Definitive Resource · Updated 2026
Everything you need to know about LLM token compression — what it is, why it matters, how much it saves, and how to implement it with open-source tools. Includes benchmarks, comparison tables, and an interactive demo.
Token compression is the practice of reducing the number of tokens sent to a large language model (LLM) during inference — before the model processes them — while preserving the information needed to answer the current query. It is a middleware optimization that sits between your application and the LLM API.
Every LLM call processes the full prompt you send, including context that may be irrelevant to the question being asked. In agent loops, RAG pipelines, and coding assistants, context accumulates rapidly: conversation history, tool outputs, retrieved documents, and system instructions all get sent on every turn. Token compression removes the low-value tokens before they ever reach the model.
The key distinction: token compression is not summarization. Summarization rewrites context (costing an extra LLM call and risking information loss). Token compression selectively removes lines that score low against the current question, keeping the original text intact.
Token compression addresses three converging pressures on every LLM-powered application:
LLM APIs charge by the token — both input and output. If 65% of your input tokens are irrelevant to the answer, you're paying for compute that doesn't improve your results. For a typical agent making 100 calls/day, that's thousands of wasted tokens daily. At scale (1M+ calls/month), the savings from token compression reach thousands of dollars.
LLM inference time scales with input length (especially with attention's quadratic complexity in prefill). Compressing tokens before inference means faster responses. In agent loops, where context grows with every turn, token compression keeps latency from ballooning over time.
Every token processed by a GPU consumes electricity and cooling water. Data centers used 415 TWh globally in 2024 and are on track to double by 2030. Token compression directly reduces the compute needed per query. At 10M calls/month, SuperCompress avoids ~120 kg CO₂ and ~1,000 L of cooling water.
Not all token compression is equal. Here are the four main approaches, ranked by effectiveness:
Oracle recall: 100% · Latency: ~60ms · CPU only
A small trained policy scores every line against the question and keeps only the most relevant. Query-aware, model-agnostic, no extra LLM calls. This is what SuperCompress uses. Best overall.
Oracle recall: 98% · Latency: ~56ms · Attention-based
Uses attention scores to identify important tokens. Strong recall but requires access to model internals (attention weights), limiting compatibility with black-box APIs.
Oracle recall: 61% · Latency: ~63ms + LLM call · Extra cost
Uses an LLM to rewrite context. Expensive (another model call), can change facts, and loses original phrasing. Not recommended for production agent pipelines.
Oracle recall: 25% · Latency: ~57ms · Rule-based
Keeps the head and tail of context, drops the middle. Simple and cheap, but if the answer-critical line sits in the middle (which it often does), truncation loses it completely.
Compiler benchmarks measure real API-call behavior: original tokens, kept tokens, tokens removed, important context kept, and verifier risk. Fixed-ratio oracle-recall baselines remain useful for research comparisons.
| Policy | Oracle Recall | Entity Recall | Latency | KV Savings | Model Size |
|---|---|---|---|---|---|
| FIFO / Truncation | 25% | 73% | ~57 ms | ~65% | 0 (rule) |
| Summarization | 61% | 65% | ~63 ms* | ~65% | LLM call |
| H2O | 98% | 73% | ~56 ms | ~65% | attention |
| SuperCompress Best | 100% | 73% | ~60 ms | ~65% | ~5K params |
* Summarization latency excludes the extra LLM call cost. SuperCompress requires zero GPU time.
In compiler mode, SuperCompress removes as many tokens as it safely can for each API call, then reports important context kept and verifier risk:
| Context Type | Original Tokens | After Compression | Savings |
|---|---|---|---|
| Full book chapter (To Kill a Mockingbird) | ~4,200 | ~420 | ~90% |
| Long coding session log | ~1,800 | ~270 | ~85% |
| Markdown documentation | ~2,100 | ~315 | ~85% |
| Average | ~2,700 | ~335 | ~87% |
Token compression reduces both token count and the GPU prefill time that billing is based on. Here's what that means in real dollars:
| Scale | Tokens Avoided | GPT-4o Savings* | Claude Sonnet Savings* | CO₂ Avoided |
|---|---|---|---|---|
| 1 day (100 calls) | ~80K | ~$0.20 | ~$0.24 | ~0.001 kg |
| 1 month (3K calls) | ~2.4M | ~$6.00 | ~$7.20 | ~0.04 kg |
| 1M calls/month | ~800M | ~$2,000 | ~$2,400 | ~12 kg |
| 10M calls/month | ~8B | ~$20,000 | ~$24,000 | ~120 kg |
* Estimated based on published API pricing and SuperCompress assumptions. Your savings depend on your average context length and query patterns.
Token compression is most valuable in these scenarios:
Token compression can be added to any LLM pipeline with minimal code changes. Here's how to integrate SuperCompress:
# Install: pip install supercompress
from supercompress import Compressor
compressor = Compressor()
# Compiler compression — no budget required
result = compressor.compress(
context=long_prompt,
query="What caused the production incident?"
)
print(f"Removed {result.tokens_removed} tokens ({result.savings_pct:.0f}% savings)")
print(f"Compressed text:\n{result.compressed_text}")curl -X POST https://supercompress.dev/api/v1/compress \
-H "X-API-Key: sc_live_your_key" \
-H "Content-Type: application/json" \
-d '{
"context": "Your long prompt here...",
"query": "What should the model answer?"
}'from supercompress import Compressor
from langchain.callbacks import BaseCallbackHandler
class CompressionCallback(BaseCallbackHandler):
def __init__(self):
self.compressor = Compressor()
def on_llm_start(self, serialized, prompts, **kwargs):
# Compress prompt before sending to LLM
compressed = self.compressor.compress(
context=prompts[0].text,
query="Continue the conversation."
)
prompts[0].text = compressed.compressed_text
return promptsSuperCompress is an open-source (MIT) learned token compression engine. It uses a ~5K-parameter policy to score every line of context against your question and keeps only the lines that matter for the answer. It runs entirely on CPU, adds ~60ms latency, and requires zero GPU time.
Paste your long prompts and see exactly how much can be removed while keeping what matters. Free, no signup needed.
Open the Playground → Get your API keyYes. Token compression operates on the text before it's sent to the model. It works with OpenAI, Anthropic, Google, open-weight models, local models — any LLM that accepts text input.
Not when done correctly. SuperCompress achieves 100% oracle recall on benchmark seeds, meaning every line that contains answer-critical information is preserved. The model receives the same signal — just with less noise.
Prompt engineering changes how you write prompts. Token compression reduces how much context you send. They're complementary: good prompt engineering + token compression = best results.
No. KV cache eviction happens during inference inside the model. Token compression happens before inference, on the text itself. Both reduce memory and compute, but at different stages of the pipeline.
Yes. SuperCompress compresses the full context before the streaming LLM call begins. The compressed text is sent as the prompt, and the response streams back normally.
No. SuperCompress runs entirely on CPU. The model is only ~5K parameters — tiny enough to run in <10KB of memory. It compresses before GPU inference, so it doesn't compete for GPU resources.
The Python library and browser demo are free (MIT license). The hosted API has a free tier (100K tokens/month) and paid plans starting at $10/month for expanded usage.
Related: Benchmarks · Interactive Comparison · Research & Citation · GitHub