Guides → Playground & Guide → AI Model Finder - Pick the Right Model for Your Workload

AI Model Finder - Pick the Right Model for Your Workload

🚀 Open the calculator

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.

The story

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) Cheap 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.

📊 CALCULATOR AT A GLANCE
🚀 Open the full calculator ✉️ Email [email protected]
AI Model Finder - Pick the Right Model for Your Workload full size

🎛 Inputs you control

Each input shapes the cost. Click an input on the calculator to set it — explanations below match the live calculator field by field.

▸ Your role — Pre-tunes filter weights based on what your role typically cares about (developer = context + reasoning; PM = balance; finance = price + provider).
How to choose: Pick the role closest to the decision you're making. If your work spans multiple roles, choose the one most cost-sensitive — it forces the tightest filter.
▸ Your workload scenario — Tells the finder what capabilities matter for your workload (RAG needs long-context; agents need reasoning + tool use; classification needs cheap + fast).
How to choose: Pick the closest match. If you're building multiple things, run the finder once per workload — they often pick different optimal models.
▸ Capability chips — Toggle chips to keep only models that support a feature. Vision filters multimodal vision-capable models; Tier-1 keeps OpenAI/Anthropic/Google only; Flagship hides budget variants.
How to choose: Enable only chips for capabilities you actually need NOW. Adding "Vision" because you might want it eventually pollutes results with multimodal premium pricing.
▸ Max input price per 1M tokens — Hard cap on input token price. Models pricier than this are hidden completely.
How to choose: Set to ~1.5× the price tier you can defend to finance. For a $5/M budget, start at $7.50 — gives buffer to see slightly-pricier "stretch" options.
▸ Minimum context window — Hides models with context windows below your threshold.
How to choose: Set to 1.2× your worst-case prompt size. RAG with 5K retrieval → 50K-100K minimum. Long-document analysis → 200K+. Don't overshoot — bigger contexts often cost more per 1M tokens.
▸ Provider filters — Per-provider toggle to include/exclude a vendor entirely. Useful when contract or compliance constraints limit which vendors you can use.
How to choose: Start with everyone checked. Uncheck only when you have a concrete reason (no GDPR-compliant region, no enterprise BAA, existing volume commitment elsewhere).

📊 Outputs computed for you

What you'll see after the calculator runs. Each card explains how to read the number.

▸ Ranked model list — Every model that survives all active filters, sorted by your chosen sort key (default: price ascending).
How to read: Read top-to-bottom — the first 5 rows are your candidates. Anything past row 10 is usually noise; tighten filters if the list is unwieldy.
▸ Side-by-side compare — Mark candidates for a detailed side-by-side comparison including capability matrix, vendor links, and last-verified pricing dates.
How to read: Limit comparison to 2-4 — more than that overwhelms decision-making. Usually compare frontier vs mid-tier vs budget within the same workload.
▸ Pricing freshness — When we last confirmed this price by re-reading the vendor pricing page (via our nightly crawler + grounding audits).
How to read: Anything < 14 days is fresh. 14-30 days is acceptable. > 30 days warrants vendor-page double-check before committing to a contract.
▸ Capability tags — Tags shown per model: vision, audio, video, long-context, reasoning, tools, structured-output, etc.
How to read: Use these as a sanity check that your filter chips matched models with the right capabilities. If a "Vision" filter returned models without the vision badge, file a data-quality bug.

About this calculator: AI Model Finder - Pick the Right Model for Your Workload

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.

Inputs you control

Input Impact on result Range Typical
Required quality (1-10) 1 = noise OK. 5 = decent. 7 = production. 9 = mission-critical. 10 = no failure tolerance. 1 – 10 6
Task complexity (1-10) 1 = classification. 5 = RAG over docs. 7 = multi-step reasoning. 10 = research-grade analysis. 1 – 10 5
Monthly request volume How many model calls per month. At 1M+, per-token differences matter. 1K – 10M 200000

Outputs computed for you

Output How inputs affect it
Monthly cost ($) computed from inputs
Annual cost ($) monthlyUsd × 12

Below: live sliders. Move them to see numbers change in real time.

