---
title: The 5-Floor LLM Guide for Marketers
description: Learn how to choose the right AI model for marketing tasks based on size, performance, cost, speed, and compliance.
image: https://ai-marketinglabs.com/hubfs/ChatGPT%20Image%20Aug%2019%2c%202025%2c%2003_31_15%20PM.png
---

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[The Lab Notes](https://ai-marketinglabs.com/lab-experiments) › ChatGPT

# The 5-Floor LLM Guide for Marketers

![Kelly Kranz](https://ai-marketinglabs.com/hubfs/1c8a298050a84a029fa51a6945685d90.png)

Kelly Kranz

Aug 19, 2025

[Twitter](https://twitter.com/intent/tweet?url=https://ai-marketinglabs.com/lab-experiments/the-5-floor-llm-guide-for-marketers&text=The%205-Floor%20LLM%20Guide%20for%20Marketers) [LinkedIn](https://www.linkedin.com/sharing/share-offsite/?url=https://ai-marketinglabs.com/lab-experiments/the-5-floor-llm-guide-for-marketers)

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*Pick the right model for each job—by size, performance, cost, speed, and guardrails.*

If you build with AI long enough, you learn that “best model” is the wrong question. The right question is: **what’s the right model *for this job*, given my budget, latency needs, context size, and compliance rules?** To make that choice fast (and factual), here’s a five-floor framework you can use to evaluate models for marketing work—from quick ad variants to long-context research and agentic automations.

Below, you’ll also find a **Model Picker flow**, a **token-cost cheatsheet**, and **three mini case studies** with dollar-and-cents examples.

---

*Notes on sources & recency: Pricing and features below reference official provider pages updated in August 2025. Specific citations appear inline.*

---

## Ground Floor — Size & Context (How much can the model “hold” at once?)

**Why it matters:** Many marketing tasks benefit from *context*: brand guidelines, past campaigns, product catalogs, CRM notes, transcripts, and competitive research. The more you can feed in one go, the less stitching you need.

- **Google Gemini 2.5/1.5 Pro**: up to **2 million tokens** of context in the paid tier (with tiered pricing by prompt size). This is currently the largest widely available context window from a major provider. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))
- **Anthropic Claude Sonnet 4**: standard pricing up to **200K tokens**; **1 million-token context** is available with a separate “long context” pricing tier and access conditions. ([Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))
- **OpenAI GPT-5**: context capacity **varies by variant**. OpenAI’s product page lists the API family with **up to ~400K** context; some documentation for chat-oriented variants lists **128K**. (In practice: check the exact model ID you’re using.) ([OpenAI](https://openai.com/gpt-5/?utm_source=chatgpt.com), [OpenAI Platform](https://platform.openai.com/docs/models/gpt-5-chat-latest?utm_source=chatgpt.com))

**When long context is overkill:** If your task only needs a short brief and a handful of examples (e.g., 30 ad variants from a 1-page creative brief), massive windows don’t buy you much—and you can save real money with smaller, cheaper models.

**Practical patterns:**

- Use **RAG** (retrieval-augmented generation) so you *retrieve only what’s relevant* rather than stuffing a full knowledge base into the prompt.
- When you *do* need huge context (e.g., auditing a 200-page content library), bias toward models with **lower input prices** and **prompt caching** (see Cost Floor).

## Second Floor — Performance & Task Fit (Who’s strong at what?)

Different providers optimize for different patterns:

