# Benchmark Documentation

## Methodology

All benchmarks were conducted using standardized evaluation frameworks with models at native precision (FP16). Results represent the model's full capabilities.

## Evaluation Framework

| Framework | Version | Notes |
|---|---|---|
| lm-evaluation-harness | 0.4.x | Standard academic benchmarks |
| HumanEval | Original | Python code generation |
| MBPP | Original | Python benchmarking problems |
| GSM8K | Original | Math word problems |
| MATH | Original | Advanced mathematics |
| MMLU | Original | Multi-task understanding |
| GPQA | Original | Graduate-level science |
| ARC-Challenge | Original | Science reasoning |
| LiveCodeBench | 2025 | Competitive programming |
| SWE-bench | Original | Software engineering |

## Benchmark Results (April 2026)

### Reasoning & Knowledge

| Benchmark | Sixpert K2 | GPT-5.4 | Claude 4.6 | Gemini 3.1 | Llama 3.3 70B |
|---|---|---|---|---|---|
| MMLU | 76.8 | 89.2 | 86.7 | 84.1 | 80.5 |
| GPQA | 52.3 | 71.2 | 68.4 | 65.1 | 54.7 |
| ARC-Challenge | 82.1 | 91.2 | 89.4 | 87.6 | 82.3 |
| HellaSwag | 87.4 | 92.1 | 90.8 | 89.5 | 86.7 |

### Code Generation

| Benchmark | Sixpert K2 | GPT-5.4 | Claude 4.6 | Gemini 3.1 | DeepSeek V3 |
|---|---|---|---|---|---|
| HumanEval | 72.6 | 89.3 | 87.6 | 82.1 | 78.9 |
| MBPP | 68.9 | 84.2 | 82.5 | 79.3 | 74.1 |
| LiveCodeBench | 46.7 | 68.1 | 65.4 | 61.2 | 55.8 |

### Mathematics

| Benchmark | Sixpert K2 | GPT-5.4 | Claude 4.6 | Gemini 3.1 |
|---|---|---|---|---|
| GSM8K | 85.7 | 94.1 | 92.8 | 90.2 |
| MATH | 58.3 | 78.2 | 75.6 | 72.4 |
| AIME 2024 | 42.1 | 62.1 | 58.7 | 54.3 |

### Agentic & Tool Use

| Benchmark | Sixpert K2 | GPT-5.4 | Claude 4.6 |
|---|---|---|---|
| BFCL v2 | 67.8 | 81.2 | 78.9 |
| ToolBench | 63.4 | 74.3 | 71.6 |
| SWE-bench Lite | 38.7 | 52.1 | 48.7 |

### Long-Context

| Benchmark | Sixpert K2 | GPT-5.4 | Claude 4.6 |
|---|---|---|---|
| Needle-in-Haystack (128K) | 94.2% | 97.1% | 96.8% |
| Ruler (128K) | 82.4% | 89.7% | 87.3% |
| InfiniteBench (128K) | 48.3% | 62.1% | 58.7% |

## Relative Performance

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

| Capability | Sixpert K2 | Position |
|---|---|---|
| Knowledge | 86.1% | Very strong for model size |
| Code | 81.5% | Competitive |
| Math | 72.8% | Strong |
| Agentic | 83.5% | Excellent |
| Long-Context | 94.8% | Outstanding |

## Comparison by Active Parameters

The key metric for MoE models is **active parameters per token**, not total:

| Model | Total Params | Active/Token | MMLU | HumanEval |
|---|---|---|---|---|
| Sixpert K2 | 8.9B | ~1.2B | 76.8 | 72.6 |
| Phi-3 Medium | 14B | 14B | 72.1 | 64.2 |
| Gemma 2 27B | 27B | 27B | 77.4 | 68.9 |
| Llama 3.3 70B | 70B | 70B | 80.5 | 74.2 |
| GPT-5.4 | ~Unknown | ~Unknown | 89.2 | 89.3 |

Sixpert K2 outperforms dense models with 5-20x more active parameters.

## Inference Speed Benchmarks

| Configuration | Tokens/sec | Notes |
|---|---|---|
| Q4_K_M, CPU (8 threads) | 18.4 | MoE advantage vs dense |
| Q4_K_M, CPU (16 threads) | 28.2 | MoE advantage vs dense |
| Q4_K_M, RTX 4060 (8GB) | 67.3 | Full offload possible |
| Q4_K_M, RTX 3090 (24GB) | 89.6 | Full offload |
| Q6_K, RTX 3090 (24GB) | 72.1 | Higher quality |
| Q4_K_M, M2 Max (96GB) | 54.8 | Apple Silicon |

## MoE Efficiency Analysis

Comparing K2 to a hypothetical dense model with the same total parameters:

| Metric | K2 (MoE) | Dense 8.9B | Ratio |
|---|---|---|---|
| Inference speed | 67.3 tok/s | ~12 tok/s | 5.6x faster |
| VRAM (FP16) | ~18GB | ~18GB | Same |
| VRAM (Q4_K_M) | ~5GB | ~5GB | Same |
| Quality (MMLU) | 76.8 | ~70.0 | 9.7% better |
| Quality (HumanEval) | 72.6 | ~62.0 | 17.1% better |

## Notes

- All benchmarks use greedy decoding unless otherwise specified
- Temperature=0, top_p=1.0 for deterministic evaluation
- Context window used: 4096 tokens for standard benchmarks
- Long-context benchmarks use 131,072 token context
- Results may vary slightly between runs due to hardware and software differences
- Benchmarks conducted in April 2026