GPT-5 Nano
Up to 30%Call GPT-5 Nano through LLM.API when you want OpenAI-family text generation with unified auth, provider choice, and production-friendly defaults.
What is GPT-5 Nano?
On LLM.API, GPT-5 Nano (gpt-5-nano) serves as a OpenAI conversational model for products that need 400K tokens context windows and predictable token pricing. Ultra-efficient GPT-5 variant for high-volume applications with reasoning support.
Providers
LLM.API routes GPT-5 Nano to the providers below, with discounted effective rates versus list price.
List price by provider ($ / 1M tokens)
InputOutputProvider list prices; the LLM.API discount applies on top.
| Provider | Pricing | Context | Capabilities |
|---|---|---|---|
| in $50; out $400 per 1M tokens | 400K tokens | vision, tools, streaming, reasoning, web search, JSON, structured | |
| in $50; out $400 per 1M tokens | 400K tokens | vision, tools, streaming, reasoning, web search, JSON, structured |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test GPT-5 Nano right here — free to start.
Suggestions for your first prompt
Code snippet
Call GPT-5 Nano through the OpenAI-compatible API — POST /v1/chat/completions.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_LLMAP_KEY",
base_url="https://api.llmapi.ai/v1",
)
resp = client.chat.completions.create(
model="gpt-5-nano",
messages=[
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."},
],
)
print(resp.choices[0].message.content){
"model": "gpt-5-nano",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}5 Core Capabilities
Structured outputs
Can produce JSON-friendly or schema-oriented responses when prompted carefully. Grounded in the model's chat role rather than generic chat claims.
Conversational UX
Maintains coherent multi-turn assistant behavior for product chat surfaces. Reflects OpenAI positioning for this endpoint.
Code assistance
Helps write, explain, refactor, and debug application code across common languages.
Multilingual drafting
Drafts and translates professional content across major business languages. Tuned to how teams typically call GPT-5 Nano.
Tool-ready dialogue
Works well in agent loops that call functions, browsers, or retrieval APIs. Grounded in the model's chat role rather than generic chat claims.
6 Most Valuable Use Cases
- Policy Q&A bots with careful refusal behavior with GPT-5 Nano
- Multilingual localization drafts for UX copy
- Customer support copilots that draft accurate, on-brand replies with GPT-5 Nano
- Product analytics narration and anomaly explanations
- Sales and success email drafting with CRM context with GPT-5 Nano
- Meeting-note cleanup and action-item generation
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent AI Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, every model. -
Cost-Aware Execution
Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend. -
Resilient Fallback Flows
Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users. -
Deep LLM Observability
Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere. -
Task-Level Orchestration
Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models. -
High-Throughput Batch APIs
Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.
Why run GPT-5 Nano on LLM.API?
Unified AI Routing
Reach GPT-5 Nano and sibling models through one OpenAI-compatible endpoint.
Cost Control
Production: Compare provider price points and keep spend visible as you scale GPT-5 Nano.
Reliability Layer
Practical: Retry and route across configured providers when a single upstream blips.
Observability
Trace prompts, tokens, and errors for GPT-5 Nano alongside the rest of your stack.
Drop-in SDKs
Practical: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Swap GPT-5 Nano for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- You need a general-purpose text model for assistants, agents, or content workflows (GPT-5 Nano)
- You want OpenAI-compatible chat completions through a single LLM.API key (GPT-5 Nano)
- You need provider failover options exposed for this model id (GPT-5 Nano)
- Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (GPT-5 Nano)
Avoid if...
- Your use case depends on unpublished proprietary benchmarks not listed here
- You need guaranteed real-time hard latency SLAs without benchmarking the provider
- You require on-prem only deployment with no cloud inference
- You need pure embedding, OCR, or media generation instead of chat
BENCHMARKS
GPT-5 Nano benchmark scores
Intelligence index
Scale: 0-100 index points
Output speed
Scale: 0-400 tokens per second
Reference price per 1M tokens
Bars compare input and output list prices for this model.
Independent scores published by Artificial Analysis (source). Reference prices are provider list prices, not LLM.API pricing.
COMMUNITY
What developers say about OpenAI models
Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to GPT-5 Nano.
- Long-horizon coding reports on GPT-6 Astra describe a clear step up over the GPT-5.x line, with the higher reasoning tiers seen as the sweet spot for planning and implementation in large (100K+ LOC) codebases.
- The same field reports note that fast/ultra modes burn quota quickly and that very large refactors still stall, so teams tend to mix a cheap tier for routine calls with a reasoning tier for hard steps.
- Community threads temper the hype: capability gains are acknowledged, but developers still report the usual failure modes on obscure reverse-engineering and modding work.
SOURCES
Frequently Asked Questions
When should I choose GPT-5 Nano?
You want OpenAI-compatible chat completions through a single LLM.API key — especially when you specifically need GPT-5 Nano.
Where is the canonical page for GPT-5 Nano?
https://llmapi.ai/models/openai-gpt-5-nano/
What modalities does GPT-5 Nano support?
GPT-5 Nano accepts text, image and produces text according to its architecture metadata on LLM.API.
Can I use tools or structured outputs with GPT-5 Nano?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
Is GPT-5 Nano a chat model?
Yes—GPT-5 Nano is exposed as a chat/completions-style endpoint on LLM.API.
What are limitations of GPT-5 Nano?
Like other API models, GPT-5 Nano can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.
How is GPT-5 Nano priced on LLM.API?
Listed pricing metadata shows: In $0.05 / 1M tokens · Out $0.4 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.
Does GPT-5 Nano support streaming?
Yes—at least one listed provider advertises streaming.
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