GPT-5

This comprehensive handbook provides an in-depth exploration of GPT-5, the groundbreaking language model released by OpenAI in early 2025. GPT-5 represents a paradigm shift in artificial intelligence, introducing deliberative reasoning a capability that enables the model to internally debate and verify its own thought processes before generating responses. Built on a sophisticated Mixture-of-Experts (MoE) architecture with a 512,000 token context window, native multimodality, and advanced tool-use capabilities, GPT-5 sets new standards for reasoning, coding, and real-world problem solving. This guide systematically presents the model architecture, performance benchmarks, access methods, API integration patterns, advanced features, and practical applications, serving as an essential resource for researchers, developers, and technical decision-makers seeking to harness the full potential of GPT-5.

1. Introduction GPT-5

The release of GPT-4 in 2023 marked a significant milestone in the journey toward artificial general intelligence, demonstrating remarkable proficiency in language understanding, generation, and even multimodal reasoning. However, as developers and researchers pushed the boundaries of what was possible, fundamental limitations became apparent: models often generated plausible-sounding but incorrect answers, struggled with multi-step reasoning, and lacked the ability to introspect or verify their own outputs.

GPT-5, launched in early 2025, was designed to overcome these challenges. It introduced a revolutionary capability: deliberative reasoning. Unlike previous models that produced responses in a single forward pass, GPT-5 internally simulates a chain of thought, debates possible answers, and verifies its conclusions before presenting them to the user. This process, inspired by how humans deliberate, dramatically improves accuracy, reduces hallucinations, and enables the model to tackle problems that were previously out of reach.

Beyond reasoning, GPT-5 brings significant architectural advancements. It employs a sophisticated Mixture-of-Experts (MoE) design that scales to trillions of parameters while maintaining computational efficiency. Its native multimodal capabilities allow it to understand and reason about images, diagrams, and documents alongside text. With a context window of 512,000 tokens, it can process entire books, codebases, or lengthy conversation histories in a single session.

This guide provides a complete, A to Z exploration of GPT-5. Whether you are a developer integrating the API, a researcher evaluating its capabilities, or a business leader considering adoption, the following chapters will equip you with the knowledge to understand, access, and leverage this transformative technology.

2. Model Architecture and Technical Specifications

2.1 Mixture-of-Experts (MoE) Architecture

At the heart of GPT-5 lies a sparse Mixture of Experts architecture. Instead of activating all parameters for every token, the model employs a routing network that directs each input to a subset of specialized “expert” modules. This design achieves the capacity of a much larger dense model while keeping inference costs manageable.

How It Works: For each token processed, a gating network evaluates the input context and selects the most relevant experts typically 2 to 8 out of dozens or hundreds. Only these experts are activated, and their outputs are combined to produce the final representation. This sparse activation means that the total computational cost per token is far lower than if all parameters were used, enabling GPT-5 to scale to over a trillion parameters while maintaining reasonable latency.

Benefits of MoE:

• Scalability: The model can incorporate knowledge from diverse domains without a proportional increase in compute.

• Specialization: Different experts naturally specialize in different types of knowledge—mathematics, code, creative writing, scientific reasoning—leading to higher quality outputs.

• Graceful Degradation: Under heavy load, the model’s performance degrades more gracefully than dense models because the routing network can prioritize critical tasks.

Trade-offs: The MoE architecture introduces some complexity. Routing decisions can sometimes be suboptimal, leading to slight inconsistencies in style or depth for very similar prompts. Additionally, the model requires careful engineering to balance the load across experts during both training and inference.

2.2 Deliberative Reasoning: The Core Innovation

GPT-5’s most transformative feature is its ability to engage in deliberative reasoning. Unlike standard autoregressive generation, which produces tokens left to right without revisiting earlier decisions, GPT-5 internally simulates a multi-step reasoning process before committing to an answer.

Internal Deliberation Process:

1. Problem Decomposition: When given a complex query, the model first breaks it down into sub-problems or steps.

2. Hypothesis Generation: For each step, it generates multiple possible approaches or answers.

3. Evaluation and Verification: It evaluates these hypotheses against internal knowledge and logical consistency, discarding those that are flawed.

