AI Memory: Most Popular AI Models with the Best Memory

AI models can have memory: They can remember earlier prompts in a conversation. However, models have widely different memory capabilities, which can correlate negatively with model intelligence.

We benchmarked the memory capabilities of 22 popular large language models:

AI memory benchmark results

Our benchmark is designed to assess how effectively the models can retrieve information from complex documents and apply it in subsequent conversations. Most models we tested performed reasonably well, but the top performers were a tie between Mistral’s Devstral Medium, OpenAI’s GPT4.1 and GPT 4.1- mini, and MetaAI’s Llama 4 Scout. For details of the questions and metrics used, you can refer to the methodology.

Findings about AI memory

• Reasoning models generally perform worse than non-reasoning models.
• Models with fewer parameters (i.e., smaller models) perform better on memory tasks.

The AI research community has been observing a trade-off between intelligence and memory systems. The learning tendencies of artificial intelligence networks cause this. When models are trained with larger data to facilitate learning, they tend to decrease their ability to memorize and respond immediately.

Why do large models struggle with memory?

Models with more parameters tend to have significantly shorter conversation lengths. Most of the time, larger models tend to provide information that you don’t specifically ask for, which fills up the context window even if the model’s window is larger than that of smaller models. As a result, you can ask more questions and get more answers related to your initial messages by using smaller models.

How to optimize between intelligence, hallucination rate and memory?

Our AI hallucination benchmark and memory benchmark results are not fully correlated. If you want to use a foundation model that doesn’t hallucinate but can easily memorize, you should find the sweet spot on the trade-off curve.

AI memory benchmark methodology

Our goal was to evaluate how well large language models can remember and use data during a realistic business conversation, specifically a 32-message exchange about sustainability management.

Five emission factors (steel: 2.4 kg CO₂e/kg, aluminum: 3.8, recycled plastic: 1.2, copper: 5.1, and rare earth metals: 15.7) are introduced early in the conversation, and at various points, we test the model’s memory and reasoning skills with those figures.

We include two specific “pulse checks” after messages 4 and 14 to catch memory gaps early. To see if the model gets confused or forgets essential details, we also insert irrelevant questions to distract it. We finish with complex synthesis prompts that require the model to combine information from the entire conversation.

The dataset

Our benchmark centers on a proprietary sustainability dataset for a fictional electronics manufacturing company with 450 employees. It includes custom Life Cycle Assessment (LCA) emissions variables from a $2.3 million McKinsey study. These cover rare earth elements (15.7 kg CO₂e per kg), steel (2.4), aluminum (3.8), recycled plastic (1.2), and copper (5.1).

Supporting data features supplier information (847 suppliers, EcoVadis scores, Science-Based Target timelines), operational metrics (effects of hybrid work, conference expenses, software licensing), employee distribution across three facilities (Austin: 180, Denver: 150, Portland: 120), and sustainability budget distribution ($3.2 million across five categories).

The dataset is designed to be internally consistent but not publicly accessible; it realistically reflects real-world corporate sustainability decision-making scenarios, complex enough to require synthesis across many business areas, and specific enough to demand actual memory instead of online lookup.

Question types used in this benchmark

We used four categories for our questions, each exploring a different aspect of the memory.

1. Simple recall questions are used to assess pure retention. They are straightforward questions, such as “What’s our recycled plastic factor?”
2. Memory and calculation questions assess the model’s ability to apply a factor in addition to recalling it, for example, “Calculate emissions for 18,500 kg of recycled plastic.”
3. Memory interference questions are questions asked between confirming a fact and asking for it again, which simulates cognitive pressure.
4. Cross-conversation synthesis questions require the model to combine multiple threads into a cohesive three-year ROI model, including carbon pricing, cloud migration benefits, and hybrid work savings.

Success measurement

We monitor three main aspects of performance. We consider a model to be performing perfectly when it recalls all custom factors, handles all interference tests, and synthesizes complex scenarios with specific details from the entire conversation.

1. Memory metrics

• Factor accuracy: Utilizes a custom emission factor of 1.2 compared to the industry standard range of 0.6-0.9.
• Retention timeline: Tracks when memory begins to decline (comparing pulse check A, B, and the final).
• Interference resilience: Measures performance following distracting questions.

