Guides → Playground & Guide → AI Model Finder - Pick the Right Model for Your Workload
Meet Jordan Kim. Product Engineer evaluating AI for a new feature. "There are 17+ models across 6 vendors. Which one fits my workload without me reading 17 spec sheets?"
🔥 Spent two weeks comparing models on a spreadsheet, ended up picking the one with the best landing page.
Model selection has 4 axes. (1) Quality bar - what's the floor? (2) Workload type - chat, agent, RAG, code, multimodal? (3) Volume - small, mid, hyperscale? (4) Constraints - compliance, latency, lock-in tolerance? The right model is the cheapest one that clears all four.
Jordan's mistake is common: comparing on per-token cost alone. The cheapest model on paper may fail on tool-calling or hallucinate on RAG. The most expensive may be overkill for classification. The picker logic encodes the actual decision tree.
Three model tiers, three sweet spots. (1) Budget tier (Haiku, Gemini Flash, DeepSeek) — classification, simple Q&A, narrow extraction. (2) Mid-tier (Sonnet 4.6, GPT-5 Mini) — general agents, RAG, structured output. (3) Premium tier (Opus 4.7, GPT-5.5 Pro) — complex reasoning, math, deep research. Pick the cheapest that clears your quality bar.
Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.
Quality and complexity choose the tier; volume scales it. One estimate from all three.
💡Complexity sets the tier (1-3 budget, 4-6 mid-tier, 7+ premium). Quality is a floor: 7+ rules out budget, 9+ forces premium. Monthly volume multiplies the tier rate.
Same calculator, three team sizes. Click a tab to see how the numbers shift.
Sentiment / intent classification at 1M/month. Low complexity, low quality bar. Budget tier wins by 80% over mid-tier. ~$300/mo.
Healthy range: DeepSeek V3 or Gemini Flash, ~$300/mo
Customer-facing RAG over support docs. Quality 7 = budget not enough. Mid-tier tier hits the sweet spot.
Healthy range: Haiku 4.5 or GPT-5 Mini, ~$1-2K/mo
Multi-turn agent with tool calls. Mid-tier tier mandatory - cheaper tiers struggle with tool-call schemas. ~$5K/mo.
Healthy range: Sonnet 4.6, ~$5K/mo
Same calculator, different applications. Sizes above, workloads here. Pick the one that looks like yours.
Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers, then hit "Try this scenario" to load it into the calculator above.
Tier-1 deflection bot. Mid-tier tier with caching. Fast TTFT important for chat UX.
Healthy range: Haiku 4.5, $1.5-2.5K/mo
Autocomplete at 5M/month. Volume tips toward FT-cheap rather than premium. Latency matters.
Healthy range: Fine-tuned Mistral or GPT-5 Mini
Long-form synthesis. Budget tiers fail. Volume small, so absolute cost is fine.
Healthy range: Opus 4.7 or GPT-5.5 Pro mandatory
Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.
🚀 Open the full calculator →Stop guessing which AI model fits your use case. Answer 5 questions about workload, quality bar, and budget - get matched to the cheapest capable model with live pricing.
Each input shapes the cost. Click an input on the calculator to set it. The explanations below match the live calculator field by field.
| Input | Default | Typical ballparks |
|---|---|---|
monthlyVolume
moves the needle
|
200,000 | Pilot · 50K/mo = 50,000 · Mid production · 500K/mo = 500,000 · High volume · 5M/mo = 5,000,000 |
avgInputTokens
|
2,000 | Chat turn · ~500 in = 500 · Typical task · ~2K in = 2,000 · RAG-heavy · ~3.5K in = 3,500 · Long-context · ~10K in = 10,000 |
avgOutputTokens
|
400 | Classification / label · ~50 out = 50 · Short answer · ~300 out = 300 · Typical response · ~800 out = 800 · Long-form · ~1.5K out = 1,500 |
qualityThreshold
moves the needle
|
6 | — |
complexityScore
moves the needle
|
5 | — |
workloadType
|
agent | — |
Ballparks are broad industry starting points (sourced ranges; * = rough estimate) — your result gets more accurate as you replace them with measured numbers.
Try them live in the calculator;
API & agent users get the same data from the MCP resource aicost://input-reference/ai-model-finder.
What you'll see after the calculator runs. Each card explains how to read the number.
The picker output is a tier, then a vendor. Complexity sets the tier; quality raises the floor (7+ rules out budget, 9+ forces premium). Vendor then comes from cost + compliance + latency.
Budget tier wins more often than people expect. 50-70% of production workloads are classification, simple Q&A, or narrow extraction - all served fine by Haiku/Flash class.
Premium tier is rarely the right answer at scale. If you need Opus quality at 1M+ requests/month, the better question is: can you split the workload (cheap for triage, premium for hard cases)?
Vendor lock-in is the hidden axis. Picking the cheapest today is fine if you build vendor abstraction. Hardcoding into Anthropic SDK = pain when DeepSeek launches a 50%-cheaper model in 8 months.
Honest limitations. Every model is wrong; some are useful. Where this one falls short:
For these, use: Cheapest Model for tier detail. Multi-Model Router for routing strategy.
