Token Optimization Production Llm Cost Guide 2026

Date: 2026-05-01
Track: paper-poc
Slug: token-optimization-production-llm-cost-guide-2026
Environment: macOS Darwin 24.6.0, Python 3.x (stdlib only — no external deps)

Purpose

Reproduce and quantify three core token optimization techniques drawn from published research:

1. Semantic caching (arXiv 2411.05276 — GPT Semantic Cache)
2. Context/conversation history trimming
3. RAG chunk pruning (LLMLingua paper concept, EMNLP 2023)

No production API keys were used. All runs are local simulations using word-overlap Jaccard similarity as a proxy for embedding cosine similarity (production deployments use real embedding vectors at 0.80–0.95 cosine threshold).

Commands Run

python3 -c "
import re

def approx_token_count(text):
    return max(1, len(text) // 4)

def simple_prune(text, budget_tokens):
    sentences = re.split(r'(?<=[.!?]) +', text)
    scored = sorted(sentences, key=len, reverse=True)
    kept = []
    total = 0
    for s in scored:
        tc = approx_token_count(s)
        if total + tc <= budget_tokens:
            kept.append(s)
            total += tc
    return ' '.join(kept), total

def cosine_sim_proxy(q1, q2):
    w1 = set(q1.lower().split())
    w2 = set(q2.lower().split())
    if not w1 or not w2:
        return 0.0
    return len(w1 & w2) / len(w1 | w2)

THRESHOLD = 0.15
cache = {}
queries = [
    'How does the Transformer attention mechanism work?',
    'How does the Transformer attention work?',
    'What is gradient descent?',
    'What is gradient descent in machine learning?',
    'Explain BERT pre-training objectives',
]
hits, misses = 0, 0
for q in queries:
    match = None
    for cached_q in cache:
        sim = cosine_sim_proxy(q, cached_q)
        if sim >= THRESHOLD:
            match = cached_q
            break
    if match:
        hits += 1
        print(f'HIT  sim={cosine_sim_proxy(q,match):.2f}: \"{q}\"')
    else:
        cache[q] = f'[response]'
        misses += 1
        print(f'MISS: \"{q}\"')

print(f'Cache hit rate: {hits}/{len(queries)} = {hits/len(queries)*100:.0f}%')
# ... (conversation trimming + RAG pruning sections)
"

Output

MISS: "How does the Transformer attention mechanism work?"
HIT  sim=0.86: "How does the Transformer attention work?"
MISS: "What is gradient descent?"
HIT  sim=0.38: "What is gradient descent in machine learning?"
MISS: "Explain BERT pre-training objectives"
Cache hit rate: 2/5 = 40%
API calls saved: 2/5

=== Conversation history trimming ===
Full history context: ~97 tokens
Last-1-turn context:  ~46 tokens
Reduction: 53%

=== RAG Context Pruning ===
Full RAG chunk: ~175 tokens
Pruned to budget (40 tokens): ~40 tokens (77% reduction)

Findings

What Worked

• Word-overlap Jaccard similarity correctly identified near-duplicate queries (sim=0.86 for "How does the Transformer attention mechanism work?" vs "How does the Transformer attention work?")
• Greedy sentence pruning by character length approximates importance reasonably for short factual chunks
• Conversation trimming to last-N turns is a zero-cost optimization requiring only tail-slicing the history array

Limitations

• Token count is approximated at 4 chars/token (production: use tiktoken for exact BPE counts)
• Jaccard similarity is a weak proxy — production requires sentence-transformer or OpenAI embedding vectors with cosine similarity threshold 0.80–0.95
• Sentence-length-based pruning does not model semantic relevance to the query (LLMLingua uses a small LM perplexity scorer)
• No API calls made, so real latency/cost numbers are not available from this run

Paper References

• LLMLingua (EMNLP 2023): arXiv 2310.05736 — 20x compression, 1.5% accuracy loss on GSM8K
• LongLLMLingua (arXiv 2310.06839) — 21.4% NaturalQuestions improvement at 4x compression
• GPT Semantic Cache (arXiv 2411.05276) — 68.8% API call reduction, >97% positive hit accuracy
• Speculative Decoding (Chen et al. 2023, arXiv 2302.01318) — 2-3x latency reduction, lossless output distribution