# O3 Model Comparison: openai/o:latest vs openai/o3

## Summary
Both `openai/o:latest` and `openai/o3` route to the **identical** underlying model deployment in CBORG with **no configuration differences** detected.

## Technical Details

### 1. Underlying Model
- **openai/o:latest** → `azure/o3-2025-04-16`
- **openai/o3** → `azure/o3-2025-04-16`
- ✓ **SAME** base model

### 2. Configuration Parameters
Tested with explicit parameters:
```python
temperature=1.0
top_p=1.0
max_tokens=10
```

**Result**: Both models respond identically
- Same token usage for same prompts
- Same response IDs format
- Same provider-specific fields: `{'content_filter_results': {}}`
- No system fingerprint differences (both return `None`)

### 3. API Response Comparison
Multiple test calls (3 each) showed:
- Identical response structure
- Same routing backend
- No detectable configuration differences
- No temperature/top_p/frequency_penalty differences

## Performance After Merging

After merging both experimental runs, the combined statistics are:

| Step | Success Rate | Trials |
|------|-------------|--------|
| 1    | 95.0% (19/20) | 20     |
| 2    | 60.0% (12/20) | 20     |
| 3    | 20.0% (4/20)  | 20     |
| 4    | 100.0% (20/20)| 20     |
| 5    | 65.0% (13/20) | 20     |

**Total records**: 100 (50 from `openai/o:latest` + 50 from `openai/o3`)

The merged data provides:
- ✓ More robust statistics (doubled sample size)
- ✓ Average performance across both experimental runs
- ✓ Reduced variance in the estimates

## Why Were There Performance Differences Before Merging?

The separate experimental runs showed different performance:
- Step 3: 10% vs 30% success (20 percentage point difference)
- Step 5: 50% vs 80% success (30 percentage point difference)

These differences were **NOT due to model configuration**, but rather:

1. **Different Experimental Runs**
   - Different timestamps when trials were conducted
   - Separate experimental sessions

2. **Natural Model Variability**
   - O3 models are reasoning models with inherent variability
   - Even with same temperature, outputs can differ significantly
   - Non-deterministic reasoning processes

3. **Small Sample Size Effects**
   - Only 10 trials per step in each run
   - Random variation can appear as systematic differences
   - Merging to 20 trials provides more stable estimates

4. **Temporal Factors**
   - Models might have been tested at different times
   - Backend infrastructure state could differ
   - Load balancing or deployment variations

By merging, we get a more representative average of the model's actual performance.

## Recommendation

**Merge both models in plots** because:

1. ✓ They are technically identical (same model, same configuration)
2. ✓ Performance differences are due to experimental variability, not model differences
3. ✓ Merging provides more robust statistics (20 trials per step instead of 10)
4. ✓ Reduces clutter in visualizations while preserving all data

**Display names** (updated):
- `openai/o:latest` → **"O3 (2025-04-16)"**
- `openai/o3` → **"O3 (2025-04-16)"**

This naming makes it clear:
- Both use the same base model (2025-04-16)
- Data from both variants is combined under a single label
- Total: 100 records (50 + 50) across 5 steps = 20 trials per step

## CBORG Routing Behavior

From our testing, CBORG treats both aliases as:
- **Functionally identical** at the API level
- **Same deployment** (azure/o3-2025-04-16)
- **No configuration override** based on alias name

The alias `openai/o:latest` is simply a pointer to `openai/o3` at the CBORG routing layer, but the experiments treated them as separate model selections, leading to different trial data.

## Conclusion

`openai/o:latest` and `openai/o3` are technically the same model with the same configuration. They have been **merged in the plots** under the single label **"O3 (2025-04-16)"** to:
- Provide more robust statistics (20 trials per step)
- Reduce visualization clutter
- Average out experimental variability
- Present a clearer picture of the model's typical performance

The merged dataset combines 100 total records (50 + 50) across all 5 steps, providing better statistical reliability than either run alone.