Cost isn't the only dimension. Click any constraint to see how recommendations change.
Single-vendor / single-model is rarely optimal at scale. Routing easy queries to cheap and hard ones to premium beats picking one model for everything.
Hallucination behavior varies by domain. Run a 100-prompt eval on your actual workload before committing.
Don't assume compliance certifications carry across all model tiers from a vendor. Some apply only to flagship; budget tier sometimes lacks.
Some cheap-tier APIs default to data-used-for-training. Read terms before piping user content.
Cheap-tier models usually win on latency. Mid-tier beats premium for sub-300ms requirements.
The cheapest model 6 months ago isn't the cheapest now. Multi-vendor abstraction (LiteLLM, custom) is worth the engineering.
Switching models without an eval is gambling. Build the eval first, switch second.
Tradeoff analysis is where most AI projects go sideways. Talk to a CFO-grade AI cost analyst →
The gaps we just listed are real, and they are the expensive ones: your actual prompts, your switching cost, your MLOps overhead. An AICost expert spends the hour on your AI and cloud costs, not a generic playbook. You leave with a written report: the way forward, in 30 days of concrete steps.
Not sure yet? The $39 AICost Blueprint credits toward a Session, and the Session fee credits toward any plan. You never pay twice for the same ground. See all pricing →
You have seen the shape of it. Open the calculator with your model, your tokens, your volume.
🚀 Open the full calculator →Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.
Why this matters: pricing for major vendors has dropped 40-90% in the last 24 months. A budget set 12 months ago is probably wrong by 30%+.
View 3-year history for →
Last-verified date is the most recent successful daily snapshot
(aicost_pricing_snapshots) or, when no snapshot exists yet,
the latest successful crawler run (aicost_crawler_runs).
10 of 10
vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.)
are not listed.
Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).
| Vendor / Model | Field | Why it’s inferred |
|---|---|---|
| Anthropic — Claude Sonnet 4.6 | cachedInput |
Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier. |
| Anthropic — Claude Sonnet 4.5 | cachedInput |
Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6. |
| Anthropic — Claude Sonnet 4.5 | batchInput |
Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount. |
| Anthropic — Claude Sonnet 4.5 | batchOutput |
Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount. |
| Anthropic — Claude Haiku 4.5 | cachedInput |
Derived at 10% of input rate — Anthropic 90% cache-hit discount convention. |
| OpenAI — GPT-5.4 Mini | cachedInput |
Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier. |
| OpenAI — GPT-5.4 Nano | cachedInput |
Derived at 10% of input — OpenAI 90% cache-hit convention. |
| OpenAI — GPT-5.4 Nano | batchInput |
Derived at 50% of input — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Nano | batchOutput |
Derived at 50% of output — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Pro | cachedInput |
Derived at 10% of input — OpenAI 90% cache-hit convention. |
| OpenAI — GPT-5.4 Pro | batchInput |
Derived at 50% of input — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.4 Pro | batchOutput |
Derived at 50% of output — OpenAI Batch API uniform 50% discount. |
| OpenAI — GPT-5.2 | cachedInput |
Derived at 10% of input; no residency uplift. |
| OpenAI — GPT-5.2 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.2 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 | cachedInput |
Derived at 10% of input. |
| OpenAI — GPT-5 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.5 Pro | cachedInput |
Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention. |
| OpenAI — GPT-5.5 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.5 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.2 Pro | cachedInput |
Derived at 10% of input — pro-tier convention. |
| OpenAI — GPT-5.2 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.2 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5.1 | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5.1 | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 Pro | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 Pro | batchOutput |
Derived at 50% of output. |
| OpenAI — GPT-5 Nano | cachedInput |
Derived at 10% of input. |
| OpenAI — GPT-5 Nano | batchInput |
Derived at 50% of input. |
| OpenAI — GPT-5 Nano | batchOutput |
Derived at 50% of output. |
| Google — Gemini 3 Flash | cachedInput |
Derived at 10% of input — Google caching discount convention ~90%. |
| Google — Gemini 3.1 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 3.1 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 3.1 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.5 Pro | cachedInput |
Derived at 10% of input. |
| Google — Gemini 2.5 Flash | cachedInput |
Derived at 10% of input. |
| Google — Gemini 2.5 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 2.5 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.5 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash | cachedInput |
Derived at 25% of input per Google 2.0 family caching rates. |
| Google — Gemini 2.0 Flash | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash-Lite | cachedInput |
Derived at 10% of input — Google caching convention. |
| Google — Gemini 2.0 Flash-Lite | batchInput |
Derived at 50% of input — Google Batch API uniform 50% discount. |
| Google — Gemini 2.0 Flash-Lite | batchOutput |
Derived at 50% of output — Google Batch API uniform 50% discount. |
| xAI — Grok 4 (legacy) | cachedInput |
Extrapolated at 25% of base. |
Pricing is cross-verified against the
LiteLLM community registry
when available. Daily snapshots are kept in aicost_pricing_snapshots;
every change is logged to aicost_price_changelog with old & new
values for full audit trail. Read the full methodology →