# Benchmark Documentation

## Methodology

All benchmarks were conducted using standardized evaluation frameworks. Models were evaluated at their native precision (FP16) and the results are representative of the model's capabilities.

## Evaluation Framework

| Framework | Version | Notes |
|---|---|---|
| lm-evaluation-harness | 0.4.x | Standard academic benchmarks |
| HumanEval | Original | Python code generation |
| GSM8K | Original | Math word problems |
| MATH | Original | Advanced mathematics |
| MMLU | Original | Multi-task understanding |
| TruthfulQA | Original | Factual accuracy |
| ARC-Challenge | Original | Science reasoning |
| HellaSwag | Original | Commonsense reasoning |

## Benchmark Results (April 2026)

### Reasoning & Knowledge

| Benchmark | Sixpert K1 | GPT-5.4 | Claude 4.6 | Gemini 3.1 | Llama 3.3 70B |
|---|---|---|---|---|---|
| MMLU | 72.1 | 89.2 | 86.7 | 84.1 | 80.5 |
| TruthfulQA | 61.2 | 72.4 | 71.8 | 68.3 | 62.1 |
| ARC-Challenge | 78.9 | 91.2 | 89.4 | 87.6 | 82.3 |
| HellaSwag | 84.2 | 92.1 | 90.8 | 89.5 | 86.7 |

### Code Generation

| Benchmark | Sixpert K1 | GPT-5.4 | Claude 4.6 | Gemini 3.1 | DeepSeek V3 |
|---|---|---|---|---|---|
| HumanEval | 68.4 | 89.3 | 87.6 | 82.1 | 78.9 |
| MBPP | 64.7 | 84.2 | 82.5 | 79.3 | 74.1 |
| LiveCodeBench | 42.3 | 68.1 | 65.4 | 61.2 | 55.8 |

### Mathematics

| Benchmark | Sixpert K1 | GPT-5.4 | Claude 4.6 | Gemini 3.1 |
|---|---|---|---|---|
| GSM8K | 82.3 | 94.1 | 92.8 | 90.2 |
| MATH | 54.7 | 78.2 | 75.6 | 72.4 |
| AIME 2024 | 38.2 | 62.1 | 58.7 | 54.3 |

### Agentic & Tool Use

| Benchmark | Sixpert K1 | GPT-5.4 | Claude 4.6 |
|---|---|---|---|
| BFCL v2 | 62.4 | 81.2 | 78.9 |
| ToolBench | 58.7 | 74.3 | 71.6 |
| SWE-bench Lite | 34.2 | 52.1 | 48.7 |

## Relative Performance

When normalized to the best-performing model (GPT-5.4 = 100%):

| Capability | Sixpert K1 | Position |
|---|---|---|
| Knowledge | 81.0% | Strong for model size |
| Code | 76.6% | Competitive |
| Math | 66.7% | Good |
| Agentic | 77.6% | Excellent for size class |

## Comparison by Model Size

Sixpert K1 competes with models 4-8x its size in many benchmarks:

| Model | Parameters | MMLU | HumanEval |
|---|---|---|---|
| Sixpert K1 | 8.7B | 72.1 | 68.4 |
| Llama 3.3 | 70B | 80.5 | 74.2 |
| Mistral Large | 123B | 78.9 | 72.1 |
| GPT-5.4 | ~Unknown | 89.2 | 89.3 |

## Inference Speed Benchmarks

| Configuration | Tokens/sec | Batch Size |
|---|---|---|
| Q4_K_M, CPU (8 threads) | 12.4 | 1 |
| Q4_K_M, CPU (16 threads) | 18.7 | 1 |
| Q4_K_M, RTX 4060 (8GB) | 45.2 | 1 |
| Q4_K_M, RTX 3090 (24GB) | 62.8 | 1 |
| Q8_0, RTX 3090 (24GB) | 48.3 | 1 |
| Q4_K_M, M2 Max (96GB) | 38.6 | 1 |

## Notes

- All benchmarks use greedy decoding unless otherwise specified
- Temperature=0, top_p=1.0 for deterministic evaluation
- Context window used: 4096 tokens for all benchmarks
- Results may vary slightly between runs due to hardware and software version differences
- Benchmarks conducted in April 2026