- **Reasoning & multi-step tasks** (brief-to-outline-to-draft, multi-tool agents): GPT-5 is positioned by OpenAI as its best general model for complex, multi-step problems and “thinking” time, with structured tool use in the API. (You still choose the right variant for your latency and context needs.) ([OpenAI](https://openai.com/api/pricing/))
- **Long-document understanding** (policies, catalogs, transcripts): Gemini’s 2M context and tiered pricing help when you truly must pass very large context inline. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))
- **Fast, price-efficient drafting** (bulk ad copy, product descriptions, summaries at scale): “mini”/“flash” tiers from providers (GPT-5 mini; Gemini 2.5/2.0 Flash; Claude Haiku) are dramatically cheaper and often plenty capable for well-specified outputs. ([OpenAI](https://openai.com/api/pricing/), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))
- **Open-weights / self-hosting**: Meta’s Llama 3.x/3.1 models are distributed under the **Llama Community License**—commercial use permitted, with conditions (e.g., a special license is required if your products exceed **700M monthly active users**). They are **not OSI-approved open source** despite being openly available. (Good for control/compliance needs when you want to host your own.) ([Hugging Face](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/main/LICENSE?utm_source=chatgpt.com), [Open Source Initiative](https://opensource.org/blog/metas-llama-license-is-still-not-open-source?utm_source=chatgpt.com))

---

***Reality check:** Benchmarks shift frequently and can be cherry-picked. For marketing workloads, the biggest deltas in business performance still come from **prompt design**, **examples**, **RAG quality**, and **post-processing**, not small leaderboard gaps.*

---

## Third Floor — Cost (Token math that marketers can actually use)

**How you’re billed:** Nearly all providers charge per **input million tokens (MTok)** and **output MTok**. Think of tokens as ~4 characters of English on average; 750–1,000 tokens is roughly 500–700 words.

**Current reference prices (USD) as of Aug 2025:**

- **OpenAI GPT-5**: **$1.25 / 1M input**; **$10.00 / 1M output**. **Cached input** (prompt caching) is **$0.125 / 1M** (≈90% off). Batch API: **50% off inputs/outputs** for asynchronous runs. ([OpenAI](https://openai.com/api/pricing/))
- **Anthropic Claude Sonnet 4**: **$3.00 / 1M input**; **$15.00 / 1M output**. Batch API: **50% discount**; long-context (>200K input tokens) has premium rates. ([Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))
- **Google Gemini 2.5 Pro**: **$1.25 / 1M input** and **$10.00 / 1M output** for prompts ≤ 200K (higher tier above that); supports context caching with storage priced separately. Batch mode: **50%** of interactive price. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

---

*Cost formula: Total = (Input\_Tokens ÷ 1,000,000 × Input\_Rate) + (Output\_Tokens ÷ 1,000,000 × Output\_Rate) If you enable prompt caching and reuse a large system/brief across many jobs, cached portions bill at the cached input rate where supported.*

---

### Token-cost cheatsheet (examples)

Below are **apples-to-apples** one-run estimates using the official list prices above. Numbers are small by design to match real marketing tasks.

 

**Example A — 30 ad variants from a 2-page brief**  
Assume **2,500 input tokens** (brief + examples) and **1,500 output tokens** (30 variants).

- **GPT-5**:
  
    - Input: 2,500 ÷ 1,000,000 × $1.25 = **$0.003125**
    - Output: 1,500 ÷ 1,000,000 × $10.00 = **$0.015**
    - **Total ≈ $0.018125** (about **1.8 cents**)
- **Claude Sonnet 4**:
  
    - Input: 2,500 ÷ 1,000,000 × $3.00 = **$0.0075**
    - Output: 1,500 ÷ 1,000,000 × $15.00 = **$0.0225**
    - **Total ≈ $0.0300** (about **3.0 cents**)
- **Gemini 2.5 Pro**:
  
    - Input: 2,500 ÷ 1,000,000 × $1.25 = **$0.003125**
    - Output: 1,500 ÷ 1,000,000 × $10.00 = **$0.015**
    - **Total ≈ $0.018125** (about **1.8 cents**)  
      *(Prices from the official pages cited above.)* ([OpenAI](https://openai.com/api/pricing/), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Example B — 5-email sequence**  
Assume **1,000 input tokens** (brief + product facts) and **1,000 output tokens** (5 emails × ~200 tokens).

