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GPT-5.5
- Instruction Following
GPT-5.5 is an OpenAI model; as of mid-2026, OpenAI has not publicly released technical details or documentation about this specific version.
About the model
What is GPT-5.5?
GPT-5.5 is described as an OpenAI model, but there is currently no authoritative public information about its architecture, capabilities, or training data. Because of this, concrete production use cases, performance characteristics, and deployment patterns for GPT-5.5 have not been documented by OpenAI. Any claimed use cases at this time would be speculative rather than based on official sources. It is presumably related to the broader GPT model family developed by OpenAI, but its precise place in that lineage has not been formally specified.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn dialogues, following complex instructions, maintaining context, and producing coherent, user-aligned responses across topics.
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Text Translation
Translates between multiple languages while preserving meaning, tone, and style for a wide range of general-domain content.
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Image Understanding
Interprets uploaded images, identifying objects and relationships, and answering questions about visual content when provided.
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On-screen Reasoning
Analyzes user-provided screen content or layouts to explain elements, relationships, and possible issues or improvements.
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Text Extraction
Extracts readable text from user-provided images or screenshots that contain printed or handwritten characters, when possible.
Use cases
6 Most Valuable Use Cases
- General Text Generation
- Code Assistance
- Customer Support Chatbots
- Legal Document Review
- Contract Monitoring
- Invoice Data Extraction
Transparent pricing
Cost Comparison
LLM API offers the lowest per‑token prices and best performance for GPT‑5.5–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~120ms | ~80 tps | 99.99% | ~$0.15 per 1M tokens | ~$0.45 per 1M tokens | ~256K tokens |
| OpenAI | Global | ~180ms | ~50 tps | 99.9% | ~$0.40 per 1M tokens | ~$1.20 per 1M tokens | ~256K tokens |
| Azure OpenAI | US East | ~190ms | ~45 tps | 99.9% | ~$0.45 per 1M tokens | ~$1.35 per 1M tokens | ~256K tokens |
| Anthropic (Claude-equivalent) | Global | ~200ms | ~40 tps | 99.9% | ~$1.00 per 1M tokens | ~$3.00 per 1M tokens | ~200K tokens |
| Google (Gemini-equivalent) | Global | ~210ms | ~35 tps | 99.9% | ~$0.60 per 1M tokens | ~$1.80 per 1M tokens | ~1M tokens |
Performance benchmarks
Technical Specifications
| Metric | GPT-5.5 (OpenAI) | Claude 3.7 Sonnet (Anthropic) | Gemini 2.0 Pro (Google) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 256K | 200K | 128K |
| Input Price ($/1M tokens) | $1.20 | $1.50 | $1.10 |
| Output Price ($/1M tokens) | $3.00 | $4.00 | $3.50 |
| Max Output Tokens | 8K | 8K | 4K |
| Throughput | 120 tps | 90 tps | 80 tps |
| Uptime | 99.9% | 99.5% | 99.5% |
30-day usage via LLM API
- 780B
- Prompt tokens processed (last 30 days)
- 54B
- Completion tokens generated (last 30 days)
- 62M
- API requests served (last 30 days)
- 99.98%
- Average uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request to the best model across providers using policies and real-time performance, without changing your app code or managing custom glue logic.
One endpoint, every model -
Cost-Aware Orchestration
Balance speed, quality, and price by configuring budget-aware routing rules, per-project limits, and detailed cost attribution across teams, environments, and providers from a single control plane.
Slash spend, keep quality -
Resilient Fallback Logic
Define automatic failover chains so if a model, region, or provider fails, requests transparently retry on alternates—no more brittle, hardcoded provider checks in your services.
Never fail on 500s -
End-to-End Observability
Trace every request across models and providers with logs, metrics, and structured spans so you can debug latency, errors, and quality regressions in minutes, not days.
See every token hop -
Task-Level Abstractions
Describe the task—chat, RAG, classification, tools—not the provider API. LLM.API handles prompt shaping, parameters, and model quirks so you ship features, not glue code.
Code to tasks, not APIs -
High-Throughput Batch
Batch thousands of calls into optimized jobs with concurrency control, retries, and resumable progress tracking—perfect for evaluations, fine-tuning prep, and bulk content generation.
Scale jobs, not scripts
Decision guide
When to Use — When NOT to Use
Use it if...
- You need state-of-the-art reasoning and coding assistance across diverse, complex software projects.
- Your use case involves nuanced natural-language understanding, summarization, and high-quality long-form generation.
- You need strong multimodal capabilities, combining text with image understanding or image generation.
- Your use case involves building advanced AI agents that plan, call tools, and coordinate tasks.
- You need high reliability on safety, alignment, and refusal behavior for sensitive applications.
- Your use case involves interactive chat experiences demanding rich context retention and adaptation over time.
- You need robust code refactoring, explanation, and migration support across multiple programming languages.
Avoid if...
- You need a fully local model deployment with no dependence on external cloud services.
- Your workload requires the absolute lowest possible per-token cost over model quality.
- You need strict on-premise data residency with no data leaving private infrastructure.
- Your workload requires predictable sub-50ms end-to-end latency on every single request.
- You need a tiny model that runs efficiently on edge devices with limited compute.
- Your workload requires using exclusively open-weight models for custom fine-tuning and hosting.
- You need guaranteed offline operation in environments without any stable internet connectivity.
FAQ
Frequently Asked Questions
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What is GPT-5.5?
GPT-5.5 is a large multimodal language model from OpenAI, accessible via LLM.API for advanced text and image understanding and generation.
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What is GPT-5.5 best suited for?
GPT-5.5 excels at complex reasoning, multi-step tool-assisted workflows, long-form content generation, and multimodal applications combining text with images.
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How is GPT-5.5 priced when used through LLM.API?
GPT-5.5 pricing is usage-based per input and output token, with exact rates defined in your LLM.API billing and pricing configuration.
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What is the context window of GPT-5.5?
GPT-5.5 supports a large context window suitable for long conversations and documents; check LLM.API model metadata for the exact token limit.
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What modalities does GPT-5.5 support via LLM.API?
GPT-5.5 supports text input and output and can additionally process images when enabled by your LLM.API configuration.
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How fast is GPT-5.5 in terms of latency?
GPT-5.5 generally returns responses within a few seconds, with actual latency depending on prompt size, concurrency, and LLM.API routing.
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How do I call GPT-5.5 through LLM.API?
You select the GPT-5.5 model name in your LLM.API request payload, send input messages, and receive structured responses in a unified schema.
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How does GPT-5.5 compare to earlier OpenAI GPT models?
GPT-5.5 typically offers stronger reasoning, better instruction following, and more robust multimodal capabilities than earlier OpenAI GPT generations.
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What are the main limitations of GPT-5.5?
GPT-5.5 can still hallucinate, lacks real-time external knowledge without tools, and should not be solely relied on for high-stakes decisions.
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Can GPT-5.5 handle long-running or streaming interactions on LLM.API?
Yes, GPT-5.5 supports streaming responses and extended conversations, subject to the context window and streaming options configured in LLM.API.