# Technical Data Extraction: Normalized Latency Comparison
## 1. Image Overview
This image is a grouped bar chart comparing the normalized latency of four different Large Language Model (LLM) inference and programming frameworks across eleven distinct benchmarks/tasks.
## 2. Component Isolation
### Header (Legend)
The legend is positioned at the top center of the image.
* **SGLang** (Orange/Coral): `[x: ~30%, y: top]`
* **vLLM** (Green): `[x: ~45%, y: top]`
* **Guidance** (Blue): `[x: ~60%, y: top]`
* **LMQL** (Grey): `[x: ~75%, y: top]`
### Main Chart Area
* **Y-Axis Title:** Latency (Normalized)
* **Y-Axis Markers:** 0.0, 0.2, 0.5, 0.8, 1.0
* **X-Axis Categories (Benchmarks):**
1. MMLU
2. ReAct Agents
3. Generative Agents
4. Tree of Thought
5. Skeleton of Thought
6. LLM Judge
7. HellaSwag
8. JSON Decoding
9. Multi-Turn Chat(short)
10. Multi-Turn Chat(long)
11. DSPy RAG Pipeline
## 3. Data Extraction and Trend Analysis
The chart uses normalized values where the slowest framework for a given task typically represents the baseline of 1.0. Lower bars indicate better performance (lower latency).
### Trend Verification by Series
* **SGLang (Orange):** Consistently the lowest or near-lowest latency across all benchmarks. It shows significant performance leads in complex reasoning tasks like "Tree of Thought" and "HellaSwag."
* **vLLM (Green):** Generally higher latency than SGLang but lower than Guidance/LMQL in early benchmarks. It becomes the baseline (1.0) for the final three chat and RAG benchmarks.
* **Guidance (Blue):** Mid-range latency. It is absent from the final three benchmarks.
* **LMQL (Grey):** Consistently the highest latency (1.0) for the first seven benchmarks, after which it is absent from the data.
### Reconstructed Data Table (Approximate Values)
| Benchmark | SGLang (Orange) | vLLM (Green) | Guidance (Blue) | LMQL (Grey) |
| :--- | :---: | :---: | :---: | :---: |
| **MMLU** | ~0.15 | ~0.28 | ~0.25 | 1.0 |
| **ReAct Agents** | ~0.12 | ~0.18 | ~0.15 | 1.0 |
| **Generative Agents** | ~0.20 | ~0.22 | ~0.35 | 1.0 |
| **Tree of Thought** | ~0.05 | ~0.18 | ~0.52 | 1.0 |
| **Skeleton of Thought** | ~0.10 | ~0.35 | ~0.42 | 1.0 |
| **LLM Judge** | ~0.08 | ~0.30 | ~0.38 | 1.0 |
| **HellaSwag** | ~0.05 | ~0.42 | N/A | 1.0 |
| **JSON Decoding** | ~0.25 | 1.0 | ~0.45 | N/A |
| **Multi-Turn Chat(short)** | ~0.80 | 1.0 | N/A | N/A |
| **Multi-Turn Chat(long)** | 1.0 | ~0.98 | N/A | N/A |
| **DSPy RAG Pipeline** | ~0.70 | 1.0 | N/A | N/A |
## 4. Key Observations
* **Framework Availability:** LMQL and Guidance data points are missing for the more complex/recent benchmarks (JSON Decoding through DSPy RAG Pipeline).
* **SGLang Performance:** SGLang demonstrates a substantial latency reduction (often >5x faster) compared to the baseline (LMQL) in the first seven tasks.
* **Scaling:** In "Multi-Turn Chat(long)", SGLang and vLLM perform almost identically, with SGLang slightly higher, whereas in "DSPy RAG Pipeline", SGLang regains a lead over vLLM.
