- Text Generation
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GPT-5.2
- Text Generation
GPT-5.2 is an OpenAI large language model in the GPT-5 family, designed for advanced natural language understanding and generation across many tasks. It emphasizes improved reasoning, safety, and versatility compared with earlier GPT models.
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?
GPT-5.2 is a generative pre-trained transformer model from OpenAI for interpreting instructions and producing human-like text. It is mainly used for tasks such as drafting and editing content, answering questions, and assisting with coding or data analysis workflows. It is also applied in building conversational agents, research assistants, and domain-specific tools that require reliable language understanding and reasoning. GPT-5.2 follows earlier models in OpenAI’s GPT series, extending the capabilities introduced by GPT-4-class and GPT-5-class systems.
Model capabilities
5 Core Capabilities
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Conversational Chat
Engages in multi-turn dialogue, following complex instructions, maintaining context, and producing coherent, relevant responses across many topics.
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Text Translation
Translates between multiple languages, preserving meaning and tone while adapting to regional expressions and domain-specific terminology.
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Visual Understanding
Interprets images to identify objects, scenes, and relationships, supporting tasks like description, comparison, and visual question answering.
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Screen Interpretation
Understands and reasons about screen content such as interfaces, layouts, and structured documents to assist with navigation and analysis.
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Document OCR
Extracts and structures text from images or scanned documents, enabling search, editing, and analysis of visual text content.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice And Receipt Parsing
- Legal Case Search
- Regulatory Case Monitoring
- E-commerce Product Recommendations
- Code Generation And Review
Transparent pricing
Cost Comparison
Save up to ~70% vs major GPT-5.2-compatible APIs with LLM API
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.50 | $1.50 | 256K tokens |
| OpenAI | Global | ~220ms | ~40 tps | 99.9% | ~$1.80 | ~$5.40 | 200K tokens |
| Azure OpenAI | US East | ~250ms | ~35 tps | 99.9% | ~$2.00 | ~$6.00 | 200K tokens |
| Google Cloud (Gemini-equivalent) | US Central | ~260ms | ~30 tps | 99.9% | ~$1.60 | ~$4.80 | 128K tokens |
| Anthropic (Claude-equivalent) | US West | ~240ms | ~32 tps | 99.9% | ~$1.70 | ~$5.10 | 200K tokens |
Performance benchmarks
Technical Specifications
| Metric | GPT-5.2 | Claude 3.7 Opus | Gemini 2.0 Ultra |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~230ms |
| Context Window | 256K | 200K | 128K |
| Input Price ($/1M) | $1.80 | $3.00 | $2.50 |
| Output Price ($/1M) | $5.00 | $15.00 | $7.50 |
| Max Output Tokens | 8K | 8K | 4K |
| Throughput | 120 tps | 80 tps | 90 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)
- 210M
- API requests served (last 30 days)
- 99.95%
- Average uptime over 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 across providers and models based on latency, cost, or performance—without changing your integration. Optimize behavior in code, not configs.
One endpoint, every model -
Cost-Aware Orchestration
Control spend with smart model selection, rate limits, and cost ceilings per project. See and tune tradeoffs between price and quality in real time.
Max performance, minimal spend -
Automatic Provider Fallback
When a model or provider fails, LLM.API transparently retries or reroutes to healthy alternatives—no manual failover logic, no downtime for your users.
Resilience by default -
Deep LLM Observability
Capture traces, logs, metrics, and payloads for every call across providers. Debug prompts, compare models, and ship reliable AI features with production-grade visibility.
See every token -
Task-Level Abstractions
Describe tasks—chat, RAG, tools, structured outputs—once and let LLM.API pick the right models and parameters. Evolve your stack without rewriting application code.
Think in tasks, not models -
High-Throughput Batch
Submit large batches of prompts for offline or async processing with built-in deduping, retries, and cost controls. Scale evaluations, content generation, and backfills easily.
Millions of calls, one pipeline
Decision guide
When to Use — When NOT to Use
Use it if...
- You need state-of-the-art reasoning and general-purpose intelligence across diverse complex tasks.
- You need strong performance on code generation, debugging, and multi-file software refactoring.
- You need high-quality natural language understanding and generation for chatbots and agents.
- Your use case involves complex data analysis, synthesis, and explanation for non-experts.
- Your use case involves multi-step tool usage and orchestration within larger AI systems.
- You need a single versatile model for text, reasoning, and light code workloads.
Avoid if...
- You need the absolute lowest-cost model for simple, repetitive, template-based outputs.
- Your workload requires ultra-low latency responses on edge devices with limited compute.
- You need strict on-prem deployment with no external API dependencies or connectivity.
- Your workload requires only basic text classification where smaller models perform similarly.
- You need deterministic, fully auditable rule-based behavior instead of probabilistic generation.
- Your workload requires heavy multimedia generation better served by specialized vision or audio models.
FAQ
Frequently Asked Questions
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What is GPT-5.2?
GPT-5.2 is a large multimodal OpenAI model accessible via LLM.API, designed for advanced reasoning, coding, and content generation across text and image inputs.
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What modalities does GPT-5.2 support through LLM.API?
GPT-5.2 supports text input and output, and can optionally process image inputs when invoked with the appropriate LLM.API parameters.
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How is GPT-5.2 priced when used via LLM.API?
LLM.API meters GPT-5.2 usage based on tokens processed, with per-input and per-output token rates defined in LLM.API’s pricing documentation.
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What is the context window of GPT-5.2?
GPT-5.2 supports a large-context window suitable for long conversations and multi-file prompts; check LLM.API docs for the exact current token limit.
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How fast is GPT-5.2 in terms of latency and throughput?
GPT-5.2 is optimized for low latency and streaming responses, but actual speed depends on prompt size and concurrent load on LLM.API.
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How do I call GPT-5.2 via LLM.API?
You select the GPT-5.2 model name in your LLM.API request payload, include your LLM.API key, then send standard chat or completion-style requests.
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What is GPT-5.2 particularly good at?
GPT-5.2 excels at complex multi-step reasoning, code generation and refactoring, long-form writing, and following detailed instructions across domains.
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How does GPT-5.2 compare to earlier OpenAI models like GPT-4.1?
GPT-5.2 generally offers stronger reasoning, better instruction following, and more robust handling of long context than GPT-4.1 at comparable usage patterns.
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What limitations should I be aware of when using GPT-5.2?
GPT-5.2 can still hallucinate facts, misinterpret ambiguous instructions, and should not be used as the sole source for high-stakes decisions.
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Can I fine-tune GPT-5.2 through LLM.API?
Fine-tuning support for GPT-5.2 depends on LLM.API’s current feature set; check their documentation for whether fine-tuning is enabled for this model.
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