Picking an AI model used to mean reading five different pricing pages, converting units in your head, and hoping you got the math right. With 8 providers, 47+ models, and per-token pricing that ranges from $0.05 to $168 per million tokens, the comparison problem has only gotten worse.
That's why we built the AI Cost Check calculator — a single place to compare every major AI API on price, context window, and total monthly cost. No signup, no paywall, no spreadsheets.
[stat] 47+ models, 8 providers All compared side by side with real-time pricing data — from GPT-5 nano at $0.05/M to GPT-5.2 pro at $168/M
What the calculator does
You set your expected usage — input tokens, output tokens, and requests per month — and the calculator shows you what each model will cost. Side by side. Instantly.
It covers every major model across eight providers:
- OpenAI: GPT-5.2, GPT-5, GPT-5 mini, GPT-5 nano, o3, o4-mini, GPT-4.1 series, and more
- Anthropic: Claude Opus 4.6, Claude Sonnet 4.6/4.5, Claude Haiku 4.5, Claude 3.5 series
- Google: Gemini 3 Pro, Gemini 3 Flash, Gemini 2.5 Pro/Flash/Flash-Lite
- DeepSeek: DeepSeek V3.2, DeepSeek R1 V3.2
- Mistral: Mistral Large 3, Codestral, Magistral Medium/Small, Mistral Small 3.2
- Meta: Llama 4 Maverick, Llama 3.1 405B/70B/8B (via Together AI)
- xAI: Grok 4, Grok 4.1 Fast, Grok 3/3 Mini
- Cohere: Command R+, Command R
Every number comes from official provider pricing, verified against our models database. We update it when providers change rates.
Why comparing matters more than you think
Token pricing looks simple until you realize output tokens can cost 5–10× more than input tokens. A model that looks cheap on the input side can blow your budget on output-heavy workloads.
Take GPT-5 at $1.25 input / $10.00 output per million tokens versus DeepSeek V3.2 at $0.28 / $0.42. For a workload generating mostly output, DeepSeek's output rate is 24× cheaper. That gap compounds fast at scale.
Context window size matters too. Running a Gemini 3 Pro query with 500K tokens of context is possible in a single call (it supports 2M). Doing the same on a 128K-limited model requires chunking strategies that increase total token consumption by 20–40%.
The calculator makes these differences visible instantly. No mental math, no spreadsheet formulas.
💡 Key Takeaway: The cheapest model on a pricing page might not be cheapest for your workload. Output volume, context size, and request patterns all shift the real cost. The calculator accounts for all three.
Four real scenarios with actual numbers
Here's where the calculator pays for itself. Let's walk through four common use cases with real pricing from our database.
Scenario 1: Customer support chatbot
A mid-size SaaS handling 50,000 conversations per month. Each conversation averages 800 input tokens (customer message plus system prompt) and 400 output tokens (response).
Monthly tokens: 40M input, 20M output.
| Model | Input Cost | Output Cost | Monthly Total |
|---|---|---|---|
| DeepSeek V3.2 | $11.20 | $8.40 | $19.60 |
| GPT-5 mini | $10.00 | $40.00 | $50.00 |
| Gemini 2.5 Flash | $6.00 | $12.00 | $18.00 |
| Mistral Large 3 | $20.00 | $30.00 | $50.00 |
| GPT-5 | $50.00 | $200.00 | $250.00 |
| Claude Sonnet 4.6 | $120.00 | $300.00 | $420.00 |
Gemini 2.5 Flash and DeepSeek V3.2 come in under $20/month for 50K conversations. GPT-5 mini is a strong middle ground at $50. If you need top-tier quality, GPT-5 costs $250 — still reasonable, but 13× more than DeepSeek.
For most customer support use cases, the quality gap between budget and flagship models is smaller than you'd expect. Start cheap, upgrade where it matters. See our DeepSeek vs GPT-5 Mini comparison for a detailed head-to-head.
Scenario 2: Code generation pipeline
A development team running AI-assisted code reviews and generation. They process 5,000 requests/day with heavier context — 2,000 input tokens (code + instructions) and 1,500 output tokens (generated code).
