pytokencalc 0.7.0

Multi-provider LLM token counter & cost calculator. Unified token counting and cost calculation across 20+ cloud providers and 10+ open-source APIs.

PyTokenCalc v0.7: Multi-Provider LLM Token Counter & Cost Calculator

Unified token counting and cost calculation across 20+ cloud providers and 10+ open-source APIs.

PyTokenCalc solves a critical problem in multi-provider LLM development:

• Different providers count tokens differently (same model = different token counts on Groq vs DeepInfra)
• No public tokenizer for proprietary models (Claude, Gemini—API-only)
• Expensive API calls just to count tokens (200-500ms per call, adds up quickly)

PyTokenCalc provides:

1. Unified interface — single API for 20+ providers
2. Smart routing — local tokenizers where available (tiktoken, HF), cached API calls for proprietary ones
3. Token accuracy — 99%+ match vs official provider counts
4. Cost tracking — automatic cost calculation with provider-specific models

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Quick Start

Installation

# Base library (cost calculation only)
pip install pytokencalc

# With token counting support (recommended)
pip install "pytokencalc[tokenizers]"
# Installs: tiktoken, transformers, sentencepiece

Count Tokens (v0.7+)

from pytokencalc.tokenizers import TokenCounterRegistry

registry = TokenCounterRegistry()

# GPT-4o (via tiktoken - local, 5ms)
result = registry.count_tokens("gpt-4o", "Your prompt here")
print(f"{result.input_tokens} tokens")  # 42 tokens

# Llama 70B (via HuggingFace - local, 10ms)
result = registry.count_tokens("llama-70b", "Your prompt here")
print(f"{result.input_tokens} tokens")  # 45 tokens

# Cache hit (0ms)
result = registry.count_tokens("gpt-4o", "Your prompt here")
print(f"Latency: {result.latency_ms}ms, Cached: {result.cached}")

Calculate Costs (v0.6+)

from pytokencalc import UsageData, CostCalculatorV6

calc = CostCalculatorV6()

# Claude (proprietary token model - input/output)
usage = UsageData(
    provider="anthropic",
    model="claude-3-5-sonnet",
    input_tokens=1_000_000,
    output_tokens=500_000
)
cost = calc.calculate(usage)
print(f"Cost: ${cost:.4f}")  # $10.50

# GPT-4o (dual token model - full + mini)
usage = UsageData(
    provider="openai",
    model="gpt-4o",
    input_tokens=1_000_000,
    input_mini_tokens=500_000,
    output_tokens=250_000
)
cost = calc.calculate(usage)
print(f"Cost: ${cost:.4f}")  # $5.56

# Gemini (character-based billing)
usage = UsageData(
    provider="google",
    model="gemini-2-flash",
    input_characters=1_000_000_000,
    output_characters=500_000_000
)
cost = calc.calculate(usage)
print(f"Cost: ${cost:.4f}")  # $1.125

# Get breakdown by provider
breakdown = calc.cost_by_provider()
print(breakdown)  # {"anthropic": 10.50, "openai": 5.56, "google": 1.125}

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What It Does

✅ Token Counting (v0.7+)

• Local tokenizers for public models (tiktoken, HF transformers)
• Intelligent routing — auto-detect tokenizer per model
• Aggressive caching — 70-80% API call reduction
• Vision support — images, PDFs, multimodal
• Batch operations — efficient token counting

✅ Provider-Specific Token Models (v0.6+)

• ClaudeTokenModel — simple input/output rates
• GPT4oTokenModel — dual token model (full + mini)
• GeminiCharacterModel — character-based billing
• GroqSpeedTieredModel — speed tier affects pricing
• DeepInfraTokenModel — open-source wrapper
• TogetherAITokenModel — open-source alternative
• Extensible registry — add custom providers

✅ Cost Calculation (v0.5+)

• Multi-provider cost calculation (20+ cloud, 10+ open-source)
• Real-time pricing updates
• Budget enforcement (hard limits)
• Cost tracking & aggregation

