📊 Benchmarks & Quality Report

Comprehensive performance analysis of SynapseX models

This document provides detailed benchmark results, methodology, and comparisons with state-of-the-art models. Updated with each major release.

🎯 Executive Summary

SynapseX-14B v2.0 Highlights

Metric	Score	vs Base Model	vs GPT-4o-mini
Quantum Computing Tasks	84.8%	+38.2%	+12.4%
Molecular Science Tasks	82.6%	+41.7%	+8.7%
Physics Problems	82.8%	+23.6%	+6.2%
MMLU (General)	79.2%	+0.3%	-3.4%
GSM8K (Math)	88.4%	+3.2%	-5.1%
HumanEval (Code)	78.3%	+3.6%	-10.2%

Key Insight: SynapseX delivers state-of-the-art performance in scientific domains while maintaining competitive general capabilities.

🔬 Domain-Specific Benchmarks

Quantum Computing Evaluation (QuantumBench v1.0)

Our custom benchmark suite for quantum computing capabilities:

Task Categories

Category	Questions	SynapseX-14B	Qwen2.5-14B	Δ
Circuit Design	150	84.2%	62.1%	+22.1pp
Error Correction	100	76.8%	51.3%	+25.5pp
Algorithm Design	120	89.1%	71.4%	+17.7pp
State Calculation	200	91.3%	68.9%	+22.4pp
Gate Decomposition	80	82.7%	59.2%	+23.5pp
Noise Analysis	50	78.4%	48.6%	+29.8pp
Quantum ML	100	81.2%	54.3%	+26.9pp

Example Questions

🔍 Circuit Design Example

Question: Design a quantum circuit that implements the Grover diffusion operator for a 3-qubit system. Provide the circuit in Qiskit.

Expected: Correct implementation with H gates, X gates, and multi-controlled Z gate.

SynapseX Response Quality: ✅ Correct circuit, explained steps, included visualization

🔍 Error Correction Example

Question: Explain the stabilizer formalism for the 5-qubit code and derive the logical operators.

Expected: Correct stabilizer generators, X_L and Z_L operators.

SynapseX Response Quality: ✅ Complete derivation with step-by-step explanation

Molecular Science Evaluation (MolBench v1.0)

Task Categories

Category	Questions	SynapseX-14B	Qwen2.5-14B	Δ
SMILES Prediction	200	87.4%	64.2%	+23.2pp
Retrosynthesis	150	79.8%	52.7%	+27.1pp
Property Prediction	180	83.6%	61.8%	+21.8pp
Reaction Mechanism	120	85.2%	58.3%	+26.9pp
Drug-Target	100	76.9%	48.5%	+28.4pp
Toxicity Analysis	80	79.3%	55.1%	+24.2pp

Physics Evaluation (PhysicsBench v1.0)

Category	Questions	SynapseX-14B	Qwen2.5-14B	Δ
Quantum Mechanics	150	86.7%	72.1%	+14.6pp
Statistical Mechanics	100	81.4%	65.3%	+16.1pp
Condensed Matter	80	78.9%	59.8%	+19.1pp
Electrodynamics	120	84.2%	71.6%	+12.6pp
Thermodynamics	100	82.8%	68.4%	+14.4pp

📚 Standard LLM Benchmarks

MMLU (Massive Multitask Language Understanding)

5-shot evaluation across 57 subjects:

┌────────────────────────────────────────────────────────────────────────────┐
│                              MMLU Results                                   │
├─────────────────────────────┬───────────────────────────────────────────────┤
│ Model                       │ Score                                         │
├─────────────────────────────┼───────────────────────────────────────────────┤
│ GPT-4o                      │ 88.7% ████████████████████████████████████▊   │
│ Claude 3.5 Sonnet           │ 88.3% ████████████████████████████████████▌   │
│ GPT-4o-mini                 │ 82.0% ████████████████████████████████        │
│ ★ SynapseX-14B              │ 79.2% ██████████████████████████████▊         │
│ Qwen2.5-14B-Instruct        │ 79.0% ██████████████████████████████▋         │
│ Qwen2.5-7B-Instruct         │ 75.4% ████████████████████████████            │
│ Llama-3.1-8B-Instruct       │ 69.4% █████████████████████████               │
│ Mistral-7B-Instruct-v0.3    │ 63.4% ██████████████████████                  │
└─────────────────────────────┴───────────────────────────────────────────────┘

STEM Subjects (where SynapseX excels):

Subject	SynapseX-14B	Qwen2.5-14B	GPT-4o-mini
Physics	86.2%	78.1%	81.4%
Chemistry	84.7%	76.3%	79.8%
Computer Science	82.1%	79.4%	84.2%
Mathematics	81.9%	78.6%	83.1%
Biology	79.3%	77.8%	80.6%

GSM8K (Grade School Math)

Mathematical reasoning benchmark:

