What Is GPT-5? Everything You Need to Know
- Jarvy Sanchez
- Aug 8
- 11 min read
GPT-5 is the latest model in OpenAI’s generative series, designed to handle reasoning, coding, and multimodal input with greater precision and efficiency. It improves context retention, adapts computation based on task complexity, and reduces latency for high-frequency queries.
The system can process text, code, and images simultaneously, making it practical for both rapid responses and in-depth analysis. Benchmark results indicate measurable gains over GPT-4 and o1 in logic-heavy tasks, multilingual accuracy, and code reliability.
Let's look at how GPT-5 works, the improvements over earlier models, its performance against Grok 4, Claude Opus 4, and Gemini 2.5 Pro, and what you should consider before deploying it in your production workflows.
What Is GPT-5?
GPT-5 is the latest generation in OpenAI’s model suite, designed as a single, adaptive system rather than a loose collection of separate models. It dynamically balances latency, reasoning depth, and accuracy in real time.
GPT-5 is built around three primary execution modes:
Default Model: Fast, high-quality responses for routine queries.
GPT-5 Thinking: More compute for multi-step reasoning and problem solving.
GPT-5 Pro: Extended reasoning using scaled parallel computing for the most complex tasks.
A built-in routing layer decides which mode to use based on prompt complexity, tool requirements, and explicit user intent (for example, “analyze/think this in depth”).
Routing is reinforced with continuous learning from live usage, factoring in correctness, user preferences, and model-switch patterns, so allocation improves over time. When usage ceilings are reached, smaller mini variants take over to maintain continuity with lower latency.
GPT-5 Family Specifications and Performance Comparison
Advancements Over GPT-4 and o1
GPT-5 builds on the same transformer architecture as GPT-4 and o1 but incorporates targeted updates to reasoning, accuracy, and multimodal integration.
1. Reasoning: More consistent multi-step inference with reduced error propagation in chained logic tasks.
2. Accuracy: Incremental gains in domain-specific precision, especially in technical, scientific, and multilingual outputs.
3. Latency: Improved inference efficiency reduces response delays under sustained usage.
4. Context Handling: Extended conversation tracking with fewer context losses in long sessions.
5. Multimodality: More stable alignment between text, code, and image processing pipelines. Benchmarks for different GPT-5 variants are as follows:
Core Technologies and Training Data
The model retains a transformer-based architecture trained through large-scale unsupervised learning, followed by fine-tuning with reinforcement learning from human feedback (RLHF). GPT-5 adds adaptive compute allocation, enabling the system to dedicate more processing cycles to complex reasoning requests without affecting shorter, simpler tasks.
OpenAI has not disclosed the full training dataset, but based on observed capabilities, it likely includes filtered web content, curated technical and scientific materials, multilingual corpora, public and licensed code repositories, and labeled image-text datasets. Architectural refinements appear focused on inference stability, latency reduction, and more efficient context retrieval.
Multimodal Features and Real-World Use Cases
GPT-5 can process and integrate text, code, and images within the same request, allowing for coordinated reasoning across formats. This enables:
Code Development and Review: Generating or debugging code with contextual cues from diagrams or screenshots.
Technical Summarization: Condensing large documents while referencing related visual data.
Interface Analysis: Identifying UI components, layout inconsistencies, or accessibility concerns from image inputs.
Operational Support: Augmenting support workflows by analyzing both written descriptions and attached visual evidence.
The multimodal pipeline operates natively, removing the need for separate processing stages when handling mixed input types.
Reliability, Safety, and Behavior
Factuality & Hallucinations
GPT-5 significantly reduces hallucinations compared to previous models. When using web-enabled prompts typical of ChatGPT traffic, GPT-5 responses are about 45% less likely to contain factual errors than GPT-4o. With “thinking” mode enabled, this improvement rises to roughly 80% fewer factual errors compared to the earlier OpenAI o3 model.
Evaluations on open-ended factuality benchmarks such as LongFact and FActScore show GPT-5 produces about six times fewer hallucinations than o3. These improvements reflect stronger reliability in handling complex, fact-based, and long-form content.
2. Honesty
GPT-5 is better at recognizing when it cannot complete a task and communicates its limitations more clearly. In controlled tests removing images from multimodal prompts, GPT-5 only gave confident but incorrect answers about 9% of the time, compared to 86.7% for o3.
Deception rates, measured in scenarios with impossible coding tasks or missing multimodal inputs, dropped from 4.8% for o3 to 2.1% for GPT-5 with reasoning enabled. While this is a meaningful reduction, OpenAI continues to research further improvements in honesty and factuality.
3. Sycophancy
Earlier versions of ChatGPT occasionally responded with excessive agreement or flattery. GPT-5 reduces sycophantic responses from approximately 14.5% to under 6%, a significant decrease achieved without compromising response quality.