4. Synthesis: Finally, it synthesizes the verified reasoning steps into a coherent response.

This process is not directly visible to the user (unless explicitly requested via chain-of-thought prompting) but operates in the model’s latent space. The result is responses that are more accurate, better reasoned, and less prone to hallucinations.

Why It Matters: Deliberative reasoning enables GPT-5 to excel at tasks that require multi-step logic, such as advanced mathematics, code debugging, and complex planning. It also makes the model more transparent and trustworthy, as users can ask it to “show its work” and receive a step by step explanation.

2.3 Context Window and Multimodal Capabilities

Context Window: GPT-5 supports a context window of up to 512,000 tokens enough to process the entire text of “The Great Gatsby” more than 20 times over, or a codebase of several hundred thousand lines. This massive capacity enables the model to maintain coherence over extremely long documents, multi-turn conversations, or complex projects without losing track of earlier information.

Multimodal Input: GPT-5 is natively multimodal. It accepts images as input alongside text, allowing it to:

• Interpret charts, graphs, and diagrams

• Extract information from scanned documents

• Understand visual context in screenshots or photographs

• Reason about engineering drawings or medical scans

While GPT-5 does not generate images natively (it relies on DALL-E integration for image generation), its understanding of visual information is at near-human level, enabling applications like automated captioning, visual question answering, and multimodal analysis.

2.4 Training Data and Knowledge Cutoff

GPT-5 was trained on a massive and diverse corpus encompassing public web text, books, academic papers, code repositories, and multilingual sources. The training process involved extensive filtering to remove low-quality content, duplicates, and personally identifiable information. The model’s knowledge cutoff is September 2024, meaning it is aware of events and developments up to that date.

Training Innovations: OpenAI employed advanced techniques such as curriculum learning, reinforcement learning from human feedback (RLHF), and constitutional AI principles to shape the model’s behavior. The deliberative reasoning capability was cultivated through specialized training that rewarded internal consistency and step-by-step verification.

3. Performance Benchmarks and Capabilities

GPT-5 has been evaluated on a comprehensive set of benchmarks that test reasoning, knowledge, coding, and multimodal understanding. The results demonstrate a significant leap over GPT-4 and competing models.

3.1 Reasoning and Knowledge (MMLU, GPQA)

MMLU (Massive Multitask Language Understanding): This benchmark covers 57 subjects across STEM, humanities, and social sciences. GPT-5 achieves a score of 92.5%, surpassing GPT-4’s 86.4% and approaching expert-level performance in many domains.

GPQA (Graduate-Level Google-Proof Q&A): Designed to test deep reasoning in biology, physics, and chemistry, GPQA features questions that are difficult even for PhDs. GPT-5 scores 85.7%, a substantial improvement over GPT-4’s 65% and indicating that the model can synthesize knowledge across disciplines at a near-expert level.

3.2 Mathematical Problem Solving (MATH)

The MATH benchmark consists of competition-level mathematics problems. GPT-5 achieves 94.0% , correctly solving problems in algebra, calculus, geometry, and number theory. This represents a 10-point gain over GPT-4 and places GPT-5 among the top performers on this challenging suite.

3.3 Coding Proficiency (HumanEval, SWE-bench)

HumanEval: GPT-5 scores 96.8% on this benchmark, which tests the model’s ability to generate correct Python code from docstrings. Its code is not only syntactically correct but often more efficient and secure than human-written solutions.

SWE-bench: This real-world benchmark presents the model with actual GitHub issues from popular repositories. GPT-5 resolves 72.8% of issues, a dramatic improvement over GPT-4’s 45% and approaching the performance of a mid-level software engineer. This capability makes GPT-5 a powerful assistant for software development tasks like bug fixing, feature implementation, and code refactoring.

3.4 Multilingual and Multimodal Performance

Multilingual MMLU: GPT-5 demonstrates near-English proficiency across dozens of languages, including Mandarin, Spanish, Arabic, and Hindi, with average scores above 90% on translated versions of MMLU.

Multimodal Benchmarks (MMMU, VQA): On the MMMU benchmark, which requires reasoning over images and text, GPT-5 scores 68.5% , outperforming GPT-4V and other multimodal models. It can accurately answer questions about charts, diagrams, and real-world images, making it suitable for applications in education, research, and accessibility.

