- Instruction Following
MoonshotAI Kimi Latest is the most recent version of MoonshotAI’s Kimi conversational large language model, designed for fast, web-connected chat and practical assistance in Chinese and…
Powered by Amazon
Nova 2 Lite
- Instruction Following
Nova 2 Lite is an Amazon foundational language model variant designed to provide efficient, general-purpose AI capabilities with reduced computational footprint. It is intended for everyday workloads where cost-effectiveness and responsiveness are prioritized over maximum scale.
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 Nova 2 Lite?
Nova 2 Lite is an Amazon language model optimized for lighter-weight, general-purpose AI tasks. It is commonly used for chat-style assistants, summarization, and basic content generation in applications that need good quality without heavy infrastructure requirements. It is also suitable for integrating natural language understanding into customer support, internal tools, and other enterprise workflows where latency and cost are important. It belongs to the Nova 2 family of Amazon models, which includes larger variants aimed at more advanced reasoning and generation.
Model capabilities
5 Core Capabilities
-
Conversational Chat
Engages in multi-turn, context-aware conversations, answering questions, following instructions, and maintaining coherent dialogue across varied everyday topics.
-
Text Translation
Translates written text between multiple natural languages, preserving core meaning and basic tone for general, non-specialized content.
-
Visual Image Analysis
Interprets input images, recognizing objects and scenes to support simple descriptions and basic reasoning about visible content.
-
Document OCR
Extracts machine-readable text from images of documents or screenshots, enabling downstream search, summarization, and text-based processing.
-
Usage Monitoring Support
Supports integration into monitored applications and workflows, enabling evaluation of outputs for quality, safety, and performance over time.
Use cases
6 Most Valuable Use Cases
- Customer Support Chatbots
- Document Understanding Automation
- Knowledge Base Q&A
- Business Workflow Orchestration
- E-commerce Content Generation
- Code Generation and Debugging
Transparent pricing
Cost Comparison
LLM API offers the lowest costs and latency with the largest context window for Nova 2 Lite–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.05 | $0.10 | 256K |
| Amazon Bedrock | US East | ~180ms | ~60 tps | 99.9% | ~$0.15 | ~$0.45 | ~128K |
| OpenAI | Global | ~120ms | ~80 tps | 99.9% | ~$0.20 | ~$0.60 | ~128K |
| Azure AI | Global | ~140ms | ~70 tps | 99.9% | ~$0.18 | ~$0.55 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Nova 2 Lite | Claude 3 Haiku | GPT-4o mini |
|---|---|---|---|
| Avg Latency | ~220ms | ~250ms | ~230ms |
| Context Window | 200K | 200K | 128K |
| Input Price ($/1M) | $0.20 | $0.25 | $0.15 |
| Output Price ($/1M) | $0.60 | $1.25 | $0.60 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 40 tps | 35 tps | 45 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 7.8B
- Prompt tokens processed (30 days)
- 3.1B
- Completion tokens generated (30 days)
- 42M
- API requests served (30 days)
- 99.8%
- 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.
-
Unified 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 -
Optimized Cost Control
Define per-project budgets, price ceilings, and preferred providers so LLM.API continuously chooses the most cost-efficient model that still meets your performance requirements.
Lower spend, same output -
Resilient Fallback Logic
Configure automatic failover chains so requests seamlessly retry on alternative models or providers when timeouts, rate limits, or outages occur—no custom retry code required.
No more hard failures -
Deep Observability
Track latency, token usage, errors, and provider-level performance in one place with structured logs and traces wired for your monitoring stack.
See every token flow -
Task-Aware Orchestration
Describe tasks at a high level and let LLM.API choose the right models, prompts, and tools for chat, generation, retrieval, and function-calling flows.
Think tasks, not models -
High-Throughput Batch
Submit large batches of prompts through a single API call, with automatic chunking, concurrency control, and retries to safely max out provider throughput.
Scale up without throttling
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a cost-effective general-purpose model for everyday coding and content tasks.
- You need an Amazon Bedrock-native model with straightforward integration into AWS workflows.
- Your use case involves moderate-length chatbots, assistants, or customer support automations.
- Your use case involves basic code generation, refactoring, or small bug-fixing tasks.
- You need reasonable text understanding and generation without requiring cutting-edge reasoning performance.
- Your use case involves experimenting with GenAI in development or staging environments cost-sensitively.
- Your use case involves educational helpers that answer straightforward questions or definitions.
Avoid if...
- You need state-of-the-art reasoning, planning, or complex multi-step tool-using agents.
- Your workload requires best-in-class code generation or complex multi-file software refactoring.
- You need extremely long-context processing for large documents, logs, or transcripts.
- Your workload requires nuanced domain-expert responses in law, medicine, or highly technical fields.
- You need best-in-class multilingual performance across many low-resource languages and dialects.
- Your workload requires rich multimodal generation, such as complex image or video understanding.
- You need maximum output quality for critical production workloads where errors are very costly.
FAQ
Frequently Asked Questions
-
What is Nova 2 Lite?
Nova 2 Lite is an Amazon large language model designed as a lightweight, cost-efficient option for general-purpose text generation and understanding.
-
What is Nova 2 Lite best suited for?
Nova 2 Lite is best for chatbots, lightweight agents, summarization, and general NLP tasks where low cost and good-enough quality matter more than peak capability.
-
What context window does Nova 2 Lite support?
Nova 2 Lite supports a 8K token context window, suitable for moderately long conversations and documents.
-
How fast is Nova 2 Lite in terms of latency?
Nova 2 Lite is optimized for low latency, making it suitable for interactive applications where quick responses are important.
-
What modalities does Nova 2 Lite support?
Nova 2 Lite supports text input and output only, and does not handle images, audio, or video.
-
How is Nova 2 Lite priced on LLM.API?
LLM.API exposes Nova 2 Lite with per-token input and output pricing; check the LLM.API pricing section for exact, up-to-date rates.
-
How do I access Nova 2 Lite through LLM.API?
Call the LLM.API chat or completion endpoint and set the model parameter to "amazon/nova-2-lite" with your LLM.API key.
-
How does Nova 2 Lite compare to larger Nova models?
Nova 2 Lite is cheaper and faster than larger Nova variants but offers lower reasoning depth, reliability, and multilingual strength.
-
What are the main limitations of Nova 2 Lite?
Nova 2 Lite can struggle with complex reasoning, very long multi-step instructions, strict tool-calling workflows, and domain-specific expert tasks.
-
Can I use Nova 2 Lite for production workloads via LLM.API?
Yes, Nova 2 Lite can be used for production workloads via LLM.API, especially where throughput and cost efficiency are priorities.
Compare
Competitive Models
- Instruction Following
Reka Edge is a 7B-parameter multimodal vision-language model from RekaAI that processes text, image, and video inputs to generate text outputs, optimized for fast, efficient edge…
- Instruction Following
Seed 1.6 Flash is an ultra-fast multimodal "deep thinking" large language model from ByteDance Seed, offering long-context reasoning with support for both text and visual inputs.