This leads to more balanced, thoughtful, and less effusive conversations. GPT-5 also avoids unnecessary emojis and adjusts follow-ups to feel more like a knowledgeable peer than a generic AI assistant.
Safety, Fine-Tuning, and Customization
GPT-5 applies a safe completions training method that balances helpfulness with safety. Rather than outright rejecting unclear or sensitive prompts, it provides partial or high-level responses when appropriate and clearly states reasons for refusal with safer alternatives. This approach improves handling of ambiguous intent and dual-use scenarios.
For high-risk domains like biology, GPT-5 uses layered safety measures including threat modeling and extensive red-teaming.
Customization options include preset conversational tones - Cynic, Nerd, Robot, and Listener - that adjust interaction style without requiring prompt changes. These presets maintain low levels of sycophancy while supporting varied user preferences.
Deployment & Access
GPT-5 is the default model for all ChatGPT users - Free, Plus, Pro, Team, and Enterprise. Plus subscribers receive higher usage limits compared to free users.
Pro subscribers get unlimited access and unlock GPT-5 Pro, which provides extended reasoning for complex tasks.
Team, Enterprise, and Edu plans include throughput and tool integrations suited for organizational use.
Free-tier users with high usage may be switched to GPT-5 Mini, a smaller, faster variant with similar capabilities.
Additionally, Pro, Plus, and Team subscribers can use GPT-5 via the Codex CLI for coding workflows after logging in through ChatGPT.
GPT-5 Benchmarks and Performance Results
Below are key benchmark results across GPT-5 variants (High capacity, Mini, and Nano), as reported by OpenAI.
Performance in Coding, Reasoning, and Multilingual Tasks
1. Intelligence
The GPT-5 high variant performs well on academic benchmarks, scoring above 90% on both AIME ’25 and HMMT 2025 math competitions. Scores decline as expected for the smaller GPT-5 mini and nano models due to their reduced size and resources but remain solid.
On FrontierMath, which tests math problems using a Python tool, performance drops from 26.3% (high) to 9.6% (nano), showing the added difficulty for smaller models handling tool-assisted reasoning.
2. Multimodal Capabilities
All GPT-5 versions show competent multimodal understanding. The high model scores above 84% on MMMU and VideoMMU tasks.
The mini and nano models show a consistent drop in performance but maintain above 60%. On the ERQA benchmark, which involves explanation reasoning, smaller models, especially nano, show a significant decrease, indicating limits in handling complex multimodal reasoning.
3. Hallucination Rates
Hallucination rates remain low for all GPT-5 variants. The high model scores about 1% or less on LongFact benchmarks, which test factual consistency on concepts and objects. FActScore hallucinations are slightly higher but still within reasonable limits, showing improvements over earlier models.
The mini and nano models have higher hallucination rates, with nano reaching 7.3% on FActScore, reflecting the trade-off between model size and reliability.
4. Function Calling
Function calling accuracy varies by domain. Telecom performs best, with 96.7% accuracy for the high model, followed by retail and airline domains. Smaller models perform notably worse in telecom, showing that detailed, domain-specific function execution benefits from larger model capacity.
5. Coding, Instruction Following, and Long Context Handling
The GPT-5 high model performs strongly across coding, instruction adherence, and long context tasks. It achieves nearly 75% accuracy on SWE-bench Verified and 88% on Aider polyglot coding benchmarks.
Mini and nano variants show lower coding accuracy but remain suitable for moderate tasks. The high variant’s estimated freelance earnings ($112K) reflect its capability in complex coding work.
The instruction following is reliable across all models, with near-perfect scores above 96% on the COLLIE benchmark. However, performance dips on more challenging internal API evaluations, especially for smaller models, indicating some room to improve instruction precision.
For handling long contexts, GPT-5 high maintains over 90% accuracy on tasks involving 128k tokens and performs well up to 256k tokens. Mini and nano models show significant performance decreases at longer context lengths, reflecting inherent limitations in their memory and context windows
Performance Considerations and Limitations of GPT-5
GPT-5’s architecture improves reasoning, multimodal input, and instruction following. Larger models handle complex tasks and long contexts well, with fewer errors. Smaller versions tend to exhibit lower performance in reasoning and coding, making them better suited for simpler tasks where cost or speed are more important.
Long context handling remains solid up to 400,000 tokens with limited loss of accuracy. Hallucination rates are reduced compared to previous models.
For coding and function calling, the full GPT-5 model outperforms the mini and nano variants significantly. So, selecting a model depends on the complexity of your use case and available resources.
GPT-5 vs Competitor Models: Key Specs and Pricing Comparison
Why GPT-5 Is a Game-Changer
GPT-5 introduces multimodal reasoning that handles text and images together, which enables workflows that involve visual and textual inputs simultaneously. This is a step beyond prior models limited to text-only prompts.
