The Cheapest AI Model Might Be Your Smartest Choice. Here's the Math.

Frontier models get the headlines, but a lot of real AI work is sorting, tagging, extracting and summarizing. For those jobs, the gap between the cheapest and most expensive model from the same vendor can be 100× on the price list. Here's the math from official pricing pages, what the vendors say small models are for, and how to route work so you only pay frontier prices when a task needs them.

The Price Gap, From the Vendors' Own Pages

All prices below are list prices per million tokens (MTok), input / output, as of October 2026. Prices change often, so check the linked pages before you budget.

Anthropic

• Claude Fable 5.1: $10 / $50
• Claude Opus 5.5: $4 / $20
• Claude Sonnet 5.5: $2 / $10
• Claude Haiku 5.5: $0.10 / $0.50 for prompts up to 100,000 tokens, and $0.50 / $2.50 above that

Source: Anthropic pricing. Anthropic describes Haiku 5.5 as built "for high-volume, latency-sensitive tasks such as classification, extraction, and routing," with the "Fastest" comparative latency in its lineup (Anthropic models overview).

OpenAI

• GPT-6 Astra: $10 / $50, "Our most capable model for the most demanding work"
• GPT-6.1 Sol: $2 / $10, "Near-Astra performance for complex work at a lower cost"
• GPT-6 Luna: $0.10 / $0.50, "Our most efficient model for focused, high-volume tasks"

Sources: OpenAI models and OpenAI pricing.

Google

• Gemini 3.1 Pro Preview: $2 / $12 for prompts up to 200k tokens
• Gemini 3.8 Flash: $0.75 / $3.75 through 31 December 2026, then $1.50 / $7.50 from 1 January 2027
• Gemini 3.1 Flash-Lite: $0.25 / $1.50 for text, image and video

Source: Gemini pricing. Google describes 3.1 Flash-Lite as offering "frontier-class performance rivaling larger models at a fraction of the cost" (Gemini models).

xAI

xAI's lineup is narrower. Grok 4.7 lists $2.00 input and $6.00 output at its lower tier, and Grok 4.3 lists $1.25 and $2.50, with higher rates for prompts over 200k tokens (xAI models).

The Math: One Million Routine Requests

Imagine a ticket-tagging job: 1,000,000 requests a month, each with about 500 input tokens and 50 output tokens. That's 500M input tokens and 50M output tokens. At list prices:

• Claude Haiku 5.5: 500 × $0.10 + 50 × $0.50 = $75
• GPT-6 Luna: 500 × $0.10 + 50 × $0.50 = $75
• Gemini 3.1 Flash-Lite: 500 × $0.25 + 50 × $1.50 = $200
• Gemini 3.1 Pro Preview: 500 × $2 + 50 × $12 = $1,600
• Claude Opus 5.5: 500 × $4 + 50 × $20 = $3,000
• GPT-6 Astra or Claude Fable 5.1: 500 × $10 + 50 × $50 = $7,500

That's arithmetic on published prices, not a benchmark. Your actual bill depends on response length, how much reasoning the model does, retries and caching. The point is the size of the gap: if the small model gets the tag right, the flagship is costing you up to 100× more for the same output.

Discounts make the gap wider. Anthropic's Batch API is 50% off input and output, and OpenAI's pricing page describes similar batch savings. So a nightly job can run on a small model at half its already low price (Anthropic pricing; OpenAI pricing).

What the Vendors Recommend

Anthropic's model-selection guide describes an "efficiency-first" approach: begin with Haiku 5.5, test thoroughly, and "upgrade only if necessary for specific capability gaps." It recommends this for prototyping, tight latency requirements, cost-sensitive work and "high-volume, straightforward tasks." The guide also says "having a good evaluation set is the most important step" and that "tuning effort is often a better lever than switching models" (Anthropic, Choosing a model).

OpenAI's docs similarly point to Luna "for cost-sensitive, high-volume workloads" and Sol to balance intelligence and cost (OpenAI models).

