diff --git a/docs/my-website/docs/completion/prompt_compression.md b/docs/my-website/docs/completion/prompt_compression.md
index 0ea679fb6b7..2d999291af6 100644
--- a/docs/my-website/docs/completion/prompt_compression.md
+++ b/docs/my-website/docs/completion/prompt_compression.md
@@ -46,7 +46,7 @@ response = litellm.completion(
 - `messages` (`List[dict]`, required): input conversation messages
 - `model` (`str`, required): model name used for token counting
 - `compression_trigger` (`int`, default `200000`): compress only if input token count exceeds this
-- `compression_target` (`Optional[int]`, default `compression_trigger // 2`): desired post-compression token budget
+- `compression_target` (`Optional[int]`, default `70% of compression_trigger`): desired post-compression token budget
 - `embedding_model` (`Optional[str]`): if set, combines BM25 + embedding relevance scoring
 - `embedding_model_params` (`Optional[dict]`): additional kwargs passed to `litellm.embedding()`
 - `compression_cache` (`Optional[DualCache]`): optional cache used by embedding scoring
@@ -70,9 +70,53 @@ args = json.loads(tool_call.function.arguments)
 full_content = compressed["cache"][args["key"]]
 ```
 
-## Evaluate Compression Quality
+## Performance
 
-You can benchmark baseline vs compressed behavior with:
+Benchmarked on [SWE-bench Lite](https://huggingface.co/datasets/princeton-nlp/SWE-bench_Lite_bm25_27K) (real GitHub issues with ~27k tokens of BM25-retrieved repo context per problem).
+
+### Claude Opus — 5 problems, trigger=10k
+
+| Metric | Baseline | Compressed | Delta |
+|---|---|---|---|
+| File overlap | 1.000 | 1.000 | +0.000 |
+| Exact file match | 100% | 100% | +0.0% |
+| Hunk overlap | 0.582 | 0.361 | -0.221 |
+| Content similarity | 0.367 | 0.373 | +0.006 |
+| Avg prompt tokens | 30,828 | 6,890 | -77.7% |
+| Avg cost/problem | $0.488 | $0.136 | **-72.0%** |
+
+**Key takeaways:**
+
+- **File-level targeting is fully preserved** — the model edits the same files with or without compression.
+- **Content similarity matches baseline** — the actual lines changed are comparable.
+- **Hunk overlap drops modestly** (-0.221) — the model targets the right files but may edit slightly different line ranges with less surrounding context.
+- **72% cost savings** with 78% token reduction.
+
+### Metrics explained
+
+| Metric | What it measures |
+|---|---|
+| **File overlap** | Fraction of gold-patch files present in the generated patch |
+| **Exact file match** | Whether the generated patch touches exactly the same set of files |
+| **Hunk overlap** | Fraction of gold hunk line ranges covered by generated hunks |
+| **Content similarity** | Jaccard similarity of changed lines (added/removed) between gold and generated patches |
+
+### Running the SWE-bench eval
+
+```bash
+# 5-problem quick check
+python tests/eval_swe_bench.py --model claude-opus-4-20250514 --problems 5
+
+# Custom trigger/target
+python tests/eval_swe_bench.py --model gpt-4o --problems 20 \
+    --compression-trigger 15000 --compression-target 10000
+
+# With embedding scoring
+python tests/eval_swe_bench.py --model gpt-4o --problems 10 \
+    --embedding-model text-embedding-3-small
+```
+
+### Running the HumanEval-style eval
 
 ```bash
 python scripts/eval_compression.py --model gpt-4o --problems 5