- **GPT-5**: 0.001 × $1.25 + 0.001 × $10.00 = **$0.00125 + $0.01 = $0.01125**
- **Claude Sonnet 4**: 0.001 × $3.00 + 0.001 × $15.00 = **$0.003 + $0.015 = $0.018**
- **Gemini 2.5 Pro**: 0.001 × $1.25 + 0.001 × $10.00 = **$0.00125 + $0.01 = $0.01125** ([OpenAI](https://openai.com/api/pricing/), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Example C — Sales insights from CRM notes via RAG**  
Assume **6,000 input tokens** (query + retrieved passages) and **800 output tokens**.

- **GPT-5**: 0.006 × $1.25 + 0.0008 × $10.00 = **$0.0075 + $0.008 = $0.0155**
- **Claude Sonnet 4**: 0.006 × $3.00 + 0.0008 × $15.00 = **$0.018 + $0.012 = $0.030**
- **Gemini 2.5 Pro**: 0.006 × $1.25 + 0.0008 × $10.00 = **$0.0075 + $0.008 = $0.0155** ([OpenAI](https://openai.com/api/pricing/), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

---

**Batching & caching tip:** If you run these at scale, enable **Batch/Async** (OpenAI & Anthropic: ~50% discount) and **prompt caching** for repeated system/brand briefs (OpenAI & Google list explicit cache pricing; Anthropic offers prompt caching with specific multipliers). ([OpenAI](https://openai.com/api/pricing/), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

---

## Fourth Floor — Speed & UX (How fast does it feel for users?)

For **interactive** experiences (live chat, on-site assistants, sales enablement tools), perceived speed matters more than raw throughput.

- **Realtime/Live APIs:**
  
    - OpenAI exposes a **Realtime API** for low-latency multimodal experiences (voice/video/streaming). Choose lighter variants if you need snappy responses. ([OpenAI](https://openai.com/api/pricing/))
    - Google exposes **Live API** pricing for Gemini Flash/Flash-Lite (interactive/streaming), distinct from batch rates. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Rules of thumb:**

- For **assistive UI** (drafting, rewriting) target first token in **< 1s** and full response in **< 3–5s**.
- For **agentic, multi-tool** flows (search → plan → write → cite), stream progress and **parallelize** tool calls where possible.
- If a job doesn’t need to feel instant (e.g., **bulk ad refresh** or **report generation**), run it in batch at 50% price and deliver when ready. ([OpenAI](https://openai.com/api/pricing/), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))

## Fifth Floor — Guardrails, Privacy & Licensing (What can you safely ship?)

**Data usage defaults (API & business tiers):**

- **OpenAI:** For business products (ChatGPT Team/Enterprise/Edu) and API, OpenAI **does not train on your business data by default**; you own your inputs and outputs (where allowed by law). Opt-in programs exist. ([OpenAI](https://openai.com/enterprise-privacy/?utm_source=chatgpt.com))
- **Anthropic:** Documentation and support pages state Anthropic **will not use your inputs/outputs to train Claude unless you explicitly opt in or report content**. ([Anthropic Privacy Center](https://privacy.anthropic.com/en/articles/10023580-is-my-data-used-for-model-training?utm_source=chatgpt.com))
- **Google (Gemini for Cloud / Gemini API):** Google Cloud’s governance docs state **Gemini for Google Cloud doesn’t use your prompts/responses to train models**. Gemini API terms include additional data-handling specifics; review your tier and toggles (e.g., “Used to improve our products” settings). ([Google Cloud](https://cloud.google.com/gemini/docs/discover/data-governance?utm_source=chatgpt.com), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Open weights (Llama) & “open source” claims:**

- **Meta’s Llama 3.1** license **permits commercial use**, but with notable conditions, including a separate license requirement if your products exceed **700M MAU**. The license is **not** OSI-approved “open source.” (Translation: it’s *openly available*, not open source in the OSI sense.) ([Hugging Face](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/main/LICENSE?utm_source=chatgpt.com), [Open Source Initiative](https://opensource.org/blog/metas-llama-license-is-still-not-open-source?utm_source=chatgpt.com))

