ChatGPT-5.4: Instant Solutions for Deep Analysis, AI Workflows, Daily Tasks, and Beyond
Chat with ChatGPT-5.4 instantly and test it with models like Opus 4.6 and Gemini 3.1 in our unified AI workspace for large-context understanding, native computer-use capabilities, and more.
The model is designed and optimized for advanced reasoning, coding, visual understanding, and native computer-use workflows
GPT-5.4 Capabilities : Analyze Large-Scale Data, Build Smarter Systems, and More
Try GPT-5.4 on Lorka AI to reason through complex problems and run multi-step digital tasks with this advanced OpenAI ChatGPT model and other top AI models available today.
Higher-level reasoning with fewer hallucinations
Strong coding and debugging
1M-token context window
GPT-5.4 Native computer-use
High-quality multimodal insight
GPT-5.4 Benchmarks: Real Prompt Examples
Go over the following prompts to get an idea of how you can use ChatGPT-5.4 to handle coding, reasoning, visual interpretation, and translation tasks.
Write a secure Python script that connects to an IMAP server, checks for unread emails, and downloads attachments to a local folder with proper error handling.
1import imaplib2import email3from email.header import decode_header4import os5import logging67IMAP_SERVER = "imap.example.com"8EMAIL_ACCOUNT = "user@example.com"9PASSWORD = "your_password"10DOWNLOAD_DIR = "./attachments"1112logging.basicConfig(level=logging.INFO)1314def download_attachments():15 try:16 mail = imaplib.IMAP4_SSL(IMAP_SERVER)17 mail.login(EMAIL_ACCOUNT, PASSWORD)18 mail.select("inbox")1920 status, messages = mail.search(None, 'UNSEEN')21 email_ids = messages[0].split()2223 if not os.path.exists(DOWNLOAD_DIR):24 os.makedirs(DOWNLOAD_DIR)2526 for e_id in email_ids:27 res, msg = mail.fetch(e_id, "(RFC822)")28 for response in msg:29 if isinstance(response, tuple):30 msg_obj = email.message_from_bytes(response[1])3132 for part in msg_obj.walk():33 content_disposition = str(part.get("Content-Disposition"))3435 if "attachment" in content_disposition:36 filename = part.get_filename()37 if filename:38 filepath = os.path.join(DOWNLOAD_DIR, filename)3940 if not os.path.exists(filepath):41 with open(filepath, "wb") as f:42 f.write(part.get_payload(decode=True))43 logging.info(f"Saved attachment: {filename}")4445 mail.logout()4647 except imaplib.IMAP4.error as e:48 logging.error(f"IMAP error: {e}")49 except Exception as e:50 logging.error(f"Unexpected error: {e}")5152if __name__ == "__main__":53 download_attachments()Here’s a screenshot of my desktop showing files named IMG_001.jpg, IMG_002.jpg, IMG_003.jpg. Write a bash command to rename them to vacation_###.jpg.
1i=12for f in IMG_*.jpg; do3 printf -v num "%03d" $i4 mv "$f" "vacation_$num.jpg"5 ((i++))6doneResult: IMG_001.jpg → vacation_001.jpg IMG_002.jpg → vacation_002.jpg IMG_003.jpg → vacation_003.jpg
You have a 7-liter jug and a 3-liter jug. Neither has measurement marks. How can you measure exactly 5 liters?
1) Fill the 7-liter jug. 2) Pour from the 7-liter jug into the 3-liter jug until it is full: Remaining in 7-liter jug: 4 liters 3) Empty the 3-liter jug. 4) Pour the 4 liters from the 7-liter jug into the 3-liter jug until full: Remaining in 7-liter jug: 1 liter 5) Empty the 3-liter jug, then pour the 1 liter into it. 6) Fill the 7-liter jug again, then pour into the 3-liter jug (which already contains 1 liter) until it is full.
The 7-liter jug now contains exactly 5 liters.
Translate this NDA clause into formal Japanese suitable for a corporate agreement: ‘The receiving party agrees not to disclose confidential information obtained during the course of this partnership.
受領当事者は、本提携の過程において取得した機密情報を、 事前の書面による承諾なく第三者に開示しないことに同意するものとする。
Meaning (English back-translation): “The receiving party agrees not to disclose confidential information obtained during the course of this partnership without prior written consent.”
