number of tokens it can process in a single session. Exceed that limit, and the model either truncates earlier content or refuses the request. The practical gap between models is wide enough to matter for real workloads.
Most recent context windows:
- Llama 4 Scout (Meta): 10M tokens (~7.5M words) the largest production-verified context window among leading models.23 In practice, this means loading entire codebases, legal archives, or multi-day conversation histories without chunking.
- Gemini 2.5 Pro: 1,048,576 tokens (~780,000 words), with native multimodal input across text, audio, images, and video within the same window. Recall holds at 100% up to 530,000 tokens and 99.7% at the full 1 million token limit
- Claude Sonnet 4.6: 1M tokens (~750,000 words) at standard pricing, available without beta headers or special configuration.24
- GPT-5.5: 1M token context window at API level.25
A large context window does not automatically mean better performance across it. Recall degrades toward the middle of very long contexts on most models, and costs scale with input length processing 1M tokens costs significantly more than processing 10K tokens on the same model. For most production workloads, the practical question is not which model has the largest window, but which model retrieves reliably at the context lengths your use case actually requires.
5- Static Knowledge Cutoff
Models rely on pre-trained knowledge with a specific cutoff date. Don’t have access to information after training unless connected to external sources.
Problems:
- Outdated information on current events
- Inability to handle recent developments
- Less relevance in dynamic domains (technology, finance, medicine)
Solution: Web search integration. ChatGPT, Claude, and Perplexity all offer real-time search. But search doesn’t eliminate hallucinations; models sometimes misinterpret search results.
Major LLM Platforms
GPT-5.5
OpenAI’s current flagship was released on April 23, 2026. Built around configurable reasoning effort, developers set the depth of thinking per request (none through xhigh), so simple queries don’t burn compute reserved for hard problems. The model excels at agentic coding, computer use, and long-horizon tasks where it needs to hold context across large systems and check its own work mid-execution.26
Who uses it: Developers, enterprises, and content creators. Largest user base among LLMs.
Limitations: $5/$30 per million tokens the highest base price in this list. Still hallucinates. Requires web search integration for anything after its training cutoff.
Claude Opus 4.7 / Sonnet 4.6
Anthropic’s two production models as of April 2026. Opus 4.7 is the flagship stronger on complex multi-step reasoning, vision (98.5% on XBOW’s visual-acuity benchmark), and long-horizon coding. Sonnet 4.6 delivers near-Opus performance on coding (79.6% SWE-bench vs Opus’s 80.8%) and computer use (72.5% vs 72.7% on OSWorld) at $3/$15 per million tokens, one-fifth the cost of Opus.27 28
Both models use adaptive thinking the model determines reasoning depth based on task complexity without requiring manual mode switching. Memory is opt-in and explicit: Claude starts each session fresh and activates memory only through tool calls, so users always know when prior context is being retrieved. Agent teams let multiple Claude instances split a task into parallel workstreams, each coordinating directly.29
Who uses it: Developers, enterprises requiring precise control over memory and context, and teams running multi-agent coding or research workflows.
Limitations: Extended thinking is slower and more expensive. Opus 4.7 is priced at $5/$25 per million tokens. The gap versus Sonnet widens on abstract reasoning tasks like ARC-AGI-2.
Gemini 2.5 Pro
Google’s current production frontier model. The headline capability is native multimodality text, audio, images, and video, all handled within a 1M token context window, without separate modality modules bolted on. Deep Think, an extended reasoning mode available to AI Ultra subscribers, allocates additional compute before producing a final answer on hard problems. Recall holds at 100% up to 530,000 tokens and 99.7% at 1 million tokens. Pricing starts at $1.25/$10 per million tokens via Vertex AI.
Who uses it: Google Cloud customers, developers building multimodal applications, and teams processing large documents or video at scale.
Limitations: Latency increases noticeably at very long contexts. Less mature third-party tooling and integration ecosystem than OpenAI’s API surface.
Llama 4 Scout
Meta’s open-weight MoE model. 109B total parameters, 17B active per token, runs on a single NVIDIA H100 GPU with int4 quantization. The practical implication is that a 10M token context window is accessible without a data center contract.30 Early-fusion multimodality means text and vision are processed jointly from the first layer rather than combined at the output stage. Available under Meta’s Llama 4 Community License.
Who uses it: Researchers, organizations that need on-premise deployment, developers avoiding vendor lock-in, and teams where cost at scale makes API pricing untenable.
Limitations: Performance depends heavily on hosting configuration and quantization choices. Requires infrastructure investment and ML ops capacity. Less production polish than commercial models.
DeepSeek V4
DeepSeek’s fourth-generation model is available as a preview. Uses a 1-trillion-parameter MoE architecture roughly 50% larger than V3 with multimodal capabilities across text, image, and video. Thinking in Tool-Use lets the model reason internally before calling external tools and verifying tool outputs against its own logic, which is the core differentiator for agentic workflows.API input pricing starts at $0.27 per million tokens (cache-miss), roughly 18x cheaper than GPT-5.5.31
Who uses it: Cost-sensitive enterprise teams, researchers, and developers deploying self-hosted or local inference where Western API pricing is a constraint.
Limitations: US export controls on advanced chips constrain DeepSeek’s training compute access. Smaller developer ecosystem and fewer production integrations than OpenAI or Anthropic. V4 is still in preview, not yet at full stable release.
FAQs
A large language model is an AI model designed to generate and understand human-like text by analyzing vast amounts of data.
These foundational models are based on deep learning techniques and typically involve neural networks with many layers and a large number of parameters, allowing them to capture complex patterns in the data they are trained on.
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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