LLM API Token Cost Calculator: Claude, GPT, Gemini & DeepSeek Pricing (2026)

Compare daily and monthly inference costs across frontier and open-weight LLM APIs with input/output token sliders, system prompt caching discounts, and batch API savings.

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2026 AI Chatbot & LLM API Token Cost Calculator — Interactive Console

Runs locally in your browser • Instant output

Daily Requests2,500

Avg Input Tokens / Call

Avg Output Tokens / Call

Prompt Cache Hit %40%

LLM Model	Rates (In / Out 1M)	Cost / 1K Calls	Daily Spend	Monthly (30-Day)
Gemini 2.5 Flash Lowest Cost	$0.15 / $0.60	$0.459	$1.15	$34.42
GPT-4o mini	$0.15 / $0.60	$0.486	$1.22	$36.45
DeepSeek R1 / V3	$0.27 / $1.10	$0.837	$2.09	$62.78
Claude 3.5 Haiku	$0.80 / $4.00	$2.722	$6.80	$204.12
Gemini 2.5 Pro	$1.25 / $10.00	$6.073	$15.18	$455.49
GPT-4o	$2.50 / $10.00	$8.100	$20.25	$607.50
Claude 3.7 Sonnet	$3.00 / $15.00	$10.206	$25.52	$765.45

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2026 LLM API Token Pricing, Context Caching & Word-to-Token Benchmark Table

2026 Verified Reference

Quick Answer & 2026 Technical Summary (llm api token cost calculator)Updated 2026 Standard

In English prose and source code, `1,000 tokens` equals approximately `750 words` (`1 token ≈ 0.75 words` or `4 characters`). In 2026, enabling Prompt Context Caching reduces repeated system prompt and RAG document input token costs by 50%–90%, while async Batch APIs cut both input and output costs by 50%.

Monthly API Cost ($) = ((Input_Tokens × Input_Rate) + (Cached_Tokens × Cache_Rate) + (Output_Tokens × Output_Rate)) / 1,000,000

Token Conversion Rule: 1M Tokens ≈ 750,000 English words (~1,500 pages)

Prompt Caching Discount: 50% to 90% off repeated prefix tokens

Batch Async API Discount: 50% off standard synchronous rates (24h SLA)

2026 Frontier / Flash Model Tier	Input Cost / 1M Tokens	Cached Input / 1M Tokens	Output Cost / 1M & Context Window
OpenAI GPT-4o (Multimodal Flagship)	`$2.50 / 1M`	$1.25 / 1M (50% Auto-Cache)	$10.00 / 1M (128k Context Window)
OpenAI GPT-4o mini (High-Volume)	`$0.15 / 1M`	$0.075 / 1M (50% Auto-Cache)	$0.60 / 1M (128k Context Window)
Anthropic Claude 3.7 Sonnet	`$3.00 / 1M`	$0.30 / 1M (90% Cache Read)	$15.00 / 1M (200k Context + Thinking)
Google Gemini 2.5 Pro	`$1.25 / 1M (≤200k)`	$0.31 / 1M (Context Cache)	$10.00 / 1M (Up to 2M Token Context)
Google Gemini 2.5 Flash	`$0.15 / 1M`	$0.0375 / 1M (75% Cache Save)	$0.60 / 1M (1M Token Context Window)
DeepSeek V3 / R1 (Open-Weight API)	`$0.27 – $0.55 / 1M`	$0.07 – $0.14 / 1M (Disk Cache)	$1.10 – $2.19 / 1M (64k–128k Context)

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How to Use 2026 AI Chatbot & LLM API Token Cost Calculator

Set Daily Request Volume

Adjust the Daily API Requests slider to match your expected traffic.

Configure Average Input & Output Tokens

Enter average prompt input tokens (including RAG context) and completion output tokens.

Set Prompt Cache Hit Rate (%)

Specify what percentage of input tokens come from cached system prompts.

Compare Monthly Cost Table

Review the sorted comparison table from lowest cost to flagship frontier models.

Key Capabilities & Technical Architecture

Side-by-Side Multi-Model Pricing Matrix

Compares per-request, daily, and monthly API spend across frontier reasoning models and fast flash/mini models simultaneously.

Context / Prompt Cache Hit Discount Modeling

Models 75%–90% input token savings when repeated system prompts or RAG documents hit prompt cache.

Word-to-Token & Character Converter

Translates average English word counts into exact BPE token estimates (1 token ≈ 0.75 words).

Unit Economics Per-User Cost Breakdown

Shows exact cost per 1,000 requests so SaaS founders can price subscriptions profitably.

Practical Use Cases

AI SaaS Margin & Pricing Architecture

Forecast monthly API bills at 1,000, 10,000, or 100,000 daily user queries before launching.

Model Routing Optimization

Calculate how much you save by routing 80% of classification tasks to Flash/Mini models and 20% to Pro/Sonnet models.

Frequently Asked Questions (FAQs)

Why are output tokens more expensive than input tokens in LLM APIs?+

Input tokens are processed in parallel during the prefill phase (compute-bound), whereas output tokens are generated sequentially one token at a time during decoding (memory-bandwidth bound), consuming significantly more GPU time per token.

How many tokens is 1,000 English words?+

In modern Byte-Pair Encoding (BPE) tokenizers, 1 token averages roughly 4 characters or 0.75 words. Therefore, 1,000 English words equals approximately 1,330 tokens.

How does Prompt Caching reduce LLM API bills?+

When you send the same large system prompt, codebase, or document prefix across multiple requests, providers cache the precomputed KV attention states and discount cached input tokens by 75% to 90%.

When should I switch from API calls to self-hosted GPUs?+

Pay-per-token APIs are almost always cheaper for spiky or low-to-medium traffic, while dedicated GPU instances become cost-effective only when sustained 24/7 GPU utilization exceeds ~50–60%.

Can I export the cost comparison table?+

Yes, click Copy Output or Download to save the complete monthly breakdown.