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Cheapest Model - Best Value for Your Workload

🚀 Open the calculator

Meet Marcus Lee. Senior Engineer told to 'use the cheap model' for a new feature. "Cheapest model is meaningless without context. Cheapest for WHAT?"

🔥 Switched to Haiku to save money. Quality dropped. Switched back. CFO unhappy.

The story

'Cheapest model' is the wrong question - 'cheapest model that hits my quality bar' is the right one. Gemini 3 Flash at $0.50/1M output is cheap. So is DeepSeek V3 at $0.27. Both 'fail' on certain tasks where Claude Haiku or GPT-5 Mini succeed. Cheapest only matters if quality clears the threshold.

Marcus's mistake: switched to the absolute cheapest tier without testing on his workload. Customer support classification - Haiku worked, saved 60%. But for the agentic workflow with tool calls, Haiku struggled with the schema and returned malformed JSON. Quality cost outweighed price savings.

Three tiers. (1) Budget (DeepSeek, Gemini Flash, Haiku) — fine for classification, simple Q&A, narrow extraction. (2) Mid-tier (GPT-5 Mini, Sonnet 3.5) — solid for general agentic work, RAG, structured outputs. (3) Premium (Sonnet 4.6, GPT-5.5) — needed for complex reasoning, math, code generation. Pick the cheapest tier that passes your eval, not the cheapest model overall.

🎮 Playground

Cheapest Model Playground

Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.

Cheapest model that still meets your bar

Two of these pick the model; one sets the scale. The estimate below uses all three together.

✓
Cheapest tier that fits—

💡Complexity sets the tier — 1-3 budget, 4-6 mid-tier, 7+ premium (~13× pricier per token). Required quality only raises that floor: 7+ rules out budget, 9+ forces premium. Tasks/day then multiplies the tier rate by your volume.

Three real scenarios

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

$180.00 / month ≈ $2,160 / year

Sentiment classification on user reviews. Low complexity, low quality bar. Budget tier wins by 80% over mid-tier. ~$300/mo at 100K/day.

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

See inputs used
qualityThreshold
5
tasksPerDay
100,000
complexityScore
2
inputTokensPerTask
800
outputTokensPerTask
50

Use cases

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.

$5,700 / month ≈ $68,400 / year

High-volume tool selection (which API to call?). Mid-tier with prompt caching cuts cost dramatically. Tool defs cache → 80% input discount.

Healthy range: Haiku + caching, ~$2.5K/mo

See inputs used
qualityThreshold
7
tasksPerDay
200,000
complexityScore
5
inputTokensPerTask
3,000
outputTokensPerTask
100

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 →

Cheapest LLMs right now (per 1M tokens)

Verified 11 hours ago
  1. 1
    Command R7b 12.2024
    $0.150 in · $0.037 out ·
  2. 2
    voxstral-mini
    $0.040 in · $0.040 out ·
  3. 3
    voxtral-mini
    $0.040 in · $0.040 out ·

About this calculator: Cheapest Model - Best Value for Your Workload

Cheapest LLM for your workload - by tier, by task type, by quality threshold. Updated daily as vendors shift pricing. Beyond the per-token table.

🎛 Inputs you control

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

▸ What are you building: Determines minimum-quality bar AND the typical token shape (input/output sizes) the recommendation is costed against.
How to choose: Pick the closest match to YOUR primary use case. If you have multiple use cases, run the calc once per use case — they often select different optimal models.
▸ Tier-1 provider only: When checked, limits to OpenAI / Anthropic / Google. Excludes DeepSeek, xAI, Mistral, and others that may not have your required compliance posture.
How to choose: Check this only if you have an actual constraint (data residency rule, BAA requirement, procurement allowlist). Otherwise leave unchecked — Tier-1 models are typically 2-10× more expensive than the absolute cheapest.
▸ Vision required: Filters to models that can process images.
How to choose: Only check if vision is required for your workload. Vision-capable variants of mid-tier models often cost more than the text-only variant.
▸ Agent-capable required: Excludes Nano-tier and Flash-Lite models that lack the reasoning depth for multi-turn tool use.
How to choose: Check this for workloads with iterative tool calls, planning, or multi-step reasoning. Skip for one-shot tasks (classification, simple extraction, single-turn chat).
📋 Typical values & starting points Don’t know a value yet? Start with these broad, sourced ballparks — the calculator’s ▾ Typical menus pre-load the same options.
Input Default Typical ballparks
tasksPerDay moves the needle 50,000 Pilot team · ~100/day = 100 · Small production · ~1K/day = 1,000 · Mid production · ~10K/day = 10,000
qualityThreshold moves the needle 6 —
complexityScore moves the needle 5 —
inputTokensPerTask 2,000 —
outputTokensPerTask 400 —

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/cheapest-model.

