- Instruction Following
Ling-2.6-flash is an open-weight, high-efficiency instruct language model from inclusionAI, optimized for fast responses, strong execution, and low token usage in real-world agent workflows.
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GPT-5.2 Pro
- Instruction Following
GPT-5.2 Pro is an OpenAI frontier large language model optimized for strong general reasoning, coding, and multimodal assistant use in demanding, real-world applications.
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 Pro?
GPT-5.2 Pro is an advanced OpenAI language model designed to provide high-quality natural language and code generation for complex tasks. It is primarily used for building robust AI assistants, handling sophisticated workflows, and serving as a core reasoning engine in products and tools. It also supports knowledge work such as analysis, drafting, and data transformation across a wide range of domains. GPT-5.2 Pro follows and extends earlier GPT-series models from OpenAI, offering improved capabilities and reliability over its predecessors.
Model capabilities
5 Core Capabilities
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Advanced Chatting
Engages in extended, context-aware conversations, following complex instructions and maintaining consistent tone, style, and persona over time.
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Image Understanding
Interprets uploaded images to identify objects, scenes, relationships, and visual details, supporting explanation, comparison, and reasoning tasks.
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Document OCR
Extracts structured text from images or scanned documents, enabling downstream search, analysis, and transformation of previously non-digital content.
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Language Translation
Translates text between multiple languages, preserving meaning and tone while adapting to context-specific terminology and domain conventions.
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Content Monitoring
Analyzes text or image content for safety, policy compliance, and categorization, supporting moderation and automated quality checks.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Invoice and Receipt Parsing
- Legal Case Research Assistance
- Regulatory Change Monitoring
- E-commerce Product Recommendations
- Code Generation and Review
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for GPT-5.2 Pro–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~110ms | ~70 tps | ~99.99% | ~$0.35 | ~$1.00 | ~256K |
| OpenAI | Global | ~180ms | ~45 tps | ~99.9% | ~$0.60 | ~$1.80 | ~200K |
| Azure OpenAI | US East | ~190ms | ~40 tps | ~99.9% | ~$0.65 | ~$1.90 | ~200K |
| Anthropic (Claude equivalent tier) | US West | ~200ms | ~35 tps | ~99.9% | ~$0.70 | ~$2.10 | ~200K |
Performance benchmarks
Technical Specifications
| Metric | GPT-5.2 Pro (OpenAI) | Claude 3.7 Opus (Anthropic) | Gemini 2.0 Ultra (Google) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~240ms |
| Context Window | 256K | 200K | 128K |
| Input Price ($/1M tokens) | $2.00 | $3.00 | $2.50 |
| Output Price ($/1M tokens) | $6.00 | $15.00 | $7.50 |
| Max Output Tokens | 8K | 8K | 4K |
| Throughput | 120 tps | 80 tps | 90 tps |
| Uptime | 99.9% | 99.5% | 99.5% |
30-day usage via LLM API
- 1.8T
- Prompt tokens processed (last 30 days)
- 320B
- Completion tokens generated (last 30 days)
- 42M
- 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 requests across providers and models based on latency, cost, or quality. One endpoint, pluggable strategies, no app rewrites.
One endpoint, any model -
Cost-Aware Control
Set hard budgets, price caps, and tiered policies per workspace or feature. Automatically choose cheaper equivalents without touching application logic.
Spend less per token -
Resilient Fallbacks
Define provider and model fallback chains that trigger on errors, timeouts, or quotas. Keep production workloads up, even when vendors go down.
Failover built in -
Deep Observability
Trace every request across providers with latency, cost, and token metrics. Debug slow or failing calls using structured logs and full payload history.
See every token -
Task-Level Abstractions
Describe tasks—chat, classify, extract, generate—and let LLM.API pick optimal models and prompts. Standardize behavior without scattering prompt logic.
Code to tasks, not models -
High-Throughput Batch
Submit massive batch jobs across providers with concurrency, retries, and partial-failure handling. Process millions of calls efficiently via one consistent API.
Scale workloads effortlessly
Decision guide
When to Use — When NOT to Use
Use it if...
- You need very strong general-purpose reasoning, coding, and writing in a single model.
- You need high-quality multi-step reasoning for complex data analysis or decision support.
- You need advanced code generation, debugging, and refactoring across multiple programming languages and frameworks.
- Your use case involves nuanced natural language understanding for chatbots, agents, or copilots.
- Your use case involves handling long, mixed-format contexts like documents, tables, and snippets.
- You need strong instruction-following behavior for tools, APIs, or workflow orchestration.
- Your use case involves prototyping cutting-edge AI features where quality matters more than cost.
Avoid if...
- You need the absolute lowest possible cost per token for massive-scale workloads.
- Your workload requires extremely low latency responses on constrained or mobile-edge environments.
- You need strict on-prem or air-gapped deployment without relying on cloud APIs.
- Your workload requires a tiny, specialized model that can be heavily fine-tuned.
- You need deterministic, fully auditable classical algorithms instead of probabilistic language model behavior.
- You need only simple pattern matching or keyword search that rule-based systems handle better.
- Your workload requires hard real-time guarantees where occasional latency spikes are unacceptable.
FAQ
Frequently Asked Questions
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What is GPT-5.2 Pro?
GPT-5.2 Pro is a flagship OpenAI large language model on LLM.API, optimized for high-quality reasoning, code generation, and complex multi-step tasks.
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What is GPT-5.2 Pro best suited for?
GPT-5.2 Pro excels at complex reasoning, multi-file codebases, data analysis, long-form content generation, and multi-step tooling workflows in production applications.
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What is the context window of GPT-5.2 Pro?
GPT-5.2 Pro supports up to a 128,000-token context window, enabling very long conversations and large document processing.
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Which modalities does GPT-5.2 Pro support via LLM.API?
GPT-5.2 Pro supports text input and output, with optional image input and structured tool-calling when enabled in the LLM.API request.
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How fast is GPT-5.2 Pro in terms of latency?
Typical end-to-end latency for GPT-5.2 Pro is a few seconds for short prompts, increasing with longer context and higher max_tokens settings.
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How is GPT-5.2 Pro priced when called through LLM.API?
GPT-5.2 Pro pricing on LLM.API is per-token for input and output, and may differ from OpenAI list prices depending on your LLM.API plan.
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How do I call GPT-5.2 Pro through the LLM.API gateway?
Specify the provider as OpenAI and the model name as gpt-5.2-pro in your LLM.API request, plus your LLM.API key and desired parameters.
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How does GPT-5.2 Pro compare to cheaper OpenAI-compatible models?
GPT-5.2 Pro usually offers better reasoning, coding, and reliability than cheaper models, at a higher per-token cost and slightly higher latency.
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Does GPT-5.2 Pro have any notable limitations?
GPT-5.2 Pro can still hallucinate, lacks real-time internet access by default, and should not be used as the sole source for high-stakes decisions.
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Can I fine-tune GPT-5.2 Pro via LLM.API?
GPT-5.2 Pro itself is not fine-tunable through LLM.API, but you can layer retrieval, system prompts, and tools to specialize behavior.
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