LLM Cost Calculator
Real Cost Per Task
Per-token pricing tables don't tell you the full story. Different models use wildly different numbers of tokens for the same task. OpenMark shows you the ACTUAL cost per task — not theoretical rates.
The problem: A model with cheaper per-token pricing might actually cost MORE for your task if it generates 3x more tokens. Reasoning models (o3, DeepSeek Reasoner) use extended thinking tokens that dramatically increase real costs. The only way to know your true cost-per-task is to measure it.
Why Per-Token Pricing Misleads
Token Count Variance
A verbose model might use 3x the output tokens as a concise one for the same answer. Cheaper per-token ≠ cheaper per-task.
Reasoning Tokens
Models like o3 and DeepSeek Reasoner use invisible "thinking" tokens. A cheap model might use 50K tokens internally per request.
Retries & Failures
A cheap model that fails 20% of the time needs retries. Those retries cost money. A reliable, slightly pricier model might be cheaper overall.
Prompt Caching
Some providers offer prompt caching (50-90% discount). But only if your prompts are structured correctly. Real savings depend on your usage patterns.
AI API Pricing Comparison (March 2026)
| Model | Provider | Input $/M | Output $/M | Tier |
|---|---|---|---|---|
| Claude Sonnet 4.5 | Anthropic | $3.00 | $15.00 | Premium |
| GPT-4o | OpenAI | $2.50 | $10.00 | Premium |
| Gemini 2.5 Pro | $1.25 | $10.00 | Standard | |
| Mistral Large 3 | Mistral | $2.00 | $6.00 | Standard |
| GPT-5 Codex | OpenAI | $1.25 | $10.00 | Standard |
| MiniMax M2.5 | MiniMax | $0.30 | $1.20 | Budget |
| Claude Haiku 3.5 | Anthropic | $0.80 | $4.00 | Budget |
| Gemini 2.5 Flash | $0.30 | $2.50 | Budget | |
| DeepSeek Chat | DeepSeek | $0.28 | $0.42 | Budget |
| GPT-5 Nano | OpenAI | $0.05 | $0.40 | Budget |
| Gemini 2.5 Flash-Lite | $0.10 | $0.40 | Budget |
For comprehensive, always-up-to-date pricing, see AI Model Pricing Comparison →
How OpenMark Calculates Real Costs
Unlike static pricing tables, OpenMark shows you what each model actually costs for YOUR specific task:
"We were paying $1,200/month on a flagship model for our classification pipeline. An OpenMark benchmark showed DeepSeek Chat matched our accuracy threshold at $130/month. We saved $12,840/year by switching a single model."
Cost Optimization Strategies
Benchmark Before Committing
Always test 3-5 models across price tiers. The cheapest adequate model might save you 90%. Use model comparison to find it.
Route by Complexity
Use cheap models for simple tasks, premium models for complex ones. Gemini 3.1 Flash-Lite ($0.25/$1.50) is a strong budget option for routing simple requests. A routing layer can cut costs 60-80% with minimal quality loss.
Re-benchmark Monthly
Models get cheaper and better. A model that was too expensive last month might be viable now. Regular benchmarks keep your costs optimal.
FAQ
How much does it cost to use OpenMark?
Sign up and get 100 free credits. Each benchmark costs credits based on models and output tokens selected. You see estimated cost before running. Pricing details →
Why do reasoning models cost so much more?
Models like o3 and DeepSeek Reasoner use extended "thinking" tokens — often 10-50x more output tokens than standard models. This makes them expensive per task despite competitive per-token rates.
What is accuracy-per-dollar?
It's accuracy score divided by API cost. A model scoring 80% at $0.001 has an acc/$ of 80,000 — vastly better than a model scoring 85% at $0.05 (acc/$ = 1,700). This metric reveals true value.
Calculate Your Real AI Costs
Stop guessing from pricing tables. See what models actually cost for YOUR task.
Free tier — no credit card required.