┌────────────────────────────────────────────────────────────────────────────┐
│                             GSM8K Results                                   │
├─────────────────────────────┬───────────────────────────────────────────────┤
│ Model                       │ Score                                         │
├─────────────────────────────┼───────────────────────────────────────────────┤
│ GPT-4o                      │ 95.8% █████████████████████████████████████▊  │
│ GPT-4o-mini                 │ 93.2% ████████████████████████████████████▉   │
│ ★ SynapseX-14B              │ 88.4% ███████████████████████████████████     │
│ Qwen2.5-14B-Instruct        │ 85.7% █████████████████████████████████       │
│ Qwen2.5-7B-Instruct         │ 82.6% ████████████████████████████████        │
│ Llama-3.1-8B-Instruct       │ 76.6% █████████████████████████████           │
│ Mistral-7B-Instruct-v0.3    │ 58.4% ██████████████████████                  │
└─────────────────────────────┴───────────────────────────────────────────────┘

HumanEval (Code Generation)

Python code generation from docstrings:

┌────────────────────────────────────────────────────────────────────────────┐
│                           HumanEval Results                                 │
├─────────────────────────────┬───────────────────────────────────────────────┤
│ Model                       │ Pass@1                                        │
├─────────────────────────────┼───────────────────────────────────────────────┤
│ GPT-4o                      │ 90.2% █████████████████████████████████████▋  │
│ Claude 3.5 Sonnet           │ 89.0% █████████████████████████████████████   │
│ GPT-4o-mini                 │ 87.2% ████████████████████████████████████    │
│ ★ SynapseX-14B              │ 78.3% █████████████████████████████████       │
│ Qwen2.5-14B-Instruct        │ 75.6% ████████████████████████████████        │
│ Llama-3.1-8B-Instruct       │ 62.8% ████████████████████████████            │
│ Mistral-7B-Instruct-v0.3    │ 35.4% █████████████████                       │
└─────────────────────────────┴───────────────────────────────────────────────┘

MT-Bench (Multi-Turn Conversation)

Quality of multi-turn dialogue:

Model	Score	Writing	Roleplay	Reasoning	Math	Coding	Extraction	STEM	Humanities
GPT-4o	9.1	9.3	9.0	9.2	8.8	9.4	9.0	9.2	8.9
GPT-4o-mini	8.5	8.6	8.3	8.5	8.2	8.9	8.4	8.6	8.5
SynapseX-14B	8.35	8.1	8.0	8.4	8.5	8.6	8.2	8.9	8.1
Qwen2.5-7B-Instruct	8.07	8.0	7.9	8.1	8.0	8.2	8.0	8.2	8.0
Llama-3.1-8B-Instruct	8.0	7.9	7.8	8.0	7.8	8.1	8.0	8.1	8.0

⚡ Performance Benchmarks

Latency Analysis

Tested with standardized prompts (512 input tokens, 256 output tokens):

First Token Latency (TTFT)

┌─────────────────────────────────────────────────────────────────────────────┐
│                        Time to First Token (ms)                              │
├──────────────────┬─────────┬─────────┬─────────┬────────────────────────────┤
│ Provider         │   P50   │   P95   │   P99   │ Visual                     │
├──────────────────┼─────────┼─────────┼─────────┼────────────────────────────┤
│ ★ SynapseX       │   45    │   82    │  120    │ ██                         │
│ Together.ai      │   95    │  180    │  250    │ ████                       │
│ Azure OpenAI     │  120    │  280    │  420    │ █████                      │
│ Replicate        │  180    │  350    │  520    │ ███████                    │
│ GCP Vertex AI    │  220    │  480    │  680    │ █████████                  │
│ AWS Bedrock      │  280    │  520    │  780    │ ███████████                │
└──────────────────┴─────────┴─────────┴─────────┴────────────────────────────┘

Throughput (Tokens/Second)

Provider	Batch=1	Batch=4	Batch=8	Batch=16
SynapseX	120	380	680	1,100
Together.ai	85	260	420	680
Azure OpenAI	72	220	360	580
Replicate	65	180	290	460
GCP Vertex AI	48	140	220	350
AWS Bedrock	42	120	190	300

Cold Start Time

Provider	Cold Start	Warm Start	Improvement
SynapseX	8s	0.5s	Baseline
Together.ai	12s	0.8s	1.5x slower
Replicate	45s	1.2s	5.6x slower
Azure OpenAI	45s	0.9s	5.6x slower
GCP Vertex AI	165s	2.1s	20x slower
AWS Bedrock	180s	2.5s	22x slower

🧪 Methodology

Benchmark Configuration

All benchmarks were run with:

evaluation:
  temperature: 0.0  # Deterministic for reproducibility
  max_tokens: 2048
  num_runs: 3       # Average across 3 runs
  timeout: 120s
  
hardware:
  gpu: AMD Instinct MI300X
  memory: 192GB HBM3
  
environment:
  framework: vLLM 0.6.0
  precision: BF16
  batch_size: dynamic

Reproducibility

All benchmark code and results are available:

# Clone and run benchmarks
git clone https://github.com/softquantus/synapsex-benchmarks
cd synapsex-benchmarks

# Install dependencies
pip install -r requirements.txt

# Run full benchmark suite
python run_benchmarks.py \
    --model synapsex-14b \
    --benchmarks all \
    --output results/

Statistical Significance

All reported scores are averages across 3 independent runs
Error bars represent 95% confidence intervals
Differences marked as significant have p < 0.05 (paired t-test)

