{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"gpt-4-turbo","slug":"gpt-4-turbo","name":"GPT-4 Turbo","type":"model","url":"https://openai.com/index/new-models-and-developer-products-announced-at-devday/","page_url":"https://unfragile.ai/gpt-4-turbo","categories":["llm-apis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"gpt-4-turbo__cap_0","uri":"capability://text.generation.language.128k.context.window.long.form.understanding","name":"128k context window long-form understanding","description":"Processes up to 128,000 tokens in a single request using an optimized transformer architecture with efficient attention mechanisms, enabling analysis of entire documents, codebases, or conversation histories without truncation. This extended context is achieved through architectural improvements to the base GPT-4 model that reduce memory overhead while maintaining coherence across long sequences.","intents":["Analyze entire source code files or multi-file projects without splitting into chunks","Process full research papers, legal documents, or technical specifications in one pass","Maintain conversation history across 50+ turn interactions without losing context","Retrieve and reason over large knowledge bases or documentation sets without summarization"],"best_for":["Enterprise developers building document analysis systems","Research teams processing academic papers and technical reports","Teams building conversational agents requiring extended memory"],"limitations":["Latency increases with context size; 128K tokens may add 5-10 seconds vs 4K context","Cost scales linearly with token count; longer contexts increase API costs proportionally","Attention computation remains O(n²) internally, limiting practical use of full 128K for real-time applications"],"requires":["OpenAI API key with GPT-4 Turbo access","HTTP client capable of handling large request/response payloads (>500KB)","Token counting library to stay within 128K limit (e.g., tiktoken)"],"input_types":["text","code","markdown","structured data (JSON, CSV)","image (via vision capability)"],"output_types":["text","code","structured JSON","markdown"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__cap_1","uri":"capability://image.visual.multimodal.vision.language.understanding","name":"multimodal vision-language understanding","description":"Processes both text and image inputs simultaneously using a unified transformer architecture that encodes images into visual tokens and interleaves them with text tokens for joint reasoning. Images are converted to token sequences via a vision encoder, then processed alongside text through the same language model backbone, enabling tasks like image captioning, visual question answering, and code-image analysis.","intents":["Extract text and analyze content from screenshots, diagrams, or scanned documents","Answer questions about images, charts, or visualizations in natural language","Debug code by analyzing error screenshots alongside source code snippets","Generate code or documentation based on UI mockups or architecture diagrams"],"best_for":["Developers building document processing or OCR-adjacent applications","Teams automating visual QA or screenshot analysis workflows","Builders creating multimodal AI agents that reason over images and text"],"limitations":["Image processing adds ~500ms-1s latency per request regardless of image complexity","Supports JPEG, PNG, GIF, WebP formats only; requires preprocessing for other formats","Image understanding quality degrades for very small text (<10pt) or complex diagrams with dense information","No video input support; must extract frames and process individually"],"requires":["OpenAI API key with vision-enabled GPT-4 Turbo access","Images in supported formats (JPEG, PNG, GIF, WebP) under 20MB per image","Base64 encoding or URL hosting for image transmission to API"],"input_types":["text","image (JPEG, PNG, GIF, WebP)"],"output_types":["text","code","structured JSON"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__cap_10","uri":"capability://automation.workflow.high.volume.batch.processing.api.with.cost.optimization","name":"high-volume batch processing api with cost optimization","description":"Processes large volumes of requests asynchronously through a batch API that queues requests and processes them during off-peak hours, reducing per-token costs by up to 50% compared to standard API calls. Trades latency (results available within 24 hours) for cost savings, making it ideal for non-time-sensitive workloads like data processing, content generation, and analysis pipelines that can tolerate delayed results.","intents":["Process millions of documents or records with LLM analysis at reduced cost","Generate large volumes of synthetic data or content without real-time latency requirements","Run nightly batch jobs that analyze accumulated data from the previous day","Optimize infrastructure costs for non-interactive LLM workloads"],"best_for":["Data teams processing large datasets with LLM analysis","Content platforms generating bulk content overnight","Cost-sensitive organizations willing to trade latency for savings"],"limitations":["Results are available within 24 hours, not in real-time; unsuitable for interactive applications","Batch API has different rate limits and quotas than standard API","Debugging failed requests is more complex due to asynchronous processing"],"requires":["OpenAI API key with batch API access","Requests formatted as JSONL (JSON Lines) with specific batch format","Ability to poll for results or handle