GPT-5.4 on Lorka AI : Privacy, Performance, and a Multi-LLM Chat
Try GPT 5.4 with a platform designed to make your work, studies, and daily tasks easier. No setup, no complexity.
Access GPT-5.4 and Other AI Models in One Chat
Use GPT-5.4 on Lorka alongside other leading models in the same chat to switch to GPT-5.3 Instant for ultra-fast responses, DeepSeek for math problems, and Opus 4.6 for coding.
Fast, Optimized Performance
Our platform is optimized to run OpenAI models efficiently. It delivers faster response times and smoother conversations, even for complex prompts and coding workflows.
Privacy-First AI
Conversations on Lorka are never used for model training to give you, whether individually or as a team, confidence when working with sensitive data or confidential projects.
Pre-Optimized Prompt Modes
Try our prompt templates and workflow modes, designed for coding, writing, research, and analysis, to get better results and control over your outputs, whether you’re a beginner or advanced user.
How to Use GPT-5.4 on Lorka
Get started using GPT on our platform by following these steps:
- Select GPT-5.4 from the model dropdown in our AI chat to start a new conversation.
- Enter your prompt or upload files. You can upload large PDFs, datasets, and more to use the model’s 1M-token context window and multimodal capabilities.
- Get instant results for coding, research, writing, data analysis, or planning complex agentic workflows.
GPT-5.4 Technical Info
Model Type / Architecture
- The model is designed and optimized for advanced reasoning, coding, visual understanding, and native computer-use workflows
Primary Use Cases
- Coding and debugging large projects
- Complex reasoning and data analysis
- Autonomous agent workflows and task automation
- Document processing and long-form content generation
Context Length (Input Window)
- Supports up to 1M tokens, which helps you analyze entire codebases, large PDFs, datasets, long conversations, and more without losing context
Modalities
- Input: Text, code, PDFs, and high-resolution images
- Output: Text and structured code responses
Key Strengths
- Tool search optimization reduces token usage by up to 47%, improving efficiency for complex tasks
- Strong performance in agentic workflows, enabling multi-step problem-solving and automated task execution
Known Limitations
- Processing extremely large prompts may take longer compared to lightweight models such as GPT-5.3 Instant, which are optimized for speed rather than deep reasoning
How to Use GPT-5.4 as a Developer, Product Lead, and More
Optimize frontend architecture as a developer
Generate and troubleshoot code in various large archives with strong reasoning and context awareness.
Optimize this React component for faster rendering and explain which hooks are causing unnecessary re-renders.
"Condense large reports into clear takeaways as an analyst
Analyze long reports and research files to quickly gather the most important insights.
Summarize this 150-page financial report into the top 5 strategic insights for executives.
"Translate specialized documents accurately across global teams
Translate technical or sophisticated material while keeping the same tone and meaning.
Translate this medical journal abstract into German while keeping the terminology accurate.
"Turn market goals into steps as a product or growth guide
Convert ideas and business goals into structured steps for successful launches and campaigns.
Draft a Q3 go-to-market strategy for a B2B SaaS startup targeting mid-market finance teams.
"Find insights from lengthy dashboards as a finance or ops team
Upload charts or screenshots and collect precise signals from complicated visual data.
Analyze this dashboard and identify the main cause of the Q2 revenue dip.
"Manage desktop tools faster as an IT or operations specialist
Use OpenAI’s model to interpret software interfaces and guide step-by-step actions from screenshots.
Based on this screenshot of my database tool, where do I click to export the full dataset?
"Review large codebases as an engineering lead
Understand architecture, dependencies, and refactoring opportunities across complex repositories.
Analyze this repository and suggest architectural improvements to reduce API latency.
"Break down technical research faster as a student
Process long papers, datasets, and technical materials to surface methods, findings, and limitations.
Review this research paper and summarize the methodology, findings, and limitations.