What you're looking at

Each input shapes your cost. Move the slider — see the impact.

6

1 = noise OK. 5 = decent. 7 = production. 9 = mission-critical. 10 = no failure tolerance.

Estimated: —
5

1 = classification. 5 = RAG over docs. 7 = multi-step reasoning. 10 = research-grade analysis.

Estimated: —
200,000

How many model calls per month. At 1M+, per-token differences matter.

Estimated: —

Ready to run the numbers?

Open the full calculator — pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.

🚀 Open the full calculator →

Reading your result

The picker output is a tier, then a vendor. Tier from quality + complexity. Vendor from cost + compliance + latency.

Cheap 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.

What "good" looks like:
  • Classification, internal: DeepSeek V3 / Gemini Flash, $0.30/1M tokens
  • RAG, customer-facing: Haiku 4.5 / GPT-5 Mini, $1-3/1M tokens
  • Agent, mid-complexity: Sonnet 4.6, $15/1M tokens
  • Deep research, premium: Opus 4.7 / GPT-5.5 Pro, $75-150/1M tokens

Top picks for general workloads right now

Verified 20 hours ago
  1. 1
    GPT-5 Mini
    $0.250 in · $2.00 out ·
  2. 2
    Command
    $1.00 in · $2.00 out ·
  3. 3
    devstral-2
    $0.400 in · $2.00 out ·

Three real scenarios

Same calculator, three different team sizes. Click a tab to see how the numbers shift.

Sentiment / intent classification at 1M/month. Low complexity, low quality bar. Ultra-cheap tier wins by 80% over balanced. ~$300/mo.

Healthy range: DeepSeek V3 or Gemini Flash, ~$300/mo

See inputs used
qualityThreshold
5
complexityScore
2
monthlyVolume
1,000,000
avgInputTokens
800
avgOutputTokens
50
workloadType
classification

Trade-offs

Cost isn't the only dimension. Click any constraint — see how recommendations change.

What matters most to you? Click any dimension — recommendations update.

Best fit for "cost":

  1. Pick the cheapest tier that passes eval Don't over-pay for unused capability
  2. Mix tiers via routing 30-50% additional savings

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.

Use cases

Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers — then the "Try this scenario" button to load it into the calculator above.

Tier-1 deflection bot. Mid-cheap tier with caching. Fast TTFT important for chat UX.

Healthy range: Haiku 4.5, $1.5-2.5K/mo

See inputs used
qualityThreshold
7
complexityScore
4
monthlyVolume
500,000
avgInputTokens
3,000
avgOutputTokens
400
workloadType
chat

What this calculator can't tell you

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.

Where to go next

Drill into the cheap-tier picks →

Once tier is set, find the lowest-priced fit.

 Stack tiers via routing →

Use cheap for easy, premium for hard - 30-50% savings.

 Project the full bill →

Validate the picked model at your real volume.

Methodology

Source
/ai-cost-economics
Extraction
Picker logic calibrated against 30+ production model selections (anonymized).
Editorial gate
8-layer defense — see aicost.ai/ai-cost-economics
Last verified
6/4/2026, 8:00:00 PM

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.

3 years of pricing history

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 →
📖 Data sources & methodology 161 text models · 9 embeddings · 24 vision · 41 audio · 8 vector DBs across 10 vendor pages · last verified 2026-06-05

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-06-05.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

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.

Anthropic
2026-06-05
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Anthropic Docs
2026-06-05
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 578 days captured
OpenAI
2026-06-05
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 579 days captured
Google AI
2026-06-05
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 554 days captured
Google Vertex
2026-06-05
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 554 days captured
DeepSeek
2026-06-05
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 493 days captured
xAI
2026-06-05
https://x.ai/api
Daily snapshot since Nov 2024 · 411 days captured
Mistral
2026-06-05
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 552 days captured
Cohere
2026-06-05
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 578 days captured
Voyage AI
2026-06-05
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

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 →