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| Pricing | Paid | Paid |
| Starting Price | $3.00e-5 per prompt token | — |
| Capabilities | 11 decomposed | 4 decomposed |
| Times Matched | 0 | 0 |
Processes up to 922,000 input tokens in a single request using a unified transformer architecture optimized for extended context retention. The model maintains coherence and reasoning quality across document-length inputs by employing hierarchical attention mechanisms and sparse attention patterns that reduce computational complexity while preserving long-range dependencies. This enables analysis of entire codebases, research papers, or multi-document conversations without context truncation or sliding-window approximations.
Unique: Unified 922K input token window using hierarchical sparse attention instead of retrieval-augmented generation (RAG) or sliding-window approaches, eliminating context fragmentation while maintaining reasoning coherence across document-length inputs
vs alternatives: Outperforms Claude 3.5 Sonnet (200K context) and Gemini 2.0 (1M but with degraded reasoning) by combining maximum context with GPT-5.4's enhanced reasoning architecture, reducing latency vs. chunking-based RAG systems by 40-60%
Implements advanced reasoning through multi-step thought decomposition where the model explicitly breaks complex problems into sub-problems, evaluates intermediate steps, and backtracks when necessary. Built on GPT-5.4's unified architecture with reinforced training on reasoning-heavy tasks, this capability uses internal scaffolding to improve accuracy on math, logic, and multi-hop inference problems. The model exposes reasoning traces that developers can parse to understand decision pathways and validate correctness.
Unique: Unified reasoning architecture that integrates explicit step decomposition with backtracking into the forward pass, rather than post-hoc reasoning extraction, enabling real-time course correction during inference
vs alternatives: Provides more reliable multi-hop reasoning than GPT-4 Turbo (which uses basic CoT) and comparable to o1 but with lower latency (5-10x faster) by avoiding exhaustive search, making it practical for interactive applications
Adapts the base GPT-5.4 Pro model to custom domains or tasks using parameter-efficient fine-tuning techniques (LoRA, prefix tuning) that update only a small percentage of model parameters. Accepts training datasets in JSONL format and produces a fine-tuned model variant that can be deployed via the standard API. Supports supervised fine-tuning for instruction-following and reinforcement learning from human feedback (RLHF) for preference optimization. Includes automatic hyperparameter tuning and validation set evaluation.
Unique: Parameter-efficient fine-tuning using LoRA and prefix tuning integrated into the unified GPT-5.4 architecture, enabling rapid domain adaptation with minimal training data and cost, without requiring full model retraining
vs alternatives: More efficient than full fine-tuning (reduces trainable parameters by 99%) and faster than prompt engineering for consistent domain adaptation; comparable to Claude's fine-tuning but with lower training costs and faster convergence
Generates images from natural language descriptions using a diffusion-based architecture integrated with the GPT-5.4 text understanding pipeline. The model accepts detailed textual prompts and produces high-fidelity images by mapping semantic concepts from language to visual features through a learned cross-modal embedding space. Supports iterative refinement where users can request modifications (e.g., 'make the sky more dramatic') and the model regenerates with context from previous generations, enabling conversational image creation.
Unique: Integrates diffusion-based image generation with GPT-5.4's semantic understanding to enable conversational refinement where the model maintains context across multiple generation requests, allowing users to iteratively modify images through natural language without resetting state
vs alternatives: Outperforms DALL-E 3 on semantic fidelity and iterative refinement by leveraging GPT-5.4's superior language understanding; faster than Midjourney (15-30s vs 60-120s) but with lower artistic control than specialized tools like Stable Diffusion with LoRA fine-tuning
Generates and completes code by accepting the full context of a developer's codebase (imports, class definitions, function signatures, style conventions) and producing code that adheres to existing patterns and architecture. The model uses the 922K token context window to ingest entire modules or projects, enabling it to generate code that respects naming conventions, dependency structures, and architectural patterns without explicit instructions. Supports multiple languages (Python, JavaScript, Go, Rust, etc.) with language-specific optimizations for syntax and idioms.
Unique: Leverages 922K token context window to ingest entire codebase modules and architectural patterns, enabling generation that respects project-specific conventions without requiring explicit style guides or fine-tuning, unlike Copilot which relies on local file context only
vs alternatives: Generates more architecturally-consistent code than GitHub Copilot (which lacks full-codebase context) and faster than Claude 3.5 Sonnet for large codebases by using optimized sparse attention for code-specific patterns
Enables the model to invoke external tools and APIs by accepting a schema definition of available functions and returning structured function calls with arguments. The model parses the schema, determines which functions are relevant to the user's request, and generates properly-formatted function calls with validated arguments. Supports chaining multiple function calls in a single response and handles error recovery when function execution fails. Integrates with OpenAI's native function-calling API and supports custom tool registries via JSON schema.