Monthly tokens: 300M input, 225M output.
| Model | Input Cost | Output Cost | Monthly Total |
|---|---|---|---|
| Claude Opus 4.6 | $1,500 | $5,625 | $7,125 |
| GPT-5.2 | $525 | $3,150 | $3,675 |
| GPT-5 | $375 | $2,250 | $2,625 |
| Gemini 3 Pro | $600 | $2,700 | $3,300 |
| Codestral | $90 | $202.50 | $292.50 |
| DeepSeek V3.2 | $84 | $94.50 | $178.50 |
The spread is enormous — from $178.50/month with DeepSeek V3.2 to $7,125 with Claude Opus 4.6. That's a 40× difference. For code generation, Codestral from Mistral is purpose-built at $292.50, and DeepSeek V3.2 undercuts everything at $178.50.
A smart approach: use DeepSeek V3.2 or Codestral for routine code suggestions, and route complex architectural questions to GPT-5 or Claude Opus 4.6. Our guide on cost optimization strategies covers this tiered routing approach in detail.
Scenario 3: RAG-powered document search
A legal tech company running retrieval-augmented generation over document collections. Each query sends 8,000 input tokens (retrieved chunks plus question) and 1,000 output tokens (synthesized answer). Volume: 20,000 queries/month.
Monthly tokens: 160M input, 20M output.
| Model | Input Cost | Output Cost | Monthly Total |
|---|---|---|---|
| Mistral Small 3.2 | $9.60 | $3.60 | $13.20 |
| Grok 4.1 Fast | $32.00 | $10.00 | $42.00 |
| Gemini 2.5 Flash | $24.00 | $12.00 | $36.00 |
| Claude Haiku 4.5 | $160.00 | $100.00 | $260.00 |
| GPT-5 | $200.00 | $200.00 | $400.00 |
RAG is input-heavy, which makes input pricing the dominant factor. Mistral Small 3.2 at $13.20/month is a standout. Gemini 2.5 Flash and Grok 4.1 Fast offer more capability at $36–$42 — strong value. If accuracy matters more than cost, Claude Haiku 4.5 at $260/month offers strong quality at moderate spend.
📊 Quick Math: For RAG workloads, input tokens outnumber output tokens 8:1 in this example. That makes input pricing the dominant cost factor — the opposite of chatbot workloads. The calculator reveals these patterns instantly for your specific ratio.
Scenario 4: High-volume data processing
An e-commerce company classifying 500,000 product descriptions per month for category tagging. Each description: 200 input tokens, 20 output tokens (just a category label).
Monthly tokens: 100M input, 10M output.
| Model | Input Cost | Output Cost | Monthly Total |
|---|---|---|---|
| Mistral Small 3.2 | $6.00 | $1.80 | $7.80 |
| GPT-5 nano | $5.00 | $4.00 | $9.00 |
| Llama 3.1 8B | $18.00 | $1.80 | $19.80 |
| GPT-4o mini | $15.00 | $6.00 | $21.00 |
| DeepSeek V3.2 | $28.00 | $4.20 | $32.20 |
For pure classification at scale, Mistral Small 3.2 and GPT-5 nano dominate. Under $10/month to classify half a million items. At this price point, the AI cost is less than the infrastructure to run the pipeline.
How to use the calculator effectively
The calculator works best when you bring realistic numbers. Here's how to get them:
Estimate your token counts
- 1 token ≈ 0.75 English words (or about 4 characters)
- A 500-word prompt is roughly 670 tokens
- A 200-word response is about 270 tokens
- Include system prompts, tool definitions, and conversation history in your input count
- Use our token counter for precise measurements
- Read our token pricing explainer for detailed rules of thumb
Focus on output costs
Output tokens cost 2–8× more than input across most providers. If your app generates long responses (summaries, code, articles), output cost will dominate your bill. The calculator makes this visible by showing input and output costs separately.