**Safety controls & compliance:**

- Favor providers with **clear enterprise privacy docs** and **regional/data residency options** if you operate in regulated environments. OpenAI, Anthropic, and Google maintain public privacy and compliance pages. ([OpenAI](https://openai.com/enterprise-privacy/?utm_source=chatgpt.com), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))
- For **brand safety**, implement **policy-aware prompts and test suites**. Many providers expose **moderation tools** or guidance in docs; you should still add your own pre/post filters for claims, regulated terms, and bias.

## The Model Picker (fast decision flow)

**Step 1 — Context size**

- **Need >200K tokens inline?** Consider **Gemini 2.5/1.5 Pro (up to 2M)** or **Claude Sonnet 4 with long-context pricing (1M)**, then weigh costs. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))
- If your prompt + inline docs are ≤200K tokens, or you can solve it with RAG (fetching only small, relevant chunks)

**Step 2 — Latency vs. depth**

- **Live UX (<3s perceived):** Try **mini/flash tiers** first (e.g., GPT-5 mini, Gemini Flash/Flash-Lite; Claude Haiku). ([OpenAI](https://openai.com/api/pricing/), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))
- **Deep reasoning / multi-step:** Use **GPT-5** or **Claude Sonnet 4**; stream output and cache the long, repeated brief. ([OpenAI](https://openai.com/api/pricing/), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))

**Step 3 — Cost sensitivity**

- For **bulk generation**, prioritize **lower input rates** and **batch/caching**. (OpenAI & Anthropic list 50% batch discounts; OpenAI & Google list prompt caching prices.) ([OpenAI](https://openai.com/api/pricing/), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Step 4 — Hosting & control**

- **Need self-hosting or VPC-only?** Evaluate **Llama 3.x** under the Community License and your compliance needs. Remember: not OSI-approved open source. ([Hugging Face](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/main/LICENSE?utm_source=chatgpt.com), [Open Source Initiative](https://opensource.org/blog/metas-llama-license-is-still-not-open-source?utm_source=chatgpt.com))

**Step 5 — Data handling**

- **Strict “no training” posture required?** Confirm defaults and agreements: OpenAI business/API (no training by default), Anthropic (no training unless opted in), Google Cloud Gemini (docs state prompts/responses aren’t used to train). ([OpenAI](https://openai.com/enterprise-privacy/?utm_source=chatgpt.com), [Anthropic Privacy Center](https://privacy.anthropic.com/en/articles/10023580-is-my-data-used-for-model-training?utm_source=chatgpt.com), [Google Cloud](https://cloud.google.com/gemini/docs/discover/data-governance?utm_source=chatgpt.com))

## Mini Case Studies (with realistic costs)

### 1) Email Nurture Sequence (5 emails)

**Brief:** New feature launch sequence; use brand voice and two example campaigns.  
**Process:**

1. Embed a short **style guide** and **feature notes** (≈1,000 tokens).
2. Ask for a 5-email plan, then drafts (≈1,000 output tokens).
3. Optional) Run a **second pass** for compliance language and CTA testing (+500 output tokens).

**One-pass cost at list prices:**

- **GPT-5:** $0.01125 (from Example B). ([OpenAI](https://openai.com/api/pricing/))
- **Claude Sonnet 4:** $0.018. ([Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))
- **Gemini 2.5 Pro:** $0.01125. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Recommendation:** Start with **GPT-5** or **Gemini** for first drafts; escalate to **Sonnet 4** if you need deeper reasoning or tool use.

