Amazon • LLM
Amazon Nova Micro 1.0 is a text-only model that delivers the lowest latency responses in the Amazon Nova family of models at a very low cost. With a context length...
Context Window
128K tokens
Input Price/1M
$0.04
Output Price/1M
$0.14
Parameters
—
Max Output
5K tokens
Amazon: Nova Micro 1.0 results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 14.0 | 100.0 | Artificial Analysis official API |
| AA Coding Index | 4.1 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MATH-500 | 70.3 | 100.0 | Artificial Analysis official API |
| AA Math Index | 6.0 | 100.0 | Artificial Analysis official API |
| AIME 2025 | 6.0 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 10.3 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 53.1 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 35.8 | 100.0 | Artificial Analysis official API |
Amazon: Nova Micro 1.0 is an AI model developed by Amazon, classified as a large language model (LLM). It focuses on text processing and natural language generation. As a proprietary model, it is available via Amazon's cloud API. With a context window of 128K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Amazon: Nova Micro 1.0 is usage-based, priced at $0.035/1M input tokens and $0.14/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
Amazon: Nova Micro 1.0 was evaluated on 8 different benchmarks, covering categories like Coding, Math, overall, Reasoning. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Amazon: Nova Micro 1.0 is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), automation with tool calling (API integration, databases, external systems), high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.
In the 2026 AI model ecosystem, Amazon: Nova Micro 1.0 competes directly with similarly capable models. Amazon competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Amazon Nova Micro 1.0 is a text-only model that delivers the lowest latency responses in the Amazon Nova family of models at a very low cost. With a context length...
Amazon: Nova Micro 1.0 costs $0.035/1M input tokens and $0.14/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, Amazon: Nova Micro 1.0 scored: LiveCodeBench: 14/100, AA Coding Index: 4.1/100, MATH-500: 70.3/100. See the full table above for a detailed comparison.
No, Amazon: Nova Micro 1.0 is a proprietary model from Amazon. It is available via cloud API. For open source alternatives, check our open source model ranking.
Amazon: Nova Micro 1.0 excels at general-purpose language tasks. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: May 15, 2026 • View methodology →