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====== MAIT-510 – Learn Azure OpenAI: GPT ======

===== Model Overview and Comparison =====

^ Model                       ^ Tokens/Minute (TPM)       ^ Requests/Minute (RPM) ^ Latency               ^ Throughput        ^ Error Handling ^
| **gpt-4o**                  | Up to 450K per region      | Varies by deployment   | Low (real-time)       | High (streaming)  | Handles large prompts; monitor for 429/500; implement retries/backoff |
| **gpt-4**                   | Varies by deployment       | Varies by deployment   | Moderate (~1.3s avg)  | Moderate          | Monitor 429; limit prompt size; retries |
| **gpt-4.1**                 | Up to 30K TPM (enforced)   | Varies by deployment   | Moderate              | Moderate          | Known 500s in regions; monitor 429/500 |
| **gpt-4.1-mini**            | Not publicly documented    | Not publicly documented| Likely low            | Likely high       | General best practices apply |
| **gpt-4-32k**               | Varies by deployment       | Varies by deployment   | Higher (context size) | Lower             | Monitor 429; 32K max prompt |
| **gpt-35-turbo-16k**        | Varies by deployment       | Varies by deployment   | Low (~900ms avg)      | High              | Monitor 429; 16K max prompt |
| **gpt-35-turbo**            | Varies by deployment       | Varies by deployment   | Low (~900ms avg)      | High              | Monitor 429; 4K max prompt |
| **gpt-35-turbo-instruct**   | Varies by deployment       | Varies by deployment   | Low                   | High              | Monitor 429; 4K max prompt |
| **gpt-4.5-preview**         | Not publicly documented    | Not publicly documented| Experimental          | Experimental      | Pre-release; expect bugs; robust error handling |
| **gpt-4.1-nano**            | Not publicly documented    | Not publicly documented| Likely very low       | Likely very high  | General best practices apply |
| **gpt-image-1**             | Not publicly documented    | Not publicly documented| Moderate              | Moderate          | Monitor image-specific errors |
| **gpt-4o-mini / tts / audio** | Not publicly documented  | Not publicly documented| Very low (real-time)  | High              | Monitor audio errors; use proper input format |

===== GPT-4o vs GPT-4.1 Turbo Comparison =====

^ Category              ^ GPT-4o                   ^ GPT-4.1 (Turbo)            ^ Winner         ^
| Reasoning             | Equal or slightly better| Strong performance         | Tie            |
| Coding                | Better real-time        | Better in benchmarks       | GPT-4.1        |
| Math                  | Better interpretive     | Better symbolic precision  | Tie / GPT-4.1  |
| Instruction Following | More expressive         | More formal                | GPT-4o         |
| Multilingual          | Better tokenization     | Less efficient             | GPT-4o         |
| Image Understanding   | Native support          | Not supported              | GPT-4o         |
| Speech/TTS            | Built-in STT/TTS        | Not supported              | GPT-4o         |
| Expressiveness        | Dynamic & expressive    | Flat tone                  | GPT-4o         |
| Factual Accuracy      | Similar cutoff          | Similar cutoff             | Tie            |
| Steerability          | Strong tone/style ctrl  | Text only                  | GPT-4o         |
| Token Efficiency      | Better compression      | Slightly worse             | GPT-4o         |

**Summary:**
  * **GPT-4.1**: Best for symbolic reasoning, coding, structured QA.
  * **GPT-4o**: Best for multimodal, expressiveness, efficiency, speech/image.

===== Latency Comparison =====

^ Model    ^ Avg Latency (Time to First Token) ^ Notes ^
| GPT-4o   | ~5 seconds (Azure)                | Optimized for low latency + multimodal tasks |
| GPT-4.1  | ~45 seconds for 1000–1500 tokens  | Higher latency, especially for long completions |

===== Throughput Comparison =====

^ Model    ^ TPM         ^ RPM     ^ Notes                                ^
| GPT-4o   | 150,000     | 900     | Higher quotas available via enterprise |
| GPT-4.1  | 3,000/PTU   | Varies  | Dependent on Provisioned Throughput  |

===== Use Cases =====

==== 1. Automated IT Support & Triage ====
  * **Use**: GPT-4o or GPT-4.1  
  * **Tasks**: Triage tickets, Tier-1 fixes, generate CLI, summarize alerts  
  * **Benefits**: Faster, reduces L1 work, integrates with ServiceNow or DevOps  

==== 2. Infrastructure-as-Code Review ====
  * **Use**: GPT-4.1  
  * **Tasks**: Review Bicep/ARM/Pulumi, validate configs  
  * **Benefits**: Promotes standardization, catches misconfigs  

==== 3. Security & Policy Review ====
  * **Use**: GPT-4o / GPT-4.1  
  * **Tasks**: Analyze IAM, firewalls, audit logs; policy translation  
  * **Benefits**: Faster audits, stronger compliance, cross-team alignment  

===== Manual Testing: Thermodynamics Prompt =====

**Prompt:**  
Calculate ΔH (kJ/mol NaNO₃) using a calorimeter (451 J/°C), 0.0300 mol NaOH, 1000 mL of 0.0300 M HNO₃, T↑ from 23.000°C → 23.639°C. Assume 4.18 J/g°C, 1.00 g/mL.

**GPT-4.1 Output:**
  * Heat (solution): **2673.3 J** ❌ (should be 2671.02)
  * Calorimeter: 288.4 J  
  * Total q: 2961.7 J  
  * ΔH = **–98.7 kJ/mol**

**GPT-4o Output:**
  * Heat (solution): **2672.82 J** ❌  
  * Calorimeter: 288.69 J  
  * Total q: 2961.51 J  
  * ΔH = **–98.7 kJ/mol**

**Correct Calculation:**
  * 1000 × 4.18 × 0.639 = **2671.02 J** ✅  
  * Total heat = 2671.02 + 288.69 = **2959.71 J**  
  * ΔH = –2959.71 / 0.0300 = **–98.7 kJ/mol**

===== Conclusion =====

  * GPT-4.1 = better **explanations**, but made arithmetic errors.
  * GPT-4o = better **numerical skill**, but also rounded incorrectly.
  * Both models accepted feedback—but repeated the **same mistake**.
  * ChatGPT (web version) corrected its error and gave the **correct final answer**.
  * Playground versions seem more prone to **repeating numeric errors**.
  * **GPT-4.1** = best for detailed QA/debug work.  
  * **GPT-4o** = best for expressive, real-time, multimodal tasks.
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