Routing: Getting Small-Model Prices With Big-Model Quality

You don't have to pick one model for everything. Research on routing and cascades has measured what that can save:

• FrugalGPT (2023) found that a learned cascade of models could "match the performance of the best individual LLM (e.g. GPT-4) with up to 98% cost reduction" on the authors' benchmarks (Chen, Zaharia & Zou, arXiv).
• RouteLLM (2024) trained routers that send each query to a strong or weak model, and reported cutting costs "by over 2 times in certain cases—without compromising the quality of responses" (Ong et al., arXiv).

Anthropic documents two patterns of its own. In the advisor pattern, a cheaper executor model runs the loop and escalates hard decisions to a stronger model. In the orchestrator pattern, a frontier model plans and hands bulk work to cheaper workers (Anthropic, Optimizing for cost and intelligence).

Anthropic's measurements show the trade-off isn't always in your favor. On routine BrowseComp tasks, a coordinator with one cheaper worker "cost about half as much" as the frontier model alone. On the full, harder BrowseComp set, the frontier model alone "reached the coordinator configuration's accuracy at 22% to 30% lower cost." Routing pays off on easy, high-volume work and can backfire on hard work.

A simple routing playbook

1. Classify the task first. Classification, extraction, formatting, translation of short text and first-pass summaries are good candidates for small models.
2. Build a small eval set. Even a modest set of real examples with known-good answers lets you compare models on your own data instead of on public leaderboards.
3. Start small and escalate. Run the cheap model first, and send work to a larger model when output fails validation or the model reports low confidence.
4. Keep frontier models for the hard parts. Multi-step reasoning, long-horizon agents and final customer-facing drafts are where the extra cost is more likely to be worth it.
5. Re-check every quarter. Lineups and prices change quickly. Google's Flash prices above, for example, are already scheduled to double in January 2027.

How to Do This in MultiverseAI

MultiverseAI is built for this kind of mixing. One account gives you multi-model chat across OpenAI, Anthropic, Google and xAI models, so you can run the same prompt on a small model and a flagship side by side before deciding. In the Workflow Builder (55+ node types) each step can use a different model: a small model tags incoming items and a larger one writes the reply, triggered by a schedule, webhook or chat message. Billing makes the difference visible: 1 credit = $0.01, charged at provider cost plus 50%, with fractions carried over, so a cheap model call costs a fraction of a credit instead of being rounded up.

Try the math on your own workload. Sign up free with 100 credits or compare models in MultiverseAI. Pro is $29/month.

Sources

1. Anthropic, "Pricing," Claude Platform Docs. https://platform.claude.com/docs/en/about-claude/pricing
2. Anthropic, "Models overview," Claude Platform Docs. https://platform.claude.com/docs/en/about-claude/models/overview
3. Anthropic, "Choosing the right model," Claude Platform Docs. https://platform.claude.com/docs/en/about-claude/models/choosing-a-model
4. Anthropic, "Optimizing for cost and intelligence," Claude Platform Docs. https://platform.claude.com/docs/en/about-claude/models/optimizing-for-cost-and-intelligence
5. OpenAI, "Models," OpenAI API Docs. https://developers.openai.com/api/docs/models
6. OpenAI, "Pricing," OpenAI API Docs. https://developers.openai.com/api/docs/pricing
7. Google, "Gemini Developer API pricing," Gemini API Docs. https://ai.google.dev/gemini-api/docs/pricing
8. Google, "Gemini models," Gemini API Docs. https://ai.google.dev/gemini-api/docs/models
9. xAI, "Models and Pricing," xAI Docs. https://docs.x.ai/docs/models
10. Lingjiao Chen, Matei Zaharia, James Zou, "FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance," arXiv:2305.05176, May 9, 2023. https://arxiv.org/abs/2305.05176
11. Isaac Ong et al., "RouteLLM: Learning to Route LLMs with Preference Data," arXiv:2406.18665, June 26, 2024 (rev. Feb 23, 2025). https://arxiv.org/abs/2406.18665