webhook callbacks"],"input_types":["text","code","structured data"],"output_types":["text","code","structured data"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__cap_2","uri":"capability://text.generation.language.json.mode.structured.output.generation","name":"json mode structured output generation","description":"Enforces valid JSON output by constraining the model's token generation to only produce well-formed JSON structures, using a constrained decoding approach that validates each token against JSON grammar rules. When JSON mode is enabled, the model generates only tokens that maintain valid JSON syntax, preventing malformed output and eliminating the need for post-hoc parsing or validation.","intents":["Generate structured API responses that are guaranteed to parse without error handling","Extract data from unstructured text into predefined JSON schemas reliably","Build reliable data pipelines where downstream systems expect valid JSON input","Create deterministic function outputs for LLM-powered agents without validation overhead"],"best_for":["Backend engineers building LLM-powered APIs with strict output contracts","Data engineers creating ETL pipelines that require guaranteed valid JSON","Teams building production agents where parsing failures are unacceptable"],"limitations":["JSON mode requires explicit schema definition; free-form JSON generation is not supported","Constrains model creativity; some complex reasoning tasks may produce suboptimal results when forced into rigid JSON structures","Adds ~5-10% latency overhead due to grammar constraint checking on each token","Does not validate semantic correctness of JSON content, only syntactic validity"],"requires":["OpenAI API key with GPT-4 Turbo access","Explicit JSON schema definition in system prompt or via schema parameter","JSON schema must be valid according to JSON Schema specification"],"input_types":["text","code","structured data"],"output_types":["structured JSON (guaranteed valid)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__cap_3","uri":"capability://text.generation.language.reproducible.output.generation.with.seed.parameter","name":"reproducible output generation with seed parameter","description":"Enables deterministic model outputs by accepting a seed parameter that controls the random number generation used in sampling, allowing identical prompts with identical seeds to produce identical responses. The seed controls softmax temperature sampling and other stochastic elements in the generation process, making outputs reproducible for testing, debugging, and audit trails.","intents":["Generate consistent test cases for LLM-powered features without flakiness","Create reproducible audit trails for compliance or debugging purposes","Enable A/B testing by fixing model behavior while varying other parameters","Build deterministic workflows where output consistency is required across runs"],"best_for":["QA engineers testing LLM-powered features with deterministic expectations","Compliance teams requiring reproducible audit trails for regulated systems","Researchers comparing model behavior across different prompts or parameters"],"limitations":["Reproducibility is not guaranteed across different API versions or model updates","Seed parameter only controls sampling randomness; does not affect model weights or training","Different seeds may produce similar outputs for high-confidence predictions, limiting diversity","Reproducibility applies only within a single API call; parallel requests may not be deterministic"],"requires":["OpenAI API key with GPT-4 Turbo access","Seed parameter specified as integer (0-2^32-1) in API request","Identical prompt, temperature, and other generation parameters for reproducibility"],"input_types":["text","code","image"],"output_types":["text","code","structured JSON"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__cap_4","uri":"capability://tool.use.integration.parallel.function.calling.with.multi.tool.orchestration","name":"parallel function calling with multi-tool orchestration","description":"Enables the model to invoke multiple functions simultaneously in a single response by generating multiple tool_call objects in parallel, rather than sequentially. The model analyzes the prompt, identifies independent function calls, and returns them all at once, which the client then executes in parallel and returns results in a single follow-up message for batch processing.","intents":["Execute multiple independent API calls in parallel to reduce round-trip latency","Orchestrate complex workflows where multiple tools must be called before proceeding","Build agents that can reason about multiple data sources simultaneously","Reduce API call overhead by batching independent function invocations"],"best_for":["Backend engineers building LLM agents with multiple tool dependencies","Teams building data aggregation systems requiring parallel API calls","Developers optimizing latency-sensitive LLM applications"],"limitations":["Requires client-side implementation to execute parallel calls and aggregate results; no built-in orchestration","Model may not always recognize opportunities for parallelization; sequential calls still possible","Parallel execution adds complexity to error handling; one failed call may require retry of entire batch","Limited to tools defined in the function schema; cannot call arbitrary external services"],"requires":["OpenAI API key with GPT-4 Turbo access","Function schema definitions in OpenAI function calling format","Client-side code to