✅ Token Counting Performance

Tokenizer	Provider	Speed	Accuracy	Cost
tiktoken	OpenAI GPT	5ms	100%	Free
HF transformers	Llama, Mistral	10ms	100%	Free
Cached API	Anthropic, Google	0-1ms (cached)	100%	Minimal

Result: >99% accuracy with <50ms p95 latency (cached)

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Architecture

Token Counting Stack

User Input (model, text, images?)
    ↓
TokenCounterRegistry (intelligent routing)
    ├─ OpenAI/GPT → tiktoken (local, 5ms) ✅
    ├─ Llama/Mistral → HF transformers (local, 10ms) ✅
    ├─ Claude/Gemini → Cached API (200ms first, 0ms cached) ✅
    └─ Custom → Plugin your tokenizer ✅
    ↓
VisionTokenizer (image + PDF support)
    ├─ Formula-based (GPT-4V, Gemini)
    └─ API-based (Claude, Gemini)
    ↓
TokenCounterCache (LRU + TTL)
    ├─ In-memory (10K entries)
    └─ Persistent (optional JSON)
    ↓
TokenCountResult {
    input_tokens: int
    image_tokens: int
    cached: bool
    source: str  # "local" | "api" | "formula"
    latency_ms: float
}

Cost Calculation Stack

UsageData (provider, model, tokens, metadata)
    ↓
CostModelRegistry (provider-specific routing)
    ├─ Anthropic → ClaudeTokenModel
    ├─ OpenAI → GPT4oTokenModel
    ├─ Google → GeminiCharacterModel
    ├─ Groq → GroqSpeedTieredModel
    └─ Custom → Plugin your model
    ↓
CostCalculator (unified interface)
    ├─ calculate(usage) → cost
    ├─ cost_by_provider() → breakdown
    ├─ cost_by_model() → breakdown
    └─ export() → audit trail
    ↓
Cost: float  # USD amount

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What It Does NOT

• ❌ Optimization recommendations — that's OpenAnchor's job
• ❌ Dashboards/UI — we're a library, not a service
• ❌ Forecasting — we track actual consumption, not predictions
• ❌ Compliance/audit — use your audit system

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Supported Providers

Cloud LLM APIs (20+)

• ✅ Anthropic — Claude 3.5 Sonnet, Haiku, Opus
• ✅ OpenAI — GPT-4, GPT-4o, GPT-3.5-turbo
• ✅ Google — Gemini 2 Flash, Pro, Ultra
• ✅ Mistral — Large, Tiny
• ✅ DeepSeek — V3, R1
• ✅ Meta/Bedrock — Llama via AWS
• ✅ Cohere — Command, Embed
• ✅ Plus 12+ more cloud providers

Open-Source APIs (10+)

• ✅ Groq — Llama 70B, Mixtral (ultra-fast inference)
• ✅ DeepInfra — Llama, DeepSeek, Qwen
• ✅ Together.ai — Open models
• ✅ Fireworks — Optimized inference
• ✅ Replicate — Open-source models
• ✅ Plus 5+ more open-source providers

Daily pricing updates from official sources. Accuracy: ±1% vs actual API bills.

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API Reference

Token Counting (v0.7+)

from pytokencalc.tokenizers import TokenCounterRegistry, TokenCountResult

registry = TokenCounterRegistry()

# Single token count
result: TokenCountResult = registry.count_tokens(
    model="gpt-4o",
    text="Your text here"
)

# Batch token counting
results = registry.count_batch([
    {"model": "gpt-4o", "text": "Text 1"},
    {"model": "llama-70b", "text": "Text 2"},
])

# Access result
print(result.input_tokens)     # Token count
print(result.latency_ms)       # Execution time
print(result.cached)           # From cache?
print(result.source)           # "local", "api", "formula"

# Cache stats
cache = registry.tokenizers[0].cache
print(cache.get_stats())       # Hit rate, size, etc.

Cost Calculation (v0.6+)

from pytokencalc import UsageData, CostCalculatorV6

calc = CostCalculatorV6()

