GenAI Chat Application
This analysis combines static model reference data with live evaluation results from the BaatCheet evaluation interface. Click Refresh Live Data to pull the latest results from the API.
| Model | Provider | Type | Input $/1K tok | Output $/1K tok |
|---|---|---|---|---|
| Claude Sonnet 4.6 | Anthropic | Proprietary | $0.003 | $0.015 |
| Claude Opus 4.6 | Anthropic | Proprietary | $0.015 | $0.075 |
| Amazon Nova Pro | Amazon | Proprietary | $0.0008 | $0.0032 |
| Amazon Nova Lite | Amazon | Proprietary | $0.00006 | $0.00024 |
| Llama 4 Maverick | Meta | Open-source | $0.0003 | $0.0009 |
Best for Complex reasoning, code review, research synthesis, legal/medical analysis
Avoid when Budget-constrained, latency-sensitive, simple Q&A
Best for Code generation, technical writing, detailed explanations, customer-facing content
Avoid when High-volume batch processing, real-time autocomplete
Best for Summarization, classification, structured extraction, internal tools
Avoid when Frontier-level reasoning tasks, nuanced creative writing
Best for High-throughput pipelines, chatbot routing, moderate-complexity Q&A
Avoid when Tasks requiring top-tier accuracy, enterprise compliance requiring proprietary models
Best for Autocomplete, simple Q&A, content moderation, high-volume pipelines
Avoid when Complex reasoning, long-form content generation
Based on evaluation test cases (knowledge, coding, architecture, AWS, creative, math reasoning, structured output, factual, logic, conciseness) run against all 6 models with automated judge scoring.
Opus 4.6 delivers the highest quality scores from our judge model, but costs 457x more than Nova Lite. Sonnet 4.6 and Nova Pro deliver comparable quality on 7 out of 10 test cases at a fraction of the cost. The premium models only differentiate meaningfully on math reasoning, multi-step logic, and complex architecture prompts.
Nova Lite averages ~400 output tokens at ~2.5s — concise but sufficient for most use cases.
Llama 4 Maverick matches Nova Lite's latency while producing slightly longer responses. On structured output and factual tasks, it performs comparably to Sonnet 4.6 at 20x lower cost. However, on creative and nuanced tasks, Anthropic models consistently score higher.