execute parallel function calls and aggregate results","Error handling logic for partial failures in parallel execution"],"input_types":["text","code"],"output_types":["multiple tool_call objects","structured JSON"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__cap_5","uri":"capability://text.generation.language.improved.instruction.following.with.reduced.hallucination","name":"improved instruction following with reduced hallucination","description":"Implements enhanced training techniques (including RLHF refinements and instruction-tuning improvements) to better adhere to user constraints and system prompts while reducing factual hallucinations. The model uses a combination of supervised fine-tuning on high-quality instruction examples and reinforcement learning from human feedback to calibrate confidence and avoid inventing information.","intents":["Build reliable systems where the model respects strict constraints and system prompts","Reduce false information in knowledge-intensive applications like customer support bots","Create deterministic workflows where the model follows specific formatting or behavior rules","Improve accuracy in code generation by better following architectural constraints"],"best_for":["Teams building production systems requiring high instruction adherence","Customer-facing applications where hallucinations damage trust","Developers building agents with strict behavioral constraints"],"limitations":["Hallucination reduction is probabilistic; not eliminated entirely, especially for out-of-distribution queries","Improved instruction following may reduce model creativity for open-ended tasks","Requires well-crafted system prompts and instructions; poorly written prompts still produce poor outputs","No mechanism to verify factual accuracy; model may confidently state false information"],"requires":["OpenAI API key with GPT-4 Turbo access","Well-structured system prompts that clearly define constraints and expected behavior","Clear, specific user instructions (vague prompts still produce vague outputs)"],"input_types":["text","code","image"],"output_types":["text","code","structured JSON"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__cap_6","uri":"capability://text.generation.language.april.2024.knowledge.cutoff.with.real.time.context.injection","name":"april 2024 knowledge cutoff with real-time context injection","description":"Provides a model trained on data through April 2024, with the ability to accept real-time context through user prompts and system messages to supplement outdated knowledge. The model itself has no built-in web search or real-time data access, but users can inject current information via the prompt to ground responses in up-to-date facts.","intents":["Build applications requiring current information by injecting real-time data into prompts","Understand recent events or developments by providing context in the system prompt","Create knowledge-grounded applications where users supply the ground truth","Avoid hallucinations about recent events by explicitly providing factual context"],"best_for":["Teams building applications where users provide real-time context","Developers creating knowledge-grounded systems with external data sources","Applications where April 2024 knowledge is sufficient and real-time data is not critical"],"limitations":["No built-in web search or real-time data access; requires external data sources for current information","Knowledge cutoff at April 2024 means no awareness of events after that date without explicit context injection","Users must manually provide context for recent events; no automatic knowledge updates","Context injection increases token usage and latency proportionally to the amount of injected data"],"requires":["OpenAI API key with GPT-4 Turbo access","External data sources or APIs to fetch real-time information for context injection","Application logic to retrieve and format current data for inclusion in prompts"],"input_types":["text","code","image","injected context (real-time data)"],"output_types":["text","code","structured JSON"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__cap_7","uri":"capability://text.generation.language.cost.optimized.inference.with.3x.faster.performance","name":"cost-optimized inference with 3x faster performance","description":"Achieves 3x faster inference speed and significantly lower API costs compared to GPT-4 base through architectural optimizations including efficient attention mechanisms, reduced model size through knowledge distillation, and optimized inference kernels. The model maintains comparable intelligence to GPT-4 while reducing computational overhead through techniques like grouped query attention and flash attention implementations.","intents":["Build cost-sensitive applications where API spend is a critical constraint","Create real-time interactive systems requiring sub-second response latency","Scale LLM applications to handle high throughput without proportional cost increases","Replace GPT-4 base in existing systems to reduce operational expenses"],"best_for":["Startups and teams with limited budgets requiring cost-effective LLM APIs","High-throughput applications like customer support bots or content generation platforms","Real-time interactive systems where latency is a primary constraint"],"limitations":["Performance gains come at the cost of some model capabilities; edge cases may be handled less robustly than GPT-4 base","Cost savings are relative; still more expensive than smaller models like GPT-3.5 Turbo","Speed