 

### 2) Paid Social Variant Factory (30 variants × weekly)

**Brief:** Weekly refresh of hooks and descriptions for 3 audiences; include 2 example winners.  
**Process:**

- Single prompt with audiences, brand lines, and “do/don’t” rules (≈2,500 input tokens).
- 30 concise variants (≈1,500 output tokens).
- Save **brand brief** in **prompt cache** (OpenAI/Gemini) so subsequent weeks bill cached input at **~90% off** (OpenAI) or at the provider’s cache price. ([OpenAI](https://openai.com/api/pricing/), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Per-run cost at list prices:**

- **GPT-5:** ≈ **$0.0181**; with caching, the repeated input can drop ~**10×** on the cached portion. ([OpenAI](https://openai.com/api/pricing/))
- **Claude Sonnet 4:** ≈ **$0.0300** (consider Sonnet 4 batch if you run this asynchronously). ([Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))
- **Gemini 2.5 Pro:** ≈ **$0.0181**; cache/storage billed per Gemini’s schedule. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Recommendation:** For steady, repeatable outputs, “mini/flash” tiers crush cost while maintaining quality—especially with **prompt caching**.

 

### 3) Long-Form Insight Synthesis (200 pages of research)

**Brief:** Turn a 200-page competitive dossier into a 3-page insight memo with citations.

**Two viable patterns:**

1. **True long-context** run (only if you *must*): use **Gemini Pro 2.5/1.5** (2M tokens) or **Claude Sonnet 4 long-context** (1M). Costs rise as input tokens climb above 200K; check tiered pricing. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))
2. **RAG pipeline (recommended)**: chunk & embed sources; retrieve **only relevant passages** per section (typical request: 5–10K input tokens, 1–1.5K output). On per-call math, RAG often costs **~10–50× less** than dumping everything into context, and it’s more controllable.

**Illustrative per-section cost with RAG (≈6K in / 1K out):**

- **GPT-5:** ≈ **$0.0155** per section; multiply by the number of sections you compile. ([OpenAI](https://openai.com/api/pricing/))
- **Claude Sonnet 4:** ≈ **$0.0300** per section. ([Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))
- **Gemini 2.5 Pro:** ≈ **$0.0155** per section. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

## Implementation Patterns That Save Real Money

**1. Separate “thinking” from “writing.”**

- Plan/outline with a **mini/flash** model.
- Draft/polish only if needed with a larger model.  
  (You’ll cut output-token costs on the expensive model.)

**2. Cache what you repeat.**

- Brand voice, terminology, compliance notes—keep them stable and **cached**. OpenAI lists **~90% off** cached input; Google publishes cache + storage prices. ([OpenAI](https://openai.com/api/pricing/), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**3. Batch when UX doesn’t matter.**

- If nobody’s staring at a spinner, schedule generation in **batch** and take the **50% discount** (OpenAI & Anthropic). ([OpenAI](https://openai.com/api/pricing/), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))

**4. Right-size the output.**

- Output tokens are the expensive half. If you don’t need a 1,500-token answer, ask for 200–400 tokens and a bullet structure.

 

**5. Use RAG, not copy-paste.**

- Embed once, retrieve small. This is the single biggest lever for *long* work.

## Compliance & Policy Checklist (ship this with stakeholders)

- **Data Training Defaults Confirmed:**
  
    - OpenAI business/API: **no training by default**; outputs owned by you (where allowed). ([OpenAI](https://openai.com/enterprise-privacy/?utm_source=chatgpt.com))
    - Anthropic: **no training unless you opt in / report content**. ([Anthropic Privacy Center](https://privacy.anthropic.com/en/articles/10023580-is-my-data-used-for-model-training?utm_source=chatgpt.com))
    - Google Cloud Gemini: **prompts/responses not used to train**; review API/App toggles. ([Google Cloud](https://cloud.google.com/gemini/docs/discover/data-governance?utm_source=chatgpt.com))
- **Licenses:**
  
    - If self-hosting **Llama**, document the **Community License** terms (and the **700M MAU** clause). Note: **not OSI open source**. ([Hugging Face](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/main/LICENSE?utm_source=chatgpt.com), [Open Source Initiative](https://opensource.org/blog/metas-llama-license-is-still-not-open-source?utm_source=chatgpt.com))
- **Audits & Logging:**
  
    - Log prompts, retrieved docs, and model IDs. Keep a reproducible paper trail for claims review.
- **Moderation & Brand Safety:**
  
    - Add pre-flight checks (e.g., restricted phrases) and post-flight QA (e.g., claim spotting) before publishing.