"OpenAI GPT-5.4 vs. Other Leading LLMs
Review how OpenAI’s model compares with other LLMs on Lorka to understand each model’s ideal use case to better use them together.
| Models | Reasoning | Speed | Multimodality | Context | Ideal use cases |
|---|---|---|---|---|---|
GPT-5.4 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Advanced reasoning, autonomous workflows, massive document analysis, and multimodal technical tasks. |
GPT-5.3 Instant | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Natural language processing and highly economical, large-scale |
GPT-5.2 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Flexible logic and independent task completion for copywriting, coding, deep research, and strategic design. |
Claude Sonnet 4.6 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | High-speed independent logic, self-directed programming, and universal OS interaction. |
Claude Opus 4.6 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Expert system development, multi-step AI automation, core corporate operations, and complex data investigation. |
DeepSeek V3.2 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Fast analytical logic, multi-step system interaction, and math/science applications. |
Gemini 3.1 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Real-time interactive assistance and high-fidelity creative generation. |
Grok 4.1 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Metric-guided roadmaps, audience mood tracking, and high-speed evaluation. |
Mistral Large | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Daily text automation and widespread system scaling. |
LLama 3.2 / 4 | 💡💡💡💡💡 | ⚡⚡⚡⚡⚡ | 🤖🤖🤖🤖🤖 | 🧠🧠🧠🧠🧠 | Transparent or corporate-controlled architectures and strict compliance-first applications. |
GPT-5.4
Advanced reasoning, autonomous workflows, massive document analysis, and multimodal technical tasks.
GPT-5.3 Instant
Natural language processing and highly economical, large-scale
GPT-5.2
Flexible logic and independent task completion for copywriting, coding, deep research, and strategic design.
Claude Sonnet 4.6
High-speed independent logic, self-directed programming, and universal OS interaction.
Claude Opus 4.6
Expert system development, multi-step AI automation, core corporate operations, and complex data investigation.
DeepSeek V3.2
Fast analytical logic, multi-step system interaction, and math/science applications.
Gemini 3.1
Real-time interactive assistance and high-fidelity creative generation.
Grok 4.1
Metric-guided roadmaps, audience mood tracking, and high-speed evaluation.
Mistral Large
Daily text automation and widespread system scaling.
LLama 3.2 / 4
Transparent or corporate-controlled architectures and strict compliance-first applications.
Strengths and Limitations of ChatGPT-5.4 and Other AI Models on Lorka
GPT-5.4
OpenAI’s latest flagship model is especially strong for autonomous workflows, huge document analysis, and complex technical tasks.
Its heavyweight architecture can make it slower than lighter models on simple prompts.
GPT-5.3 Instant
An extremely fast, economical model designed for mass-scale integration, everyday text automation, and concise conversational flow with fewer denials.
Due to its emphasis on speed, it’s less suitable for multi-step reasoning or long-horizon agentic workflows compared to the 5.4 version or even 5.2.
GPT-5.2
Offers dependable performance across a wide range of tasks that is supported by strong reasoning, and follows detailed instructions well.
It uses more compute than lighter models, so it is less suitable for highly time-sensitive use cases where instant replies matter most.
Claude Sonnet 4.6
A high-speed model that offers frontier-grade coding and agentic capabilities, backed by a 1-million-token context window for larger tasks.
It is not as strong as Opus for the most reasoning-heavy work, especially when it comes to high-stakes tasks like broad codebase restructuring.
Claude Opus 4.6
Anthropic’s most capable model for deep reasoning and highly accurate data collection across long-context inputs.
Its more deliberate reasoning style makes it slower than Sonnet, which can add unnecessary latency for easier tasks.
Gemini 3.1
Excellent for long-context reasoning, multimodal analysis, and benchmark-heavy technical tasks.
Can be less consistent in agentic coding, terminal workflows, and deeper planning-heavy use cases.
DeepSeek V3.2
A very efficient open-weight model with reasoning and coding performance that competes closely with the newest GPT-class systems.
It still trails leading proprietary models in total knowledge breadth and in the maturity of its surrounding integrations and tooling.
Grok 4.1
Strong at interpreting public sentiment, responding quickly, and delivering dependable reasoning for problem-solving tasks.
Its integrations and developer tools are still expanding, and access to certain multimodal features may vary by workflow.
Mistral Large
Delivers strong multilingual performance and reliable reasoning for text-focused work, with flexible deployment options.
It is centered mainly on text, with more limited multimodal support, and its context size and ecosystem trail the biggest proprietary platforms.
Llama 3.2 / 4
Semi-open weights, strong coding and reasoning in Llama 4, and dependable vision capabilities in 3.2 Vision make it well-suited for fine-tuning and privacy-focused deployments.
Out-of-the-box performance usually remains behind the newest closed frontier models, with results depending heavily on hosting quality, tuning, and prompt design.
FAQs
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