improvements are measured on OpenAI's infrastructure; actual latency depends on network and client implementation","No local deployment option; must use OpenAI API, limiting cost optimization for high-volume use cases"],"requires":["OpenAI API key with GPT-4 Turbo access","Acceptance of slightly lower performance on edge cases vs GPT-4 base","Application architecture that can tolerate API latency (not suitable for sub-100ms requirements)"],"input_types":["text","code","image"],"output_types":["text","code","structured JSON"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__cap_8","uri":"capability://code.generation.editing.code.generation.and.reasoning.with.extended.context","name":"code generation and reasoning with extended context","description":"Generates and analyzes code across multiple files and large codebases using the 128K context window to understand architectural patterns, dependencies, and project structure without truncation. The model can reason about entire projects, suggest refactorings, identify bugs across file boundaries, and generate code that respects existing patterns and conventions.","intents":["Generate code that respects existing project architecture and patterns across multiple files","Identify bugs and security issues by analyzing entire codebases without missing context","Suggest refactorings that maintain consistency across large projects","Understand and document complex systems by analyzing full source code"],"best_for":["Developers working on large codebases requiring full-project context","Teams using AI for code review and architectural analysis","Builders creating AI-powered IDE features for code understanding"],"limitations":["Code generation quality depends on code quality in training data; patterns from low-quality code may be reproduced","Extended context increases latency and cost; full-codebase analysis is slower than single-file analysis","No built-in understanding of build systems, dependencies, or runtime behavior; requires explicit context","Generated code may not compile or run without testing; model cannot verify correctness"],"requires":["OpenAI API key with GPT-4 Turbo access","Code in supported languages (Python, JavaScript, Java, C++, Go, Rust, etc.)","Token counting to ensure codebase fits within 128K limit","IDE or editor integration to send code context to API"],"input_types":["code","text (natural language instructions)"],"output_types":["code","text (explanations)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__cap_9","uri":"capability://code.generation.editing.vision.based.code.understanding.and.debugging","name":"vision-based code understanding and debugging","description":"Analyzes code screenshots, error messages, and UI elements to understand debugging context and provide targeted fixes. The model can extract code from screenshots, read error stack traces from terminal captures, and correlate visual UI state with code logic to diagnose issues.","intents":["Debug issues by analyzing error screenshots and code screenshots together","Understand UI bugs by analyzing screenshots of broken interfaces alongside code","Extract code from images or documentation for analysis and modification","Diagnose deployment or runtime errors from terminal screenshots"],"best_for":["Developers debugging visual or UI-related issues","Teams using screenshot-based bug reports in issue trackers","Builders creating AI-powered debugging tools"],"limitations":["OCR quality for code in screenshots is lower than plain text; small fonts or poor contrast reduce accuracy","Cannot execute code or verify fixes; requires manual testing","Vision understanding of complex UI layouts may be imperfect, especially for custom components","Adds latency and cost compared to plain text code analysis"],"requires":["OpenAI API key with vision-enabled GPT-4 Turbo access","Screenshots in supported formats (JPEG, PNG, GIF, WebP)","Clear, readable code and error messages in screenshots"],"input_types":["image (screenshots)","text (code, error messages)"],"output_types":["text (explanations, fixes)","code"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gpt-4-turbo__headline","uri":"capability://llm.apis.multimodal.large.language.model.api","name":"multimodal large language model api","description":"OpenAI's GPT-4 Turbo is a powerful multimodal large language model API that supports both text and vision inputs, offering enhanced performance with a 128K context window and improved instruction following.","intents":["best multimodal LLM API","multimodal LLM for text and vision tasks","top large language model for developers","fast and cost-effective LLM solutions","GPT-4 alternatives for advanced AI applications"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["llm-apis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"low","permissions":["OpenAI API key with GPT-4 Turbo access","HTTP client capable of handling large request/response payloads (>500KB)","Token counting library to stay within 128K limit (e.g., tiktoken)","OpenAI API key with vision-enabled GPT-4 Turbo access","Images in supported formats (JPEG, PNG, GIF, WebP) under 20MB per image","Base64 encoding or URL hosting for image transmission to API","OpenAI API key with batch API access","Requests formatted as JSONL (JSON Lines) with specific batch format","Ability to poll for results or handle webhook callbacks","Explicit JSON schema definition in system prompt or via schema parameter"],"failure_modes":["Latency increases with context size; 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