Amazon (5 models) vs Microsoft (3 models) - compared across composite scores, pricing, capabilities, and context windows.
| Amazon | Score | vs | Microsoft | Score |
|---|---|---|---|---|
| Nova 2 Lite | 61 | Phi 4 | 60 | |
| Nova Premier 1.0 | 40 | WizardLM-2 8x22B | 28 | |
| Nova Lite 1.0 | 40 | Phi 4 Mini Instruct | 53 |
| Capability | Amazon | Microsoft | Leader |
|---|---|---|---|
Vision | 4/5 | 0/3 | Amazon |
Reasoning | 1/5 | 0/3 | Amazon |
Function Calling | 5/5 | 0/3 | Amazon |
JSON Mode | 0/5 | 2/3 | Microsoft |
Web Search | 0/5 | 0/3 | Tie |
Streaming | 5/5 | 3/3 | Amazon |
Image Output | 0/5 | 0/3 | Tie |
| Metric | Amazon | Microsoft |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.035 Nova Micro 1.0 | $0.065 Phi 4 |
| Cheapest Output (per 1M tokens) | $0.140 | $0.140 |
| Most Expensive Input (per 1M tokens) | $2.50 Nova Premier 1.0 | $0.620 WizardLM-2 8x22B |
| Most Expensive Output (per 1M tokens) | $12.50 | $0.620 |
| Free Models | 0 | 0 |
| Max Context Window | 1.0M | 128K |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Nova 2 Lite | 61 | $0.300 | $2.50 |
| Nova Premier 1.0 | 40 | $2.50 | $12.50 |
| Nova Lite 1.0 | 40 | $0.060 | $0.240 |
| Nova Micro 1.0 | 40 | $0.035 | $0.140 |
| Nova Pro 1.0 | 40 | $0.800 | $3.20 |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Phi 4 | 60 | $0.065 | $0.140 |
| Phi 4 Mini Instruct | 53 | $0.080 | $0.350 |
| WizardLM-2 8x22B | 28 | $0.620 | $0.620 |
Compare any two AI providers side-by-side.
Amazon's Nova 2 Lite (54/100) benefits from broader capability coverage with 4/5 vision models and perfect 5/5 function calling support, while Microsoft's Phi 4 (32/100) lacks these entirely with 0/2 scores across all capabilities. The performance gap suggests Amazon prioritized capability breadth over pure cost optimization, as both models share the same $0.140/M output pricing floor.
Microsoft's 100% open source portfolio (both models) enables on-premise deployment and fine-tuning without vendor lock-in, while Amazon's 0/5 open source offerings require AWS infrastructure. This philosophical difference is reflected in their max context windows - Microsoft caps at 66K tokens for local deployment feasibility, while Amazon pushes to 1M tokens leveraging their cloud infrastructure.
Amazon dominates multimodal use cases with 4 out of 5 models supporting vision, while Microsoft offers zero vision capabilities across both models. The pricing premium for Amazon's multimodal models ranges up to $12.50/M tokens (89x their base rate), suggesting these capabilities target enterprise computer vision workloads rather than general-purpose applications.
Microsoft's focused approach with 2 open source models at competitive prices ($0.140-$0.620/M) appeals to teams prioritizing deployment flexibility and avoiding vendor lock-in. Amazon's 5-model portfolio averages 43/100 performance with significant capability variance, creating decision paralysis and potential over-engineering for straightforward text generation tasks.
Amazon's minimal 20% reasoning coverage (1/5 models) and Microsoft's complete absence (0/2) reveal neither provider prioritizes complex analytical tasks. Amazon's single reasoning-capable model likely serves specific AWS enterprise workflows, while Microsoft's Phi models focus on efficient text generation at scale rather than advanced problem-solving.
Amazon's massive price variance reflects a segmented strategy targeting everything from cost-sensitive applications (Nova 2 Lite at $0.140/M) to specialized enterprise workloads (premium models at $12.50/M with vision and 1M context). Microsoft's compressed pricing around the low end indicates focus on democratizing AI access rather than premium feature differentiation.