📊 Outputs computed for you

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

▸ Cheapest-first model ranking: Models meeting ALL your constraints, ordered cheapest to most expensive at the use case's typical token shape.
How to read: Top entry is the cheapest valid option. Don't default to it blindly — read the "why this works" reasons. If the cheapest is a brand-new or unfamiliar provider, the 2nd-cheapest from a familiar vendor is often the pragmatic choice.
▸ Per-model rationales: Workload-specific reasons each model survives your filters: caching applicability, batch eligibility, long-context handling, free-tier availability, compliance posture.
How to read: A "Prompt caching 90% off" note means effective price is dramatically lower than sticker for repeat-context workloads. A "Batch API (50% off)" note means async-eligible traffic gets half-price.
▸ Per-call cost at typical shape: Dollar cost per single API call using the use case's default input/output token shape. With cache/batch savings applied where relevant.
How to read: Multiply by your expected daily request count for daily total. If your actual token shape differs from the preset, recompute in Cost Calculator with your real numbers.
📊 CALCULATOR AT A GLANCE
🚀 Open the full calculator ✉️ Email [email protected]
Cheapest Model - Best Value for Your Workload full size

Reading your result

Complexity picks the tier. 1-3 → a budget model is plenty. 4-6 → mid-tier. 7+ → premium.

Quality is a floor, not a dial. Required quality 7+ rules out the budget tier; 9+ forces premium — whatever the complexity. It can only raise the tier, never lower it.

Volume amplifies savings - and risks. At 50K/day, picking 30% cheaper-per-task = $X/mo saved. Picking 5% lower-quality = customer complaints. Test before scaling.

What "good" looks like:
  • Budget winners now: Gemini 3 Flash, DeepSeek V3, Haiku 4.5 - for tasks scoring complexity ≤4 and quality ≤6
  • Mid-tier winners: GPT-5 Mini, Claude Haiku - for complexity 4-6, quality 6-8
  • Best value at premium quality: Sonnet 4.6 (often beats Opus on cost-quality ratio)
  • Avoid: Premium-only for tasks scoring complexity ≤6 - wastes 5-10× the cost

What this calculator can't tell you

Honest limitations. Every model is wrong; some are useful. Where this one falls short:

For these, use: Multi-Model Router for routing strategy. Cost Calculator for full bill.

Trade-offs

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

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

Best fit for "cost":

  1. Budget (DeepSeek, Gemini Flash) $0.10-0.50/1M output
  2. Mid-tier (Haiku 4.5, GPT-5 Mini) $1-3/1M output
  3. Mid-tier (Sonnet 4.6) $15/1M - best value at quality

Cost ranks change weekly. Anchor on tier, not vendor. Re-check pricing quarterly because rankings shift as vendors compete.

Most popular

Everything above is the 80% case. The last 20% is where the money is.

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.

  • An hour with the people who built the engines
  • Report the same day
  • The fee credits toward any AICost plan
  • Two slots a week
Book a Solution Session: $299 → or $99 for small business →

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 →

Ready to run your own numbers?

You have seen the shape of it. Open the calculator with your model, your tokens, your volume.

🚀 Open the full calculator →

Where to go next

Route per-query for stacked savings →

Use cheap for easy, premium for hard.

 Project full bill with chosen model →

Validate the savings.

 FT a cheap model for narrow tasks →

Cheap + FT often beats premium + prompting.

Methodology

Source
/ai-cost-economics
Extraction
Per-vendor pricing pulled daily. Quality benchmarks from LMSYS Arena, MMLU, HumanEval.
Editorial gate
8-layer defense, see aicost.ai/ai-cost-economics
Last verified
7/27/2026, 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 163 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-28

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-07-28.
  • 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
2026-07-28
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 631 days captured
Anthropic Docs
2026-07-28
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 631 days captured
OpenAI
2026-07-28
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 632 days captured
Google AI
2026-07-28
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 607 days captured
Google Vertex
2026-07-28
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 607 days captured
DeepSeek
2026-07-28
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 546 days captured
xAI
2026-07-28
https://x.ai/api
Daily snapshot since Nov 2024 · 464 days captured
Mistral
2026-07-28
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 605 days captured
Cohere
2026-07-28
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 631 days captured
Voyage AI
2026-07-28
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 →