LLM Prompt Token & Model Pricing Calculator
HOTCalculate AI prompt tokens and compare monthly API pricing across GPT-6, Claude 5.5, and Gemini 4.
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(CLS Prevention Active · Zero Shift)50% of input tokens will be billed at discounted prompt caching rates.
Adjust the exchange rate to view accurate local currency cost estimates.
Custom Pricing Override & Simulator
$11.75 / mo
Per request: $0.00059
$1,130.00 / mo
Per request: $0.05650
| Model | Provider | Per Request | Monthly Total | 1M Pricing (In / Out) | Cached In |
|---|---|---|---|---|---|
GPT-6 LunaSpeed | OpenAI | $0.00059 | $11.75 | $0.1 / $0.5 | $0.025 |
Gemini 3.5 Flash-Lite | $0.00249 | $49.90 | $0.3 / $2.5 | $0.03 | |
Gemini 3.8 FlashBest Value | $0.00424 | $84.75 | $0.75 / $3.75 | $0.075 | |
Gemini 3.6 Flash | $0.00424 | $84.75 | $0.75 / $3.75 | $0.075 | |
Custom Custom ModelEditable | Custom | $0.00678 | $135.60 | $1.2 / $6 | $0.12 |
Claude Sonnet 4.6 | Anthropic | $0.00847 | $169.50 | $1.5 / $7.5 | $0.15 |
Claude Sonnet 5.5Balanced | Anthropic | $0.01115 | $223.00 | $2 / $10 | $0.1 |
Gemini 4 ArgonUltra | $0.01115 | $223.00 | $2 / $10 | $0.1 | |
GPT-6.1 Sol | OpenAI | $0.01175 | $235.00 | $2 / $10 | $0.5 |
Gemini 3.1 Pro | $0.01290 | $258.00 | $2 / $12 | $0.2 | |
GPT-5.2 Codex | OpenAI | $0.01450 | $290.00 | $1.75 / $14 | $0.45 |
GPT-5.6 Sol | OpenAI | $0.02030 | $406.00 | $4 / $16 | $1 |
Claude Opus 5.5Pro | Anthropic | $0.02230 | $446.00 | $4 / $20 | $0.2 |
Claude Opus 5.5 (Fast Mode) | Anthropic | $0.04460 | $892.00 | $8 / $40 | $0.4 |
GPT-6 AstraFlagship | OpenAI | $0.05650 | $1,130.00 | $10 / $50 | $1 |
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(CLS Prevention Active · Zero Shift)Understanding LLM Tokenization and API Pricing Mechanics
In modern generative AI and Large Language Model (LLM) engineering, compute and billing are measured not by raw characters or words, but by 'tokens'. Industry leaders like OpenAI (GPT series), Anthropic (Claude series), and Google (Gemini series) break down arbitrary multilingual text into subword units using Byte-Pair Encoding (BPE) or SentencePiece algorithms before feeding them into high-dimensional transformer layers. Consequently, the exact token volume depends heavily on morphological characteristics, vocabulary overlap, and tokenizer architecture.
A crucial architectural reality of LLM pricing is the severe asymmetry between 'Input (Context / Prompt)' and 'Output (Completion / Generation)' tokens. Input tokens can be processed concurrently across GPU tensor cores in an optimized prefill phase, yielding superior hardware throughput. In contrast, output generation is strictly auto-regressive—each new token must be sequentially generated conditioned on all prior tokens, heavily bottlenecking GPU memory bandwidth. As a result, all major cloud providers price output tokens 3x to 5x higher than input tokens.
OrvoKit's LLM Token Cost Calculator features official 2026 pricing for flagship and compact models, including GPT-6 Astra/Sol, Claude 5.5 Sonnet/Opus, Gemini 4 Argon, and Gemini 3.8 Flash. With interactive Prompt Caching simulations (up to 90% savings) and custom rate overrides, you can accurately budget single API calls or simulate enterprise multi-million monthly request workloads in real time.
Mathematical Cost Formulations & Prompt Caching Dynamics
The complete enterprise monthly billing formula incorporating input/output rates, request volumes, and cache hit efficiency.
Input and output tokens are universally billed per 1 million (1M) tokens. Cached prompt tokens benefit from a 75% to 95% discount rate, drastically slashing context-heavy operational expenditures.
👉 Prompt Caching alone saves $190.00 each month compared to standard uncached execution ($900.00/mo).
- Subword Token Multipliers: While English averages ~1.3 tokens per word (4 chars), non-Latin scripts and agglutinative languages often require 1.5x to 2x more tokens for the exact same semantic payload.