## Putting It All Together — Your “5-Floor” Playbook

**Ground Floor: Size/Context**

- Pick **Gemini Pro** or **Claude long-context** when you must inline huge context; otherwise use RAG and right-size. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))

**Performance Floor: Task Fit**

- **GPT-5 / Sonnet 4** for complex, tool-rich tasks; **mini/flash** tiers for fast drafting. ([OpenAI](https://openai.com/api/pricing/), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Cost Floor: Token Math**

- Know your input/output rates; exploit **batch** and **cache**. (OpenAI & Anthropic batch 50% off; OpenAI & Google publish cache pricing.) ([OpenAI](https://openai.com/api/pricing/), [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Speed Floor: Latency**

- For interactive flows, use **Realtime/Live** APIs and smaller models where possible. ([OpenAI](https://openai.com/api/pricing/), [Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

**Guardrails Floor: Safety/Licensing**

- Document **data-training defaults** and **Llama license** terms; align with legal and IT before scale-up. ([OpenAI](https://openai.com/enterprise-privacy/?utm_source=chatgpt.com), [Anthropic Privacy Center](https://privacy.anthropic.com/en/articles/10023580-is-my-data-used-for-model-training?utm_source=chatgpt.com), [Google Cloud](https://cloud.google.com/gemini/docs/discover/data-governance?utm_source=chatgpt.com), [Hugging Face](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/blob/main/LICENSE?utm_source=chatgpt.com), [Open Source Initiative](https://opensource.org/blog/metas-llama-license-is-still-not-open-source?utm_source=chatgpt.com))

## Quick Reference — Current Pricing (Aug 2025)

- **OpenAI GPT-5:** $**1.25** / 1M input, $**10.00** / 1M output; cached input $**0.125** / 1M; **Batch API: –50%**. ([OpenAI](https://openai.com/api/pricing/))
- **Anthropic Claude Sonnet 4:** $**3.00** / 1M input, $**15.00** / 1M output; **Batch API: –50%**; **1M context** tier with premium rates above 200K. ([Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing))
- **Google Gemini 2.5 Pro/1.5 Pro:** $**1.25** / 1M input, $**10.00** / 1M output (≤200K tier); context caching priced separately; **Batch Mode: 50%** of interactive. **Up to 2M context**. ([Google AI for Developers](https://ai.google.dev/gemini-api/docs/pricing))

### Footnotes on context sizes (why you’ll see different numbers)

Vendors list *families* of models and **variants** (chat vs. API, mini vs. large), each with potentially different context windows. For example, OpenAI’s public product page references GPT-5 API family with context listed “up to ~400K,” while a chat-specific doc references 128K for a particular “chat-latest” variant. Always check the **exact model ID** you deploy and your account limits before promising a number to stakeholders. ([OpenAI](https://openai.com/gpt-5/?utm_source=chatgpt.com), [OpenAI Platform](https://platform.openai.com/docs/models/gpt-5-chat-latest?utm_source=chatgpt.com))

**Bottom line:** Treat models like **tools on different floors of the same building**. Start at **Ground (context)** and move up: **Performance**, **Cost**, **Speed**, **Guardrails**. Make the choice that fits the job, not the hype—and your team will ship faster, cheaper, and safer.

*Prepared with current, primary sources; last verified August 19, 2025.*

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With over 15 years of marketing experience, Kelly is an AI Marketing Strategist and Fractional CMO focused on results. She is renowned for building data-driven marketing systems that simplify workloads and drive growth. Her award-winning expertise in marketing automation once generated $2.1 million in additional revenue for a client in under a year. Kelly writes to help businesses work smarter and build for a sustainable future.

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