prompt-complexity-analyzer

ML-powered prompt complexity analyzer for LLM routing. Scores any prompt 1–10 and recommends the right model tier — no API calls, runs locally.

Install

pip install prompt-complexity-analyzer

Quick start

from prompt_complexity_analyzer import complexity

r = complexity("prove P≠NP")
print(r.score)    # 9.2
print(r.tier)     # capable
print(r.model)    # claude-opus-4-6
print(r)          # Score 9.2/10 | Tier: capable | Model: claude-opus-4-6 | Backend: ml
r.explain()       # full breakdown with dimension scores

Score tiers

Providers

r = complexity("your prompt", provider="openai")    # gpt-4o-mini / gpt-4o / o1
r = complexity("your prompt", provider="google")    # gemini-2.0-flash / pro / 2.5-pro
r = complexity("your prompt", provider="ollama")    # qwen3:1.7b / 14b / 32b
r = complexity("your prompt", provider="anthropic") # default

Result object

r = complexity("Compare ECDH vs RSA for TLS 1.3")

r.score          # float 1–10
r.tier           # "fast" | "balanced" | "capable"
r.model          # recommended model string for the chosen provider
r.label          # "Fast / Lightweight" | "Balanced" | "High Capability"
r.backend        # "ml" | "heuristic"
r.dimensions     # {"Reasoning Depth": 8.5, "Domain Specificity": 9.0, ...}
r.flags          # advisory messages, e.g. ["⚠ Very high complexity"]

# Fuzzy key access — substring matches dimension names
r["reasoning"]   # Reasoning Depth score
r["domain"]      # Domain Specificity score
r["multi"]       # Multi-part score
r["ambiguity"]   # Ambiguity score
r["output"]      # Output Complexity score

# Serialization
r.to_dict()      # plain dict
r.to_json()      # JSON string

explain() output

  ────────────────────────────────────────────────────────────
  Score        8.1 / 10
  Tier         High Capability
  Model        claude-opus-4-6  [anthropic]
  Backend      ml
  Use when     Complex research, deep reasoning, ambiguous high-stakes tasks
  ────────────────────────────────────────────────────────────

  Dimension                Bar               Score
  ──────────────────────────────────────────────────────────
  Vocabulary               ████░░░░░░░░░░     4.5
  Multi-part               ████████░░░░░░     7.0
  Reasoning Depth          █████████░░░░░     8.5
  Domain Specificity       █████████░░░░░     9.0
  Ambiguity                ███░░░░░░░░░░░     2.0
  Output Complexity        █████░░░░░░░░░     4.5

CLI

# Full breakdown
prompt_complexity_analyzer -p "your prompt"

# Single field
prompt_complexity_analyzer --only score -p "your prompt"
prompt_complexity_analyzer --only tier -p "your prompt"
prompt_complexity_analyzer --only model -p "your prompt"
prompt_complexity_analyzer --only reasoning -p "your prompt"

# Provider
prompt_complexity_analyzer --provider openai -p "your prompt"

# JSON output
prompt_complexity_analyzer --json -p "your prompt"

# Stdin
echo "your prompt" | prompt_complexity_analyzer

Load a custom ML model

from prompt_complexity_analyzer import set_model, complexity

set_model("./my_model.joblib")   # call once at startup
r = complexity("your prompt")
print(r.backend)  # ml

# Or per-call
r = complexity("your prompt", model_path="./my_model.joblib")

Semantic embeddings

The package uses sentence-transformers (all-MiniLM-L6-v2) for higher-accuracy scoring. The embedding model loads automatically on import if sentence-transformers is installed (it is — it's a hard dependency). The first run downloads ~80MB, cached locally after that.

from prompt_complexity_analyzer import load_embedding_model, complexity

load_embedding_model()   # explicit load — optional, auto-loads on import
r = complexity("your prompt")

Feature dimensions