Which LLM is actually cheapest? An honest 2026 comparison

The pricing tables lie by omission

Every provider publishes their per-1M-token price. That number alone tells you almost nothing about what you'll pay, because your bill depends on four things the pricing page never mentions together:

1. Input/output ratio. A summarizer sends 10k tokens in, gets 200 back. A code generator does the reverse. Same model, wildly different bill.
2. Prompt caching. Anthropic, OpenAI and DeepSeek discount repeated prompt prefixes by ~90%. If 80% of your prompt is a fixed system message, the "real" input rate is roughly 20% of the sticker price.
3. Context window fit. If your prompt is 150k tokens, GPT-4o (128k) is not on the menu regardless of price.
4. Whether the model is actually good enough. DeepSeek V3 is 20x cheaper than Claude Opus but not always a substitute.

The AI Model Cost Comparator lets you paste your actual prompt and see per-call, per-day and per-month cost across 12 models side-by-side, with a slider for cache hit rate.

The tiers, honestly

Bottom tier - pennies per million tokens. Gemini 2.5 Flash, GPT-4o mini, Llama 3.3 70B (via Groq/Together), DeepSeek V3. All useful for classification, extraction, simple chat, high-volume routing. If your workload is 90% "the easy stuff", one of these is doing the work.

Middle tier - a few dollars per million. Claude Sonnet 4.5, GPT-4o, Gemini 2.5 Pro, Mistral Large. This is where most agentic and production RAG workloads live. Sonnet 4.5 and GPT-4o are close on price, close on capability; the differences show up on specific tasks (coding, long-form reasoning, tool-calling reliability).

Top tier - expensive. Claude Opus 4 and OpenAI o1 sit at $15/1M input, $60-75/1M output. They earn their price on hard reasoning, ambiguous specs, long autonomous runs - and they will burn a month of budget in a weekend if you let them drive a chat product.

Where prompt caching flips the answer

A common pattern: you have a 20k-token system prompt (documentation, examples, instructions) that never changes, plus a 500-token user question.

Without caching, Claude Sonnet 4.5 charges you for 20,500 input tokens per call at $3/1M = ~$0.062. Multiply by 100k calls/month = $6,200/mo.

With 95% cache hit rate on that system prompt, the fresh tokens per call drop to about 1,500 (500 user + occasional refresh). Cost drops to roughly $700/mo. Same model, same workload, 9x cheaper.

The takeaway: if you're comparing prices without factoring in caching, you're comparing the wrong numbers. Structure your prompts so the stable part comes first, and use the Anthropic/OpenAI/DeepSeek cache endpoints. Drag the cache slider in the tool to see the effect on your actual bill.

What the tool doesn't tell you

Three things you still have to judge yourself:

Latency. Groq-hosted Llama is 5-10x faster than any hosted GPT/Claude. For chat, that matters. Price alone won't show it.

Rate limits. Cheap doesn't matter if you can't get 1000 rps. Enterprise tiers on OpenAI and Anthropic give you headroom; DeepSeek and open-model hosts can throttle unpredictably.

Quality drift. Cost is measured; quality has to be evaluated. Any model swap needs a real eval on your data, not vibes.

The workflow

1. Take a representative prompt from your app.
2. Paste it into AI Model Cost Comparator.
3. Set your realistic call volume.
4. Drag the cache slider to your realistic hit rate.
5. Pick the 2-3 models that fit your budget.
6. Run an eval on those 2-3 for your actual task.

You'll spend an hour and probably save $500-$5000/mo. It's the highest-leverage hour in an AI project.

Related tools

• AI Model Cost Comparator - the interactive version.
• Token Counter - for exact input token counts.
• Prompt Enhancer - get more out of the cheaper models before you upgrade.