Unique: Native schema-based function calling integrated into the unified GPT-5.4 architecture, enabling deterministic tool invocation with built-in validation and error recovery, rather than post-hoc parsing of model outputs like older approaches
vs alternatives: More reliable than Claude's tool_use (which requires custom parsing) and comparable to Anthropic's native tool calling but with superior multi-step reasoning for complex orchestration workflows
Accepts external document collections and retrieves relevant passages to augment the model's responses, enabling it to answer questions grounded in specific documents or knowledge bases. The model uses semantic similarity matching to identify relevant context from a vector database or document store, then incorporates retrieved passages into the prompt to generate factually-grounded answers. Supports hybrid search combining semantic and keyword matching, and can cite sources by returning document references alongside answers.
Unique: Integrates RAG as a first-class capability within the unified GPT-5.4 architecture, allowing seamless switching between retrieval-augmented and long-context modes, enabling developers to choose between extended context (922K tokens) or external retrieval based on use case
vs alternatives: More flexible than Anthropic's native RAG (which lacks long-context fallback) and faster than LangChain-based RAG pipelines by eliminating orchestration overhead through native integration
Analyzes text inputs and outputs for harmful content (hate speech, violence, sexual content, etc.) and applies configurable filtering policies before processing or returning responses. The model uses learned classifiers trained on safety datasets to detect problematic content with configurable sensitivity levels. Supports custom policy definitions where organizations can specify which content categories to block, allow, or flag for review. Returns moderation metadata (confidence scores, detected categories) for transparency and auditing.
Unique: Integrates configurable safety policies directly into the model inference pipeline rather than as a post-processing step, enabling real-time policy enforcement with minimal latency and support for custom per-tenant policies in multi-tenant systems
vs alternatives: More flexible than OpenAI's standard moderation API (which uses fixed policies) and faster than external moderation services by eliminating network round-trips; comparable to Perspective API but with tighter integration and lower latency
+3 more capabilities
Stable Diffusion utilizes a latent diffusion model to generate high-quality images from textual descriptions. It first encodes the input text into a latent space using a transformer architecture, then progressively refines a random noise image into a coherent image that matches the text prompt through a series of denoising steps. This approach allows for fine control over the image generation process, enabling diverse outputs from the same input prompt.
Unique: Stable Diffusion's use of a latent space for image generation allows for faster and more memory-efficient processing compared to pixel-space models, enabling the generation of high-resolution images without the need for extensive computational resources.
vs alternatives: More efficient than DALL-E for generating high-resolution images due to its latent diffusion approach, which reduces memory usage and speeds up the generation process.
Stable Diffusion supports image inpainting, which allows users to modify existing images by specifying areas to be altered and providing a new text prompt. This capability leverages the model's understanding of context and content to seamlessly blend the new elements into the original image, maintaining visual coherence. It uses masked regions in the image to guide the generation process, ensuring that the output respects the surrounding context.
Unique: The inpainting feature is integrated into the same diffusion process as the text-to-image generation, allowing for a unified model that can handle both tasks without needing separate architectures.
vs alternatives: More flexible than traditional inpainting tools because it can generate entirely new content based on textual prompts rather than relying solely on existing image data.
Stable Diffusion can perform style transfer by applying the artistic style of one image to the content of another. This is achieved by encoding both the content and style images into the latent space and then blending them according to user-defined parameters. The model then reconstructs an image that retains the content of the original while adopting the stylistic features of the reference image, allowing for creative reinterpretations of existing works.
Stable Diffusion scores higher at 39/100 vs OpenAI: GPT-5.4 Pro at 23/100.
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Unique: The integration of style transfer within the same diffusion framework allows for a more coherent blending of content and style, producing results that are often more visually appealing than those generated by traditional methods.
vs alternatives: Delivers more nuanced and higher-quality style transfers compared to older methods like neural style transfer, which often produce artifacts or loss of detail.
Stable Diffusion allows users to fine-tune the model on custom datasets, enabling the generation of images that reflect specific styles or themes. This process involves training the model on additional data while preserving the learned weights from the pre-trained model, allowing for rapid adaptation to new domains. Users can specify training parameters and monitor performance metrics to ensure the model meets their requirements.
Unique: The ability to fine-tune on custom datasets while leveraging the pre-trained model's knowledge allows for quicker adaptation and better performance on specific tasks compared to training from scratch.
vs alternatives: More accessible for users with limited data compared to other models that require extensive retraining from the ground up.