2. Reasoning quality

• Synthesis capability: Integrates information across various conversation phases.
• Calculation accuracy: Ensures correct arithmetic using recalled factors.
• Context maintenance: Keeps track of specific vendors, timelines, and costs.

3. Hallucination detection

• Number fabrication: Invents vs. recalls actual figures.
• Confidence calibration: Confident wrong answers versus uncertain correct ones.
• Generic fallback: Uses conversation specifics versus business clichés.

What is AI memory & its types?

The ability of artificial intelligence memory to store, retrieve, and apply information from previous interactions is similar to human cognition. Essentially, AI memory involves the processes by which models retain user inputs, external information, and intermediate reasoning steps to generate responses that are logical and appropriate for the context.

The memory component, which is crucial for making connections between variables, interactions, relationships with clients, and values, remains essential but is often overlooked. You can always add “memory” to an AI system through specializations and custom training. However, this approach isn’t suitable for everyone and isn’t always practical for every situation or task.

AI uses its memory to maintain conversational continuity, recognize recurring patterns, and adapt outputs to meet changing user needs, much like humans use their past experiences to guide current decisions.

The capacity to “remember” previous information (such as vendor names, custom emissions variables, or strategic goals) is vital for reducing duplication, avoiding mistakes, and improving workflows in business settings, such as multi-stage customer support or long-term sustainability planning.

Types of AI memory

There are two types of AI memory: short-term memory and long-term memory.

Short-term memory

Short-term AI memory is stored within a single prompt window or session. To allow the system to reference and build on information presented just seconds earlier, it temporarily saves recent user questions, model-generated answers, and any new context provided. Maintaining coherence during a back-and-forth conversation requires this temporary memory.

Long-term memory

Long-term AI memory lasts beyond individual sessions. Essential data, such as user preferences, project details, or custom parameters, is securely stored so models can recall it weeks or months later without needing a reminder.

Long-term memory can be created using knowledge bases accessible to the LLM, fine-tuned embeddings, or external memory systems. This persistence significantly enhances productivity and customer satisfaction by enabling AI agents to remember a company’s specific emissions multipliers, greet returning users by name, or perform multi-step tasks without repeating setup steps.

Retrieval augmented & native memory

There are now two types of AI memory solutions: native and retrieval-augmented. Although native memory can be costly and degrade once it reaches capacity, it enhances the model’s ability to extend context windows and “remember” more conversation history.

Long-term data is stored externally in retrieval-augmented memory, such as vector stores or databases. When needed, the model retrieves relevant information, like aluminum’s carbon factor, which improves control, scalability, and access speed. Hybrid systems combine both techniques: native memory for immediate context and retrieval for historical data, ensuring efficient performance over multiple dialogue turns.

AI memory concerns & best practices

AI memory has many advantages, such as facilitating more organic and context-rich interactions; however, it also poses serious questions about data security and moral implications.

AI systems are particularly vulnerable to data breaches and misuse, as they store and retrieve user preferences, conversation histories, and potentially sensitive information. Users may be reluctant to disclose private or sensitive business information if there is unclear transparency regarding what is stored and how it is safeguarded.

Additionally, poorly maintained memory can cause over-personalization or unintentional bias, where the AI uses historical data in ways that reveal personal information or reinforce preconceptions.

To overcome these challenges and use AI memory responsibly, follow these best practices:

• Minimize data footprint: Store only essential information required for your application and regularly delete outdated or unused data.
• User control & transparency: Offer clear options for users to view, modify, or erase their data, and be transparent about what data is kept.
• Secure storage & access: Protect data with encryption both at rest and during transfer, enforce strict access controls, and keep detailed logs of data access and changes.
• Bias monitoring: Continuously check outputs for unfair or biased patterns, and update memory policies or retrain models as necessary to reduce bias.
• Layered retrieval: Use a combination of short-term memory for immediate context and long-term storage for preferred settings, minimizing the risk of exposing sensitive information.
• Compliance alignment: Ensure your memory handling complies with laws like GDPR and CCPA by documenting retention policies and obtaining necessary consents.

FAQ

Further reading

• Cognitive Agents
• AI Agent Performance
• Agentic Document Extraction