4. The GPT-5 Model Family

To meet diverse needs, OpenAI offers several variants of GPT-5, each optimized for different use cases.

4.1 GPT-5 (Flagship)

• Purpose: The most capable and intelligent model. Designed for tackling complex reasoning tasks, scientific research, and high-stakes applications.

• Use Cases: Advanced mathematics, multi-step planning, legal analysis, and any task requiring deep deliberation.

• Characteristics: Highest accuracy, longest reasoning times, and full access to the model’s capabilities. Best for non-latency-sensitive applications.

4.2 GPT-5-Turbo

• Purpose: Optimized for speed and cost-efficiency. It is a distilled version of the flagship model, trained to produce high-quality responses with lower latency.

• Use Cases: Chatbots, real-time customer support, content generation, and applications where responsiveness is critical.

• Characteristics: Faster inference, lower cost, and slightly reduced performance on extremely complex reasoning tasks compared to the flagship.

4.3 GPT-5-Mini

• Purpose: A compact model designed for on-device deployment and resource-constrained environments. It can be further fine-tuned for specific tasks.

• Use Cases: Mobile apps, edge devices, privacy-sensitive applications where data cannot leave the device.

• Characteristics: Small footprint, fast local inference, but with capabilities focused on general language understanding rather than deep reasoning.

4.4 GPT-5-Code

• Purpose: A specialized variant fine-tuned exclusively for programming tasks. It excels at code generation, debugging, and explanation.

• Use Cases: Integrated development environments (IDEs), code review tools, automated documentation, and pair programming assistants.

• Characteristics: Superior performance on coding benchmarks, deeper understanding of programming languages, and optimized for tool use (e.g., executing code, interacting with version control).

5. Access and Availability

5.1 For Consumers: ChatGPT Plus, Pro, and Team

GPT-5 is integrated into ChatGPT, available to paid subscribers:

• ChatGPT Plus ($20/month): Access to GPT-5-Turbo with moderate usage limits. Suitable for everyday tasks and general assistance.

• ChatGPT Pro ($200/month): Unlimited access to the flagship GPT-5 model, priority during peak times, and advanced features like the “Deep Research” agent that can run extended analyses.

• ChatGPT Team: Shared workspace with collaborative features, access to GPT-5 and GPT-5-Turbo, and higher context windows for team projects.

5.2 For Developers: OpenAI API

Developers can integrate GPT-5 into their applications via the OpenAI API. Access requires an API key, which can be obtained from the OpenAI platform after creating an account and adding payment information.

API Endpoints:

• Chat Completions (/v1/chat/completions): The primary interface for most applications.

• Completions (/v1/completions): Legacy endpoint, still supported but not recommended for new projects.

• Assistants API (/v1/assistants): For building persistent, stateful agents with tools.

5.3 For Enterprises: Custom Deployments and Fine-Tuning

Enterprises with specialized needs can contract for private, isolated instances of GPT-5 models. They can also fine-tune GPT-5-Turbo and GPT-5-Mini on proprietary data to create bespoke models tailored to their domain. Fine-tuning for the flagship model is not generally available due to its complexity and risk of catastrophic forgetting.

5.4 Pricing and Rate Limits

Pricing is based on token usage (input and output). As of early 2025, approximate rates are:

Rate limits vary by subscription tier and are expressed in tokens per minute (TPM) and requests per minute (RPM). Higher tiers offer increased limits.

6. API Integration Guide GPT-5

6.1 Authentication and Basic Setup

All API requests require authentication using an API key. Store your key securely, preferably in environment variables.

Python Example:

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key-here"  # Use os.getenv("OPENAI_API_KEY") in production
)

JavaScript Example:

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

6.2 Chat Completions API

The Chat Completions endpoint is the primary interface for interacting with GPT-5.

response = client.chat.completions.create(
    model="gpt-5",  # or "gpt-5-turbo", "gpt-5-code"
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain the concept of deliberative reasoning."}
    ],
    temperature=0.5,
    max_tokens=500
)

print(response.choices[0].message.content)

6.3 Function Calling (Tool Use)

GPT-5 can call external functions or APIs based on user input. This enables the model to perform actions like fetching data, executing code, or interacting with other services.

