Cohere (4 models) vs Amazon (5 models) - compared across composite scores, pricing, capabilities, and context windows.
| Cohere | Score | vs | Amazon | Score |
|---|---|---|---|---|
| Command A | 51 | Nova 2 Lite | 61 | |
| Command R+ (08-2024) | 49 | Nova Premier 1.0 | 40 | |
| Command R (08-2024) | 49 | Nova Lite 1.0 | 40 | |
| Command R7B (12-2024) | 36 | Nova Micro 1.0 | 40 |
| Capability | Cohere | Amazon | Leader |
|---|---|---|---|
Vision | 0/4 | 4/5 | Amazon |
Reasoning | 0/4 | 1/5 | Amazon |
Function Calling | 2/4 | 5/5 | Amazon |
JSON Mode | 4/4 | 0/5 | Cohere |
Web Search | 0/4 | 0/5 | Tie |
Streaming | 4/4 | 5/5 | Amazon |
Image Output | 0/4 | 0/5 | Tie |
| Metric | Cohere | Amazon |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.037 Command R7B (12-2024) | $0.035 Nova Micro 1.0 |
| Cheapest Output (per 1M tokens) | $0.150 | $0.140 |
| Most Expensive Input (per 1M tokens) | $2.50 Command A | $2.50 Nova Premier 1.0 |
| Most Expensive Output (per 1M tokens) | $10.00 | $12.50 |
| Free Models | 0 | 0 |
| Max Context Window | 256K | 1.0M |
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| Command A | 51 | $2.50 | $10.00 |
| Command R+ (08-2024) | 49 | $2.50 | $10.00 |
| Command R (08-2024) | 49 | $0.150 | $0.600 |
| Command R7B (12-2024) | 36 | $0.037 | $0.150 |
| 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 |
Compare any two AI providers side-by-side.
Amazon's focus on multimodal AI reflects their AWS customer base requiring vision for document processing, retail analytics, and manufacturing QA, with Nova 2 Lite (54/100) leading their lineup. Cohere's text-only strategy targets enterprise NLP use cases exclusively, trading multimodal flexibility for specialized text performance and lower complexity, though their top model Command R+ scores only 38/100.
Cohere's partial open source approach allows customers to self-host and customize models for regulated industries, though their average score of 36/100 trails Amazon's 43/100. Amazon's proprietary-only strategy locks customers into AWS infrastructure but delivers 100% function calling coverage (5/5 models) versus Cohere's 50% (2/4), making Amazon superior for production API integrations.
Amazon's superior performance stems from massive compute investment and multimodal training, achieving 80% vision coverage and 100% function calling support across their 5-model portfolio. Cohere's lower scores reflect their bet on efficient inference over raw performance, offering a narrower price range ($0.150-$10.00) compared to Amazon's wider spread ($0.140-$12.50), suggesting different optimization priorities.
Amazon's million-token context enables document-heavy enterprise workflows like contract analysis and codebase understanding, justifying their $12.50/M premium tier pricing. Cohere's 256K limit targets conversational AI and moderate document tasks, focusing on cost efficiency with their cheapest model at $0.150/M versus Amazon's $0.140/M, a negligible 7% difference that doesn't justify the 4x context limitation.
Both providers show weak reasoning coverage, with Amazon's 20% (1/5) barely beating Cohere's 0%, indicating neither prioritizes complex analytical tasks despite Amazon's higher average score of 43/100. This gap pushes both toward simpler applications: Cohere for text generation and classification, Amazon for vision-enabled workflows leveraging their 80% vision model coverage.