- GPU Memory Bandwidth Bottlenecks: Generating output tokens sequentially monopolizes HBM3e GPU memory channels, justifying the 3x-5x pricing premium across all providers.
- Cache Invalidation Rules: Caching requires an exact prefix match of at least 1,024 tokens. Any dynamic mutation at the beginning of the prompt invalidates the entire cache.
- Streaming Parity: Streaming responses via Server-Sent Events (SSE) enhances Time-to-First-Token (TTFT) perception but incurs the exact same per-token billing as non-streaming calls.
2026 Global LLM API Pricing & Specifications Benchmark (Per 1M Tokens)
Official published rates for flagship and compact production tiers across major providers.
| Provider | Model Architecture | Input Price / 1M | Output Price / 1M | Cached Input / 1M | Context Window |
|---|---|---|---|---|---|
| OpenAI | GPT-6 Astra (Flagship) | $10.00 | $50.00 | $1.00 (90% off) | 512K Tokens |
| OpenAI | GPT-6.1 Sol | $2.00 | $10.00 | $0.50 (75% off) | 256K Tokens |
| OpenAI | GPT-6 Luna (Speed) | $0.10 | $0.50 | $0.025 (75% off) | 128K Tokens |
| Anthropic | Claude Sonnet 5.5 | $2.00 | $10.00 | $0.10 (95% off) | 1M Context |
| Anthropic | Claude Opus 5.5 | $4.00 | $20.00 | $0.20 (95% off) | 1M Context |
| Gemini 4 Argon | $2.00 | $10.00 | $0.10 (95% off) | 2M Context | |
| Gemini 3.8 Flash (Best Value) | $0.75 | $3.75 | $0.075 (90% off) | 1M Context | |
| Gemini 3.5 Flash-Lite | $0.30 | $2.50 | $0.030 (90% off) | 1M Context |
* Prices reflect official developer API tiers as of 2026. Enterprise commitments and volume tiers may apply.
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(CLS Prevention Active · Zero Shift)How to Use
11. Enter Prompt Text or Set Token Sliders
Paste your production prompt or configure your estimated input context and output generation token counts.
22. Configure Monthly Request Volume
Specify your expected monthly query scale, from small internal automations to enterprise SaaS operations.
33. Adjust Prompt Caching Hit Ratio
Toggle Prompt Caching and set your cache hit percentage (0% to 90%) to calculate substantial recurring discounts.
44. Compare Model Pricing & Export Estimates
Review side-by-side cost comparisons across OpenAI, Anthropic, and Google, or simulate fine-tuned custom rates.
Real-World Architecture & Budgeting Scenarios
Estimated cloud expenditure across typical engineering deployments.
At 1,000 queries daily on Claude Sonnet 5.5, operational costs average just $35/month for complete corporate semantic retrieval.
At 1M monthly requests using Gemini 3.8 Flash, monthly cloud bills remain comfortably under $1,350 while maintaining lightning response times.
Complex agentic loops requiring deep reasoning on GPT-6 Astra or Claude Opus 5.5 demand a dedicated multi-thousand dollar monthly budget.
Routing high-frequency event categorization to GPT-6 Luna or Flash-Lite keeps infrastructure bills below $50 even at scale.
💡 Enterprise Prompt Engineering Tips & Cost Pitfalls
Anchor invariant system instructions, tool definitions, and few-shot examples at the very start of the prompt so caching layers maintain >90% hit rates.
Use terse keys like 'uid' instead of 'user_account_identifier' and prune optional fields in output schemas to trim expensive generation tokens by up to 30%.
Deploy a small, ultra-cheap model (e.g. Luna or Flash-Lite) to classify user intent before forwarding only complex queries to heavyweight flagship models.
Always set an explicit max_tokens ceiling (e.g. 1,500) on API calls to prevent runaway repetitive generation loops from exhausting token budgets.
⚠️ Common Pitfalls & Mistakes to Avoid
Frequently Asked Questions (FAQ)
Expert answers to the most frequently asked questions and edge cases.
Most popular tokenizers (such as OpenAI's tiktoken) use Byte-Pair Encoding trained predominantly on English corpora. Common English words form single whole tokens, whereas Korean, Japanese, Chinese, and Arabic words frequently fragment into multiple 2 to 3 byte subword chunks. Consequently, identical semantic sentences in non-English languages can consume 30% to 70% more tokens, proportionally elevating API costs.
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