Define Tools:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    }
]

Invoke with Tool Choice:

response = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools,
    tool_choice="auto"  # Model decides whether to call a tool
)

The response will include a tool_calls field if the model decides to use a function.

6.4 Structured Outputs and JSON Mode

For applications that require structured data, GPT-5 supports JSON mode and can be guided to produce output conforming to a specific schema.

JSON Mode:

response = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "List three fruits and their colors as JSON."}],
    response_format={"type": "json_object"}
)

With Pydantic (Python):

from pydantic import BaseModel

class FruitList(BaseModel):
    fruits: list[dict[str, str]]

completion = client.beta.chat.completions.parse(
    model="gpt-5",
    messages=[{"role": "user", "content": "List three fruits and their colors."}],
    response_format=FruitList
)
data = completion.choices[0].message.parsed
print(data.fruits)

6.5 Streaming for Real-Time Applications

Streaming allows you to receive tokens as they are generated, enabling a responsive user experience.

stream = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "Write a short story."}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

6.6 Error Handling and Retries GPT-5

Implement robust error handling to manage rate limits and transient failures.

import time
from tenacity import retry, stop_after_attempt, wait_exponential

@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
def call_gpt5(messages):
    try:
        response = client.chat.completions.create(
            model="gpt-5",
            messages=messages,
            max_tokens=500
        )
        return response
    except Exception as e:
        if "429" in str(e):  # Rate limit
            time.sleep(5)  # Respect Retry-After header if present
            raise  # Let tenacity retry
        elif "500" in str(e) or "503" in str(e):  # Server errors
            raise  # Retry
        else:
            # Client errors (400, 401, etc.) should not be retried
            raise

7. Advanced Features in Depth GPT-5

7.1 System Prompts and Instruction Following

System prompts are a powerful way to set the behavior, tone, and constraints for the model. GPT-5 is highly steerable and follows instructions with remarkable consistency.

Example System Prompt:

You are a legal AI assistant. Your responses must:
1. Be based solely on the provided context.
2. Cite specific sections of the context when making claims.
3. If the information is not in the context, state that you cannot answer and suggest consulting a human lawyer.
4. Maintain a formal and neutral tone.

7.2 Temperature, Top-P, and Reproducibility

• Temperature (0–2): Controls randomness. Lower values make output more deterministic; higher values increase creativity.

• Top-P: Alternative to temperature, controlling nucleus sampling. Values like 0.9 mean the model considers only tokens that make up the top 90% probability mass.

• Seed: For reproducible outputs, set a seed parameter along with temperature=0.

response = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "Tell me a joke."}],
    temperature=0.8,
    seed=42  # Ensures reproducibility across calls with same seed
)

7.3 Logit Bias and Token Control

Logit bias allows you to adjust the probability of specific tokens appearing in the output. This can be used to discourage certain words or enforce constraints.

response = client.chat.completions.create(
    model="gpt-5",
    messages=[{"role": "user", "content": "Write a product description."}],
    logit_bias={
        "2435": -100,  # Discourage the token for "cheap"
        "640": -100    # Discourage "inexpensive"
    }
)

7.4 The Assistants API for Persistent Threads

The Assistants API simplifies building stateful applications by managing conversation threads, tools, and files.

Create an Assistant:

assistant = client.beta.assistants.create(
    name="Math Tutor",
    instructions="You are a personal math tutor. Answer questions step-by-step.",
    model="gpt-5",
    tools=[{"type": "code_interpreter"}]
)

Create a Thread and Run:

thread = client.beta.threads.create()
message = client.beta.threads.messages.create(
    thread_id=thread.id,
    role="user",
    content="Solve for x: 2x + 5 = 15"
)
run = client.beta.threads.runs.create(
    thread_id=thread.id,
    assistant_id=assistant.id
)
# Poll for completion and retrieve messages

8. Prompt Engineering for GPT-5

Prompt engineering is the art of crafting inputs to elicit the best possible outputs from the model. GPT-5’s deliberative reasoning makes it more responsive to well-structured prompts.

8.1 Leveraging Deliberative Reasoning

To activate deep reasoning, explicitly ask the model to think step by step.

Example:

Solve this physics problem: A ball is thrown upward at 20 m/s. How high does it go? Think step-by-step, showing all formulas and calculations. Then double-check your work for errors.