Compare across tiers, not just providers
Sometimes the best deal isn't switching from OpenAI to Anthropic — it's dropping from GPT-5 to GPT-5 mini within the same provider. A tier downgrade within one provider often saves more than switching providers at the same tier.
Factor in context windows
If you need to process long documents, models with 1M+ context windows like Gemini 3 Pro (2M tokens), Grok 4.1 Fast (2M tokens), or o4-mini (2M tokens) can process everything in a single call. Smaller context windows force chunking strategies that add complexity and cost — typically 20–40% more total tokens.
Account for hidden costs
The calculator shows raw per-token costs. In production, your effective cost will be 30–50% higher due to retries, failed requests, context waste, and system prompt overhead. Read our hidden costs guide to budget accurately.
⚠️ Warning: The calculator gives you the theoretical minimum cost. Real-world costs include retries (5% overhead), prompt engineering iterations during development ($50–$200 per feature), and thinking token overhead for reasoning models (5–14× the visible output). Always add a 30–50% buffer to your calculator results.
The pricing landscape at a glance
Prices vary by 2,800× across the market. Here's a quick orientation:
Ultra-budget tier ($0.18–$0.50/M output): Mistral Small 3.2, GPT-5 nano, Llama 3.1 8B, DeepSeek V3.2, Grok 4.1 Fast. Classification, extraction, simple Q&A, high-volume processing. Pennies per thousand requests.
Efficient tier ($0.60–$2/M output): GPT-5 mini, Gemini 2.5 Flash, GPT-4o mini, Llama 4 Maverick, Codestral, Mistral Large 3. Solid for most production workloads. Surprisingly capable for customer-facing features.
Mid tier ($3–$15/M output): GPT-5, Claude Sonnet 4.6, Gemini 3 Pro, Claude Haiku 4.5, o4-mini. Strong general-purpose models. The sweet spot for applications where quality directly impacts user retention.
Premium tier ($15–$168/M output): Claude Opus 4.6, o3-pro, GPT-5.2 pro. Best reasoning and quality, highest cost. Use for complex analysis, legal/medical applications, and high-stakes decisions where accuracy justifies the price.
For a full breakdown with all 47+ models ranked, see our complete cost-per-million-tokens ranking or the provider-by-provider pricing guide.
Start comparing
The fastest way to find the right model for your budget is to plug in your numbers and see the results. No spreadsheets, no mental math, no reading five different pricing pages.
Try the AI Cost Check calculator — pick your workload, compare the models, and make a decision backed by real numbers.
Already know you're overspending? Read our 10 strategies to cut your AI API bill in half for actionable optimization tactics, or learn how to estimate AI API costs before building for a step-by-step budgeting framework.
Frequently asked questions
Is the AI Cost Calculator free to use?
Yes, completely free. No signup, no paywall, no usage limits. Enter your token counts and request volume, and compare every model instantly. We maintain the calculator as a public tool for the AI developer community.
How often is the pricing data updated?
We update pricing data whenever providers announce rate changes. Our models database tracks 47+ models across 8 providers. All numbers are sourced from official provider pricing pages and verified regularly. The last update was February 2026.
How accurate are the calculator's estimates?
The calculator uses exact per-token pricing from each provider, so the raw cost calculations are precise. However, real-world costs are typically 30–50% higher due to retries, failed requests, context window waste, and development overhead. We recommend adding that buffer to your calculator results. See our hidden costs guide for details.
Can I compare reasoning models like o3 and DeepSeek R1?
Yes. Reasoning models are included in the calculator. Keep in mind that reasoning models generate thinking tokens billed as output, so the actual output token count will be higher than the visible response. Factor in a 3–10× thinking token multiplier when estimating output for reasoning models.
Which model should I start with for a new project?
Start with a budget model — DeepSeek V3.2 ($0.28/$0.42), GPT-5 mini ($0.25/$2.00), or Gemini 2.5 Flash ($0.15/$0.60). Test it on 50–100 representative prompts. If quality meets your bar, you're done. If not, try the next tier up. Most developers are surprised at how capable budget models are for routine tasks. Only escalate to flagship models when measurably better quality justifies the 10–25× cost increase.