Safety training has moved away from simple refusal toward a nuanced approach that attempts to provide partial or high-level answers when full responses pose risks. This reduces unnecessary refusals but still maintains guardrails, a necessary balance for dual-use scenarios.
Internal benchmarks show GPT-5 performs better than OpenAI’s o3 model on front-end coding about 70% of the time. The model has improved instruction following and tool integration based on real-world coding data.
Impact on Developers, Product Teams, and Researchers
GPT-5’s reasoning extends to multi-step workflows requiring sustained context, as shown by its 96.7% score on the τ2-bench telecom tool-calling benchmark. This suggests it can effectively sequence and manage complex calls with external tools.
Developers can utilize new API parameters to control verbosity and reasoning effort, allowing tuning between faster, less detailed answers and slower, more thorough responses. Support for plaintext-based custom tools increases flexibility when integrating GPT-5 with external systems.
Product teams can prototype AI features faster using GPT-5’s improved multimodal and reasoning abilities. Researchers may explore more complex mixed-media workflows, given the model’s ability to process images and text in tandem.
Pricing and Access
API Pricing (Standard)
Prices per 1 million tokens:
For detailed pricing and updates, check OpenAI’s official documentation.
How to Access GPT-5
Using GPT-5 via ChatGPT
Just create an OpenAI account or log in if you already have one. GPT-5 runs by default in ChatGPT. If you need higher limits or extra features, check the available plans and pricing here: ChatGPT pricing, and upgrade accordingly.
Accessing GPT-5 Through API
To use GPT-5 through the OpenAI API, you first need an OpenAI account and a valid API key. After adding a payment method on your billing page, generate an API key for authentication.
API requests are sent as HTTP calls with the key included in the headers. You specify the model (e.g., gpt-5, gpt-5-mini, or gpt-5-nano) and provide prompts in JSON format.
The API supports parameters like verbosity (low, medium, high) and reasoning_effort (minimal to extensive) to control response detail and speed.
GPT-5 supports large context windows - up to 400,000 tokens per request - and accepts multimodal inputs, including text plus images (PNG, JPEG, WebP, GIF) up to 50 MB per call.
Microsoft also provides GPT-5 access through Azure AI Foundry, integrating the model with enterprise security and compliance features.
Safety, Ethics, and Responsible Use
GPT-5 uses multiple safety layers, including content filters, human review, and account controls, especially in sensitive areas like biology. It aims to provide safe, policy-compliant responses rather than blunt refusals, but can still occasionally output problematic content in complex cases.
Hallucinations and false claims are reduced but not eliminated, with ongoing monitoring of model reasoning to catch errors. Extensive external testing helped identify risks like jailbreaks and misinformation. The model enforces system-level safety rules above user instructions.
Data privacy is enforced with encryption and compliance with standards like GDPR. User data isn’t used for training by default. You should also be cautious when sharing sensitive or confidential information through the model.
Responsible use requires transparency, continuous monitoring, and clear AI attribution. The model blocks harmful or illegal requests and supports bias mitigation and fairness through human oversight.
Your Next Move
GPT-5 offers better reasoning, coding, and multimodal processing, which helps with more complex tasks and longer context requirements. You can pick smaller versions to save costs, but they come with lower performance.
When using GPT-5, match the model size to your project needs and budget. Make sure you have monitoring in place to handle errors and unexpected results.
You can connect with our experts to discuss GPT-5 integration, explore practical use cases, or get guidance on optimizing its capabilities for your projects.
FAQs
1. What Is GPT-5 Used For?
GPT-5 handles reasoning, coding, and multimodal inputs (text and images). It’s used for tasks like code generation and debugging, technical summarization, UI analysis, and complex multi-step workflows involving both text and images.
2. How Does GPT-5 Compare to GPT-4 and o1?
GPT-5 improves on GPT-4 and o1 in reasoning consistency, accuracy, latency, and multimodal integration. It better handles long contexts, reduces hallucinations, and follows instructions more reliably.
3. Is GPT-5 Available for Free?
Yes, GPT-5 is the default model for free ChatGPT users with usage limits. Paid plans (Plus, Pro, Team, Enterprise) offer higher limits, additional features, and extended reasoning capabilities.
4. What Are GPT-5’s Benchmarks?
GPT-5 scores above 90% on several academic reasoning tests, performs well on multimodal benchmarks (around 80%+ accuracy), and achieves roughly 75% accuracy on coding benchmarks like SWE-bench Verified. Smaller variants trade accuracy for speed and cost.
5. Can GPT-5 Generate Images or Handle Video?
GPT-5 accepts images as input alongside text for integrated reasoning but does not generate images or videos. It supports image formats like PNG, JPEG, WebP, and GIF for tasks such as UI analysis and document review.