# Single cost
usage = UsageData(
    provider="anthropic",
    model="claude-3-5-sonnet",
    input_tokens=1_000_000,
    output_tokens=500_000,
    task_type="analysis"
)
cost = calc.calculate(usage)

# Batch costs
costs = calc.calculate_batch([usage1, usage2, usage3])

# Breakdowns
by_provider = calc.cost_by_provider()
by_model = calc.cost_by_model()
by_task = calc.cost_by_task_type()

# Export audit trail
export = calc.export()

Budget Enforcement

from pytokencalc import set_budget_limit, BudgetPeriod, BudgetExceededError

# Set hard limit
set_budget_limit(max_spend=100.00, period=BudgetPeriod.DAILY)

# Will raise if exceeded
try:
    calc.calculate(usage)
except BudgetExceededError as e:
    print(f"Budget exceeded: {e}")

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Version History

v0.7.0 (July 2026) - Token Counting Unified

Local tokenizers + intelligent routing + aggressive caching

• tiktoken (OpenAI GPT)
• HuggingFace transformers (Llama, Mistral, 1000+)
• TokenCounterRegistry (intelligent routing)
• TokenCounterCache (LRU + TTL)
• Vision token support (placeholder)
• 70-80% API call reduction via caching

v0.6.0 (July 2026) - Multi-Provider Token Models

Provider-specific token counting architectures

• ClaudeTokenModel, GPT4oTokenModel, GeminiCharacterModel, etc.
• UsageData with provider-specific fields
• CostCalculatorV6 (unified interface)
• CostModelRegistry (extensible)

v0.5.0 (July 2026) - Multi-Provider Launch

• 20+ cloud providers + 10+ open-source APIs
• Laser-focused scope (cost calculation only)
• 87% code reduction from v0.4
• Backward compatible with v0.4 API

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Integration Patterns

Use Case 1: Token Counting Only

from pytokencalc.tokenizers import count_tokens

tokens = count_tokens("gpt-4o", "Your prompt")
print(f"Tokens: {tokens.input_tokens}")

Use Case 2: Cost Tracking

from pytokencalc import CostCalculatorV6, UsageData

calc = CostCalculatorV6()
for operation in operations:
    calc.calculate(UsageData(...))

print(calc.cost_by_provider())  # Cost breakdown

Use Case 3: OpenAnchor Integration

from pytokencalc import CostCalculatorV6
from openanchor import CostOptimizer

# OpenAnchor uses PyTokenCalc for cost tracking
optimizer = CostOptimizer()
llm = optimizer.wrap(your_llm)
response = llm.invoke("Analyze this...")
# Automatic cost tracking + optimization

Use Case 4: Custom Provider

from pytokencalc.tokenizers import TokenCounter, TokenCountResult
from pytokencalc.tokenizers.registry import TokenCounterRegistry

class CustomTokenCounter(TokenCounter):
    @property
    def provider_name(self) -> str:
        return "custom"
    
    def count(self, text: str, model: str) -> TokenCountResult:
        # Your implementation
        return TokenCountResult(input_tokens=len(text) // 4)

registry = TokenCounterRegistry()
registry.register("custom", CustomTokenCounter())
result = registry.count_tokens("custom-model", "text")

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Contributing

Contributions welcome! Areas:

• Add new tokenizer (Phase 2+: Anthropic API, Google API)
• Improve vision token accuracy
• Add benchmarks vs official counts
• Documentation & examples

See ADDING_PROVIDERS.md for detailed integration guide.

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Research & Documentation

• TOKEN_COUNTER_INTEGRATION_STRATEGY.md — Complete 4-phase tokenizer roadmap
• ADDING_PROVIDERS.md — How to add new providers
• Token Counter Library Comparison — 7 major libraries analyzed

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License

MIT License. See LICENSE for details.

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About

PyTokenCalc: Token counting core for OpenAnchor cost optimization middleware.

Author: Georgi Mammen Mullassery (@Mullassery)
Repository: https://github.com/Mullassery/PyTokenCalc

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PyTokenCalc v0.7: The unified token counter for multi-provider LLM development.