Specifications
Context Window
—
Input Price/1M
$0.30
Output Price/1M
$2.50
Parameters
—
Benchmarks
Nova 2.0 Omni (low) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
Agentic
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 4.0 | 100.0 | — |
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 59.0 | 100.0 | — |
| SciCode | 34.0 | 100.0 | — |
| AA Coding Index | 13.9 | 100.0 | — |
Knowledge
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 80.0 | 100.0 | — |
Long Context
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 51.0 | 100.0 | — |
Math
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AIME 2025 | 56.0 | 100.0 | — |
| AA Math Index | 56.0 | 100.0 | — |
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 23.2 | 100.0 | — |
Reasoning
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| GPQA Diamond | 70.0 | 100.0 | — |
| IFBench | 62.0 | 100.0 | — |
| HLE | 4.0 | 100.0 | — |
Tool Use
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 68.0 | 100.0 | — |
Information
- Release date
- November 26, 2025
- Tool Calling
- ❌ Not supported
- Vision
- ❌ Not supported
- Audio
- ❌ Not supported
Full Analysis: Nova 2.0 Omni (low)
What is Nova 2.0 Omni (low)?
Nova 2.0 Omni (low) is an AI model developed by Amazon, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via Amazon's cloud API.
Pricing & Costs in 2026
Nova 2.0 Omni (low) is usage-based, priced at $0.3/1M input tokens and $2.5/1M 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.
Benchmarks & Performance
Nova 2.0 Omni (low) was evaluated on 13 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show solid 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.
Recommended Use Cases
Nova 2.0 Omni (low) specializes in text, offering advanced capabilities for creating and processing text content.
Comparison with Alternatives
In the 2026 AI model ecosystem, Nova 2.0 Omni (low) 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.
Frequently Asked Questions
What is Nova 2.0 Omni (low)?
Nova 2.0 Omni (low) is an AI model developed by Amazon. It is a text model.
How much does Nova 2.0 Omni (low) cost?
Nova 2.0 Omni (low) costs $0.3/1M input tokens and $2.5/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
How does Nova 2.0 Omni (low) compare with other models?
In available benchmarks, Nova 2.0 Omni (low) scored: Terminal-Bench Hard: 4/100, LiveCodeBench: 59/100, SciCode: 34/100. See the full table above for a detailed comparison.
Is Nova 2.0 Omni (low) open source?
No, Nova 2.0 Omni (low) is a proprietary model from Amazon. It is available via cloud API. For open source alternatives, check our open source model ranking.
What is Nova 2.0 Omni (low) best for?
Nova 2.0 Omni (low) excels at general-purpose language tasks.
Last updated: June 01, 2026 • View methodology →