- Instruction Following
GPT-5.5 Pro is an OpenAI model name that has been mentioned publicly but has not been formally documented or specified by OpenAI as of now. Reliable…
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GPT-5.2 Chat
- Instruction Following
GPT-5.2 Chat is an OpenAI conversational language model designed for interactive dialogue and task assistance. It focuses on providing coherent, context-aware responses across a wide range of topics.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is GPT-5.2 Chat?
GPT-5.2 Chat is a conversational AI model from OpenAI optimized for multi-turn dialogue and natural language understanding. It is mainly used for chat-based assistance, such as answering questions, drafting and editing text, and helping users reason through complex problems. It also supports integration into applications and workflows where reliable, instruction-following dialogue is required. GPT-5.2 Chat belongs to OpenAI’s GPT family of large language models, following earlier generations such as GPT-3 and GPT-4.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn conversations, maintaining context, following instructions, and adapting tone for assistance, brainstorming, and problem-solving.
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Code Reasoning
Understands, writes, and explains code across multiple languages, assisting with debugging, refactoring, and algorithmic reasoning tasks.
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Image Understanding
Interprets images to identify objects, text, layouts, and visual relationships, supporting analysis, explanation, and content extraction.
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Text Translation
Translates between many languages, preserving meaning and style, and can clarify ambiguities or cultural nuances when needed.
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Visual Text OCR
Extracts readable text from images, including documents, screenshots, and signs, enabling search, editing, and downstream processing.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbot
- Financial Report Summaries
- Legal Document Review
- Regulatory Change Monitoring
- Marketing Content Generation
- Code Review Assistance
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for GPT-5.2–class chat workloads.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.995% | $0.25 | $0.75 | 512K |
| OpenAI | Global | ~150ms | ~80 tps | 99.9% | ~$0.60 | ~$1.80 | ~256K |
| Azure OpenAI | US East | ~170ms | ~70 tps | 99.9% | ~$0.65 | ~$1.90 | ~256K |
| Anthropic (Claude-equivalent tier) | US West | ~180ms | ~60 tps | 99.9% | ~$0.70 | ~$2.10 | ~200K |
| Google (Gemini-equivalent tier) | Global | ~190ms | ~55 tps | 99.9% | ~$0.55 | ~$1.70 | ~200K |
Performance benchmarks
Technical Specifications
| Metric | GPT-5.2 Chat (OpenAI) | Claude 3.7 Sonnet (Anthropic) | Gemini 2.0 Pro (Google) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~240ms |
| Context Window | 256K | 200K | 128K |
| Input Price ($/1M tokens) | $0.80 | $1.25 | $1.00 |
| Output Price ($/1M tokens) | $4.00 | $5.00 | $4.50 |
| Max Output Tokens | 8K | 8K | 4K |
| Throughput | 120 tps | 90 tps | 80 tps |
| Uptime | 99.95% | 99.9% | 99.9% |
30-day usage via LLM API
- 3.8T
- Prompt tokens processed (last 30 days)
- 2.4T
- Completion tokens generated (last 30 days)
- 620M
- API requests served (last 30 days)
- 99.98%
- Avg uptime across regions (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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Dynamic AI Routing
Automatically route each request to the optimal model across providers based on latency, reliability, and capabilities, so you ship faster without hardcoding vendor logic.
One endpoint, any model -
Cost-Aware Orchestration
Balance quality and price with per-call cost controls, smart tiering, and spend visibility, letting you optimize inference budgets without rewriting application code.
Maximum performance per dollar -
Resilient Fallback Flows
Define automatic failover to backup models or providers on errors, timeouts, and rate limits, keeping your AI features online even when vendors aren’t.
Built-in reliability layer -
End-to-End Observability
Get centralized logs, traces, and metrics for every model call—latency, errors, and tokens—so you can debug issues and tune performance from a single dashboard.
See every token, everywhere -
Task-Level Abstractions
Describe tasks—chat, generation, tools, scoring—once and let LLM.API handle provider-specific quirks, so you focus on product logic instead of API plumbing.
Code to tasks, not vendors -
High-Throughput Batch Jobs
Run large-scale batch inference across models and providers with concurrency, retries, and progress tracking handled for you, ideal for backfills and async workloads.
Ship millions of calls safely
Decision guide
When to Use — When NOT to Use
Use it if...
- You need strong general-purpose chat, coding, and analysis with minimal integration overhead.
- You need advanced reasoning over complex instructions, including planning, refactoring, and debugging code.
- You need high-quality natural language generation for support bots, agents, or content drafting.
- You need tight integration with other OpenAI models, tools, or function-calling ecosystems.
- Your use case involves multi-step workflows where the assistant must maintain long, coherent context.
- You need good performance on ambiguous user queries that require clarification and safe handling.
- Your use case involves mixing natural language, structured data, and light mathematical reasoning.
Avoid if...
- You need strict on-premise deployment with no external API calls or cloud dependency.
- Your workload requires ultra-low latency, sub-50ms responses for tight real-time interactivity.
- You need deterministic, auditable outputs where non-probabilistic rule-based systems are mandatory.
- Your workload requires heavy, high-frequency numerical computation better suited to traditional GPU code.
- You need guaranteed compliance with highly specialized domain regulations beyond configurable policies.
- Your workload requires fully offline operation, disconnected from the internet or external services.
- You need a tiny, device-embedded model optimized for inference on low-power hardware.
FAQ
Frequently Asked Questions
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What is GPT-5.2 Chat?
GPT-5.2 Chat is a state-of-the-art OpenAI conversational language model accessible through the LLM.API unified gateway for general-purpose and agentic applications.
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What is GPT-5.2 Chat best suited for?
GPT-5.2 Chat excels at complex multi-step reasoning, tool-using agents, high-quality coding assistance, and production chatbots requiring reliable, steerable behavior.
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What modalities does GPT-5.2 Chat support via LLM.API?
GPT-5.2 Chat supports text input and output via LLM.API; additional modalities depend on LLM.API’s configured OpenAI feature support.
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How is GPT-5.2 Chat priced on LLM.API?
GPT-5.2 Chat pricing is defined by LLM.API’s OpenAI-backed tariff; refer to your LLM.API dashboard or pricing docs for current per-token rates.
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What context window does GPT-5.2 Chat support?
GPT-5.2 Chat supports a large context window; check the LLM.API model metadata for the exact maximum tokens for your deployment.
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How fast is GPT-5.2 Chat in terms of latency?
Typical end-to-end latency depends on prompt size and LLM.API infrastructure, but GPT-5.2 Chat is optimized for responsive interactive use.
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How do I call GPT-5.2 Chat through the LLM.API?
Specify the model identifier "GPT-5.2 Chat" in your LLM.API completion or chat endpoint request, plus your prompt and any desired parameters.
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How does GPT-5.2 Chat compare to earlier GPT-4.x models?
GPT-5.2 Chat generally provides stronger reasoning, better adherence to instructions, and improved coding capabilities compared with GPT-4.x-class models.
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What limitations does GPT-5.2 Chat have?
GPT-5.2 Chat can still hallucinate, reflect outdated knowledge, and must not be solely relied on for high-stakes domains without external verification.
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Can GPT-5.2 Chat use tools or functions through LLM.API?
Yes, if LLM.API exposes a tool-calling interface, GPT-5.2 Chat can be configured to call tools or functions based on your schema.
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