Amazon Nova Micro
Up to 30%Call Amazon Nova Micro through LLM.API when you want Amazon-family text generation with unified auth, provider choice, and production-friendly defaults.
What is Amazon Nova Micro?
Amazon Nova Micro is an Amazon chat model exposed on LLM.API under id `nova-micro`. nova-micro provided by aws-bedrock. It is wired for API access through LLM.API with OpenAI-compatible patterns. Teams use it when they need reliable text generation with text inputs and text outputs over an OpenAI-compatible API.
Providers
LLM.API routes Amazon Nova Micro 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 |
|---|---|---|---|
 aws-bedrock30% off | in $35; out $140 per 1M tokens | 128K tokens | tools, streaming, JSON, structured |
Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.
Try this model
Test Amazon Nova Micro right here — free to start.
Suggestions for your first prompt
Code snippet
Call Amazon Nova Micro 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="nova-micro",
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": "nova-micro",
"messages": [
{"role": "system", "content": "You are a precise product assistant."},
{"role": "user", "content": "Give me three crisp launch checklist items."}
]
}5 Core Capabilities
Long-context synthesis
Summarizes and cross-references information across large prompts when context allows. Relevant for `nova-micro` workloads on LLM.API.
Analytical writing
Produces clear analyses, comparisons, and decision memos from messy source material. Tuned to how teams typically call Amazon Nova Micro.
Conversational UX
Maintains coherent multi-turn assistant behavior for product chat surfaces.
Code assistance
Helps write, explain, refactor, and debug application code across common languages.
Multi-step reasoning
Breaks down complex problems into intermediate steps before answering. Tuned to how teams typically call Amazon Nova Micro.
6 Most Valuable Use Cases
- Policy Q&A bots with careful refusal behavior with Amazon Nova Micro
- Data extraction into JSON for downstream systems
- Meeting-note cleanup and action-item generation with Amazon Nova Micro
- Internal knowledge assistants grounded with your retrieval layer
- Research synthesis across long documents and tickets with Amazon Nova Micro
- Coding agents for refactors, tests, and PR explanations
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 Amazon Nova Micro on LLM.API?
Unified AI Routing
Practical: Reach Amazon Nova Micro and sibling models through one OpenAI-compatible endpoint.
Cost Control
Compare provider price points and keep spend visible as you scale Amazon Nova Micro.
Reliability Layer
Practical: Retry and route across configured providers when a single upstream blips.
Observability
Production: Trace prompts, tokens, and errors for Amazon Nova Micro alongside the rest of your stack.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Model Breadth
Production: Swap Amazon Nova Micro for chat, media, or embedding alternatives without rewriting auth.
When to Use — When NOT to Use
Use it if...
- Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Amazon Nova Micro)
- You need a general-purpose text model for assistants, agents, or content workflows (Amazon Nova Micro)
- You need provider failover options exposed for this model id (Amazon Nova Micro)
- You want OpenAI-compatible chat completions through a single LLM.API key (Amazon Nova Micro)
Avoid if...
- You require on-prem only deployment with no cloud inference
- You need guaranteed real-time hard latency SLAs without benchmarking the provider
- Your use case depends on unpublished proprietary benchmarks not listed here
- You need pure embedding, OCR, or media generation instead of chat
BENCHMARKS
Amazon Nova Micro 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 Amazon Nova 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 Amazon Nova Micro.
- Reviews consistently call Nova cheap and underrated — best-in-class pricing with tight AWS/Bedrock integration.
- Amazon's own positioning, echoed by commentators, is that benchmark position matters less than cost and deployment fit.
- Developers pick Nova for high-volume, cost-sensitive workloads already running inside AWS.
SOURCES
Frequently Asked Questions
What is Amazon Nova Micro?
nova-micro provided by aws-bedrock. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `nova-micro`.
Can I use tools or structured outputs with Amazon Nova Micro?
Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.
Which providers serve Amazon Nova Micro?
LLM.API currently lists: aws-bedrock. Availability can vary by region and account.
What are limitations of Amazon Nova Micro?
Like other API models, Amazon Nova Micro 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 Amazon Nova Micro priced on LLM.API?
Listed pricing metadata shows: In $0.035 / 1M tokens · Out $0.14 / 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.
When should I choose Amazon Nova Micro?
You need provider failover options exposed for this model id — especially when you specifically need Amazon Nova Micro.
Does Amazon Nova Micro support streaming?
Yes—at least one listed provider advertises streaming.
Is Amazon Nova Micro a chat model?
Yes—Amazon Nova Micro is exposed as a chat/completions-style endpoint on LLM.API.
What is the context length for Amazon Nova Micro?
Reported context for Amazon Nova Micro is 128K tokens. Always verify the active provider row if multiple providers are listed.
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