OpenAI vs Amazon
OpenAI (97 models) vs Amazon (5 models) - compared across composite scores, pricing, capabilities, and context windows.
OpenAI
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View all modelsHead-to-Head: OpenAI vs Amazon Model Matchups
| OpenAI | Score | vs | Amazon | Score |
|---|---|---|---|---|
| GPT-5.5 Pro | 93 | Nova 2 Lite | 61 | |
| GPT-5.5 Pro (batch) | 93 | Nova Premier 1.0 | 40 | |
| GPT-5.5 | 93 | Nova Lite 1.0 | 40 | |
| GPT-5.5 (batch) | 93 | Nova Micro 1.0 | 40 | |
| GPT-5.4 Pro | 92 | Nova Pro 1.0 | 40 |
Capability Comparison
| Capability | OpenAI | Amazon | Leader |
|---|---|---|---|
Vision | 78/97 | 4/5 | OpenAI |
Reasoning | 67/97 | 1/5 | OpenAI |
Function Calling | 89/97 | 5/5 | OpenAI |
JSON Mode | 95/97 | 0/5 | OpenAI |
Web Search | 76/97 | 0/5 | OpenAI |
Streaming | 95/97 | 5/5 | OpenAI |
Image Output | 4/97 | 0/5 | OpenAI |
Pricing Comparison
| Metric | OpenAI | Amazon |
|---|---|---|
| Cheapest Input (per 1M tokens) | $0.025 gpt-oss-20b | $0.035 Nova Micro 1.0 |
| Cheapest Output (per 1M tokens) | $0.130 | $0.140 |
| Most Expensive Input (per 1M tokens) | $150.00 o1-pro | $2.50 Nova Premier 1.0 |
| Most Expensive Output (per 1M tokens) | $600.00 | $12.50 |
| Free Models | 1 | 0 |
| Max Context Window | 1.1M | 1.0M |
All OpenAI Models (97)
| Model | Score | Input $/M | Output $/M |
|---|---|---|---|
| GPT-5.5 Pro | 93 | $30.00 | $180.00 |
| GPT-5.5 Pro (batch) | 93 | $15.00 | $90.00 |
| GPT-5.5 | 93 | $5.00 | $30.00 |
| GPT-5.5 (batch) | 93 | $2.50 | $15.00 |
| GPT-5.4 Pro | 92 | $30.00 | $180.00 |
| GPT-5.4 Pro (batch) | 92 | $15.00 | $90.00 |
| GPT-5.4 | 92 | $2.50 | $15.00 |
| GPT-5.4 (batch) | 92 | $1.25 | $7.50 |
| GPT-5.3 Chat | 91 | $1.75 | $14.00 |
| GPT-5.3-Codex | 91 | $1.75 | $14.00 |
| GPT-5.2-Codex | 91 | $1.75 | $14.00 |
| GPT-5.2 Chat | 91 | $1.75 | $14.00 |
| GPT-5.2 Pro | 91 | $21.00 | $168.00 |
| GPT-5.2 Pro (batch) | 91 | $10.50 | $84.00 |
| GPT-5.2 | 91 | $1.75 | $14.00 |
| GPT-5.2 (batch) | 91 | $0.875 | $7.00 |
| GPT-5.6 Luna Pro | 89 | $0.100 | $0.600 |
| GPT-5.6 Luna Pro (batch) | 89 | $0.100 | $0.600 |
| GPT-5.6 Luna | 89 | $0.100 | $0.600 |
| GPT-5.6 Luna (batch) | 89 | $0.100 | $0.600 |
All Amazon Models (5)
| 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 |
More Provider Comparisons
Compare any two AI providers side-by-side.
OpenAI's broad portfolio reflects their research-first approach, with 59 additional models providing specialized capabilities like web search (31/64 models) and diverse pricing tiers from $0.110 to $600/M tokens. Amazon's focused strategy delivers 43/100 average score with just 5 models, all supporting function calling (5/5) and vision (4/5), suggesting enterprise customers may not need OpenAI's experimental long-tail.
Nova 2 Lite targets pure generation workloads at $0.140/M tokens, sitting between OpenAI's cheapest ($0.110/M) and GPT-4 class models. With only 1/5 Amazon models supporting reasoning versus 34/64 at OpenAI, Amazon clearly optimizes for high-volume production use cases where function calling (5/5 support) matters more than complex reasoning.
OpenAI spans from experimental models at $0.110/M to specialized high-context models at $600/M tokens, including 2 free options for prototyping. Amazon's narrower $0.140-$12.50/M range with zero free tier signals focus on predictable enterprise pricing rather than research accessibility or extreme specialization.
Amazon provides vision on 80% of models versus OpenAI's 65%, with tighter integration into AWS ecosystem for production deployments. OpenAI's 42 vision models include experimental variants and research models, while Amazon's 4 vision-capable models all support function calling (5/5), making them more suitable for structured enterprise workflows.
GPT-5.4's 67/100 score represents diminishing returns territory where each point costs exponentially more in compute and pricing. Amazon's Nova 2 Lite at 54/100 delivers 80% of the capability while maintaining consistent function calling support across all 5 models, versus OpenAI's 57/64 coverage, suggesting the performance gap may not justify the complexity for most use cases.