The model will internally deliberate and produce a verified answer.

8.2 Chain-of-Thought and Self-Verification

For complex tasks, combine chain-of-thought prompting with a request for self-verification.

Prompt:

Plan a week-long itinerary for a trip to Paris. First, list the major attractions. Then, group them by geographical proximity. Next, assign them to days considering opening hours. Finally, review the itinerary to ensure it's logistically feasible and not too rushed.

8.3 Few-Shot and In-Context Learning

Providing examples in the prompt helps the model understand the desired format and style.

Example:

Convert these Python functions to JavaScript:

Python:
def add(a, b):
    return a + b

JavaScript:
function add(a, b) {
    return a + b;
}

Python:
def greet(name):
    return f"Hello, {name}!"

JavaScript:

8.4 Persona-Based Prompting for Specialization

Assigning a persona primes the relevant expert networks in the MoE architecture.

Prompt:

You are a senior software architect with 15 years of experience. Review the following code for scalability and security. Provide specific recommendations.

8.5 Handling Long Contexts

When working with large documents, structure your prompts to help the model locate relevant information.

Example:

I have attached a 300-page financial report. Answer the following questions based on it:
1. What was the total revenue for 2024?
2. Summarize the risk factors mentioned in Section 4.
3. Compare the R&D spending to the previous year.
Always cite the page number where you found the information.

9. Real-World Applications and Use Cases

9.1 Software Development: From Specification to Deployment

A developer can feed GPT-5 a product specification and an existing codebase, then ask it to implement new features, write tests, and generate documentation. The model’s deep understanding of code structure and dependencies enables it to make changes that integrate seamlessly.

Example:

Here is our current Django app code. Add a new endpoint for user profile editing. Ensure it follows our existing patterns, uses the User model, and includes unit tests. Write a brief explanation of the changes.

9.2 Scientific Research: Hypothesis Generation and Data Analysis

Researchers can use GPT-5 to analyze literature, generate hypotheses, and even design experiments. Its ability to synthesize information across domains makes it a valuable collaborator.

Example:

I have uploaded 20 recent papers on CRISPR gene editing. Based on these, identify unresolved questions in the field. Propose three novel hypotheses that could be tested experimentally. For each, suggest a high-level experimental approach.

9.3 Legal and Financial Document Analysis

Lawyers and analysts can upload lengthy contracts or financial reports and ask GPT-5 to extract key clauses, summarize risks, or compare versions.

Example:

This is a 200-page merger agreement. Highlight all termination clauses and summarize the conditions under which either party can walk away. Also, list any unusual indemnification provisions.

9.4 Creative Industries: Storytelling and Multimodal Concepts

Writers and artists can use GPT-5 to brainstorm ideas, develop characters, and generate story outlines. Combined with image understanding, it can also analyze visual references and suggest creative directions.

Example:

Here is a series of mood board images for a fantasy film. Based on these visuals and the logline provided, write a detailed scene breakdown for the opening sequence. Include camera angles, lighting, and character actions.

9.5 Education: Personalized Tutoring

GPT-5 can serve as a personalized tutor, adapting explanations to the student’s level, providing practice problems, and offering step-by-step guidance.

Example:

I'm a high school student struggling with calculus. Explain the concept of derivatives using simple terms and analogies. Then give me three practice problems with increasing difficulty. After I attempt them, check my answers and explain any mistakes.

10. Agentic Capabilities and Tool Integration

GPT-5 is not just a language model—it’s an agent capable of using tools to accomplish tasks.

10.1 Code Interpreter: Executing Python for Analysis

The Code Interpreter tool allows GPT-5 to write and execute Python code in a sandboxed environment. This enables data analysis, visualization, and complex calculations.

Example Workflow:

1. User uploads a CSV file.

2. GPT-5 writes code to clean the data, perform statistical analysis, and generate plots.

3. The code is executed, and results (including images) are returned to the user.

10.2 Web Browsing: Real-Time Information Retrieval

With web browsing, GPT-5 can search the internet for up-to-date information, read articles, and incorporate findings into its responses. This extends its knowledge beyond the training cutoff.