📈 Training Data Quality

Dataset Composition

The quality of training data directly impacts model performance:

                        Premium Dataset v1.0 Statistics
┌─────────────────────────────────────────────────────────────────────────────┐
│ Metric                          │ Value                                     │
├─────────────────────────────────┼───────────────────────────────────────────┤
│ Total Examples                  │ 764,842                                   │
│ Estimated Tokens                │ ~1.2 billion                              │
│ Average Instruction Length      │ 598.2 characters                          │
│ Average Response Length         │ 987.3 characters                          │
│ Technical Density               │ 0.78%                                     │
│ Information Density             │ 42.7%                                     │
│ Code Examples                   │ 21,795 (2.8%)                             │
│ With System Prompts             │ 371,107 (48.5%)                           │
│ Unique Sources                  │ 18 datasets                               │
│ Deduplication Rate              │ 17%                                       │
└─────────────────────────────────┴───────────────────────────────────────────┘

Quality Filtering Pipeline

Length Filter: Remove examples < 50 or > 10,000 tokens
Language Filter: English-only (detect with langdetect)
Deduplication: MinHash + semantic clustering
Density Score: Rank by information/technical density
Category Balancing: Max 10.1% per category

🆚 Competitive Analysis

vs. Foundation Models

Capability	SynapseX-14B	Qwen2.5-14B	Llama-3.1-70B	GPT-4o-mini
Quantum Computing	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Molecular Science	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Physics	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
General Knowledge	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Coding	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Multi-turn	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Cost Efficiency	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐

vs. Specialized Scientific Models

Model	Quantum	Molecular	Physics	General
SynapseX-14B	84.8%	82.6%	82.8%	79.2%
ChemLLM-7B	45.2%	78.4%	61.3%	52.1%
BioMistral-7B	38.6%	71.2%	54.8%	61.4%
SciLLM-8B	52.3%	68.9%	72.1%	65.3%

📝 Appendix

Benchmark Definitions

Benchmark	Description	Metric
MMLU	57-subject knowledge test	5-shot accuracy
GSM8K	Grade school math word problems	Exact match accuracy
HumanEval	Python code generation	Pass@1
MT-Bench	Multi-turn conversation quality	GPT-4 judge score (1-10)
QuantumBench	Custom quantum computing tasks	Task accuracy
MolBench	Custom molecular science tasks	Task accuracy
PhysicsBench	Graduate-level physics problems	Task accuracy

Version History

Version	Date	Key Changes
v2.0	Dec 2025	Premium dataset (765K), physics domain
v1.5	Sep 2025	Multi-domain expansion
v1.0	Jun 2025	Initial release, DPO training
v0.1	Mar 2025	Prototype, SFT only

📧 Contact

For benchmark questions or custom evaluations:

Technical: benchmarks@synapsex.ai
Enterprise: enterprise@synapsex.ai
Research: research@synapsex.ai

Last updated: December 2025

SynapseX — Enterprise AI for Science

🎯 Executive Summary​

SynapseX-14B v2.0 Highlights​

🔬 Domain-Specific Benchmarks​

Quantum Computing Evaluation (QuantumBench v1.0)​

Task Categories​

Example Questions​

Molecular Science Evaluation (MolBench v1.0)​

Task Categories​

Physics Evaluation (PhysicsBench v1.0)​

📚 Standard LLM Benchmarks​

MMLU (Massive Multitask Language Understanding)​

GSM8K (Grade School Math)​

HumanEval (Code Generation)​

MT-Bench (Multi-Turn Conversation)​

⚡ Performance Benchmarks​

Latency Analysis​

First Token Latency (TTFT)​

Throughput (Tokens/Second)​

Cold Start Time​

🧪 Methodology​

Benchmark Configuration​

Reproducibility​

Statistical Significance​

📈 Training Data Quality​

Dataset Composition​

Quality Filtering Pipeline​

🆚 Competitive Analysis​

vs. Foundation Models​

vs. Specialized Scientific Models​

📝 Appendix​

Benchmark Definitions​

Version History​

📧 Contact​

🎯 Executive Summary

SynapseX-14B v2.0 Highlights

🔬 Domain-Specific Benchmarks

Quantum Computing Evaluation (QuantumBench v1.0)

Task Categories

Example Questions

Molecular Science Evaluation (MolBench v1.0)

Task Categories

Physics Evaluation (PhysicsBench v1.0)

📚 Standard LLM Benchmarks

MMLU (Massive Multitask Language Understanding)

GSM8K (Grade School Math)

HumanEval (Code Generation)

MT-Bench (Multi-Turn Conversation)

⚡ Performance Benchmarks

Latency Analysis

First Token Latency (TTFT)

Throughput (Tokens/Second)

Cold Start Time

🧪 Methodology

Benchmark Configuration

Reproducibility

Statistical Significance

📈 Training Data Quality

Dataset Composition

Quality Filtering Pipeline

🆚 Competitive Analysis

vs. Foundation Models

vs. Specialized Scientific Models

📝 Appendix

Benchmark Definitions

Version History

📧 Contact