Example:

Search for the latest news on quantum computing breakthroughs in 2025. Summarize the top three developments and explain their potential impact.

10.3 Function Calling: Connecting to External APIs

Through function calling, GPT-5 can interact with any API. This enables it to book appointments, check inventory, send emails, or perform any action that has a programmable interface.

Example:

I need to schedule a meeting with John for next Tuesday at 3 PM. Check my calendar (via API) for availability, then send an invitation if the slot is free.

10.4 Multi-Agent Simulations

Researchers can use the API to create simulations where multiple GPT-5 instances, each with distinct personas, interact. This can model economic scenarios, social dynamics, or collaborative problem-solving.

Example:

Create three agents: a consumer, a regulator, and a corporate executive. Simulate a discussion about a new data privacy regulation. Have each agent argue their position based on their persona. Then summarize the debate.

11. Safety, Alignment, and Limitations

11.1 Deliberative Alignment: A New Safety Paradigm

GPT-5 introduces deliberative alignment, a technique where the model is trained to reason about safety guidelines internally. Instead of simply learning which outputs are unsafe from examples, it is given a specification of safety policies and learns to apply them through deliberation. This makes its refusal behavior more nuanced and harder to bypass.

11.2 Refusal Behavior and Content Moderation

When faced with a potentially harmful request, GPT5 may refuse to comply, but it often provides a contextual explanation. For example, if asked for instructions on building a weapon, it might explain why it cannot provide such information and suggest alternative, safe topics. This approach reduces the likelihood of adversarial jailbreaks.

11.3 Hallucinations and Mitigation Strategies

Despite improvements, GPT-5 can still generate incorrect information, especially in areas outside its training data or when pushed beyond its capabilities. Common mitigation strategies include:

• Asking the model to cite sources

• Requesting step-by-step reasoning

• Using structured outputs to validate against schemas

• Implementing retrieval-augmented generation (RAG) with verified knowledge bases

11.4 Ethical Considerations and Responsible Use

Organizations deploying GPT-5 should consider:

• Transparency: Inform users when they are interacting with an AI.

• Fairness: Monitor for biased outputs and implement corrective measures.

• Privacy: Avoid sending sensitive personal data to the API.

• Accountability: Ensure human oversight for critical decisions.

OpenAI provides usage policies and guidelines to promote responsible deployment.

12. Conclusion and Future Outlook

GPT5 represents a watershed moment in the evolution of artificial intelligence. By introducing deliberative reasoning, scaling to unprecedented sizes with MoE, and expanding context and multimodal capabilities, it has moved closer to the long-standing goal of artificial general intelligence. Its impact is already being felt across industries—from software development and scientific research to education and creative arts.

Yet, GPT-5 is not the final destination. The rapid pace of innovation suggests that future versions will bring even deeper reasoning, more efficient architectures, and richer multimodal interactions. The release of GPT-5.1 and GPT-5.2 in late 2025 demonstrates OpenAI’s commitment to continuous improvement, adding features like adaptive reasoning, extended prompt caching, and specialized coding tools.

For developers, researchers, and businesses, mastering GPT-5 today provides a foundation for leveraging whatever comes next. The principles of prompt engineering, tool integration, and safety awareness will remain relevant as models evolve. GPT-5 is not just a tool. it is a partner in problem-solving, a catalyst for creativity, and a glimpse into a future where AI collaborates with humanity to achieve the extraordinary.

13. Appendices GPT-5

API Reference (Key Parameters)

Cost Comparison Across Models

Glossary of Terms

• Deliberative Reasoning: A process where the model internally debates and verifies its own thought steps before generating a response.

• Mixture of Experts (MoE): An architecture that uses multiple specialized sub-networks and a routing mechanism to activate only a subset per token.

• Context Window: The maximum number of tokens the model can consider in a single prompt.

• Token: A unit of text, roughly equivalent to 3/4 of a word in English.

• Function Calling: The model’s ability to invoke external functions or APIs.

• Code Interpreter: A tool that allows the model to execute Python code in a sandboxed environment.

• Assistants API: An API for building persistent, stateful agents with tools and threads.

• System Prompt: A message that sets the behavior and persona of the assistant.

• Temperature: A parameter controlling output randomness.

• Hallucination: Generation of false or nonsensical information.