Cheapest LLM API in 2026: What to Actually Pick

Last updated: October 2026

"What's the cheapest LLM API?" is the wrong question — or rather, it's a question with a trap in it. The model with the lowest per-token price is rarely the cheapest one on your invoice. Your real bill is decided by three things the headline price doesn't show: cache rates, output ratios, and pricing thresholds.

Here's the honest comparison, using only verified current prices.

The Verified Price Table

These are the models with confirmed pricing as of October 2026. No rumors, no "reportedly" — just published rate cards:

ModelInput / 1MCached input / 1MOutput / 1M
GPT-5.6 Sol (promo)$4.00$0.40$20.00
GPT-6.1 Sol$2.00$0.10$10.00
Claude Opus 5.5$4.00$0.20$20.00
Claude Sonnet 5$2.00—$10.00
Gemini 4 Argon (intro)$2.00—$10.00
Gemini 4 Argon (from Dec 1)$4.00—$20.00
Muse Wonder$4.00—$20.00

Two deadlines matter here. GPT-5.6 Sol's promo is "available at least through November 21, 2026" — after that, pricing is unpublished, but the pre-promo list price was $5/$30. Gemini 4 Argon's intro rate runs through November 30, 2026, then doubles to $4/$20 on December 1.

If you stopped at the table, you'd conclude GPT-6.1 Sol and Sonnet 5 and Argon (today) tie at $2/$10. But the table isn't the bill. Here's why.

Why Headline Price Doesn't Decide Your Bill

Cache rates change everything

Look at the cached-input column. GPT-5.6 Sol charges $0.40 for cached reads — 10% of its input rate. GPT-6.1 Sol charges $0.10 — just 5%. Claude Opus 5.5 charges $0.20.

On a cache-heavy workload, these differences dominate. Take the reference workload: 1,100 tasks/month, each with 12K fresh input, 48K cached input, and 6K output tokens:

  • GPT-5.6 Sol (promo): $205.92/month
  • GPT-6.1 Sol: $97.68/month
  • Claude Opus 5.5: $195.36/month — 5.1% cheaper than the Sol promo, despite identical $4/$20 headline rates, purely because its cache reads cost half as much

Two models with the same sticker price can differ 5% or more on real workloads. Always model with your actual cache ratio, not the headline rate.

Output ratios punish verbosity

Output tokens cost roughly 5x input tokens across the board. That means the model that generates concise answers is cheaper than the model that rambles — at identical per-token prices. When evaluating, benchmark cost per completed task, not cost per million tokens.

Thresholds and deadlines are hidden price hikes

GPT-5.6 Sol reprices the entire request at 2x input / 1.5x output past 272K input tokens. Meanwhile, Argon's price doubles on December 1 and Sol's promo has an expiry date with no published follow-on price. The "cheapest" model today can be the most expensive one next quarter if you're not watching the dates.

Quality-Per-Dollar Verdicts by Use Case

Bulk processing and evals (cost-first): GPT-6.1 Sol at $2/$10 with $0.10 cached reads is the value pick among verified options. Pair it with batch APIs (50% off) and the economics get hard to beat.

Coding agents (balanced): This is where cache economics matter most. Claude Opus 5.5 at $4/$20 with $0.20 cache reads is 5% cheaper than GPT-5.6 Sol's promo on the reference workload — and on the AA Intelligence Index it scores 58 versus Wonder Full's 56 and Argon 53. The right answer is workload-dependent: measure your cache ratio, then pick.

Chat and interactive products (latency-sensitive): Batch discounts don't apply, so you're paying full rate-card prices. The $2/$10 tier versus the $4/$20 tier — Sonnet 5, GPT-6.1 Sol, or Argon before December 1. Pick on capability fit and ecosystem.

One more consideration: don't ignore switching costs. Chasing the cheapest rate every quarter only pays if your stack is provider-agnostic.

The Re-Check Calendar: Prices Move

Two dates are on the calendar: November 21, when GPT-5.6 Sol's promo period reaches its published "at least through" date with no follow-on pricing announced, and December 1, when Gemini 4 Argon's intro rate doubles. Re-verify any price table older than 30 days before budgeting against it, and put the known deadlines in your calendar now.

The Bottom Line

The cheapest LLM API in 2026 isn't a model — it's a method. Model your actual token mix, watch the pricing deadlines, benchmark cost per task instead of cost per token, and re-check quarterly. The developers with the lowest bills are the ones who measure instead of assuming.

Popular posts from this blog

Argon vs Wonder: Which AI Should Developers Build On?

Image
Last updated: October 2026 Two flagship models, two very different price tags — and one of the prices is about to double. If you're choosing a foundation model to build on right now, the decision isn't just about capability. It's about timing. Here's the honest comparison: what each model costs, what the benchmarks actually say, and which one to pick depending on what you're optimizing for. The Pricing: Read the Dates Gemini 4 Argon is on introductory pricing: $2.00 per 1M input tokens / $10.00 per 1M output tokens . That intro rate runs through November 30, 2026 . From December 1 , the price doubles to $4.00 / $20.00 . Muse Wonder is priced at $4.00 / $20.00 per 1M tokens — no intro period, no scheduled change. So today, Argon costs half what Wonder does. On December 1, they cost exactly the same. That convergence matters more than it looks. Every cost comparison you run today has a 6-week expiry date. If you're doing the math — and you should be — do...
Read more

GPT-5.6 API Pricing: The November 21 Deadline

Image
Last updated: October 2026 If you're running GPT-5.6 through the API right now, you're on a promotional price. And that promotion has an expiry date: November 21, 2026 . OpenAI's pricing page states the current rate is "available at least through November 21, 2026." Read that carefully. "At least through" — not "until." The promo could extend. It could also end exactly on that date. OpenAI has published no pricing for what comes after. This post lays out exactly what the pricing is today, what it costs on a realistic workload, and how to budget for the unknown that follows November 21. The Current Pricing, Exactly GPT-5.6 is available in several variants. The one most developers are using — the flagship reasoning model, referred to here as GPT-5.6 Sol — is currently priced at: Input: $4.00 per 1M tokens Cached input: $0.40 per 1M tokens Output: $20.00 per 1M tokens That is promotional pricing. Before the promo, announced August 21, ...
Read more

Prompt Caching: The 90% Discount You're Missing

Last updated: October 2026 There is a 90% discount sitting in your API bill, and most developers are either not using it or using it in a way that doesn't work. Prompt caching — paying a tenth of the input price for tokens the provider has already seen — is the highest-leverage cost optimization available on modern LLM APIs. Here's how it actually works and how to get the savings. How Prompt Caching Actually Works The idea is simple. When you send a request, the provider processes your input tokens through the model — that's the expensive computation you're paying for. But if your next request starts with the exact same tokens — the same system prompt, the same repo context, the same few-shot examples — the provider doesn't need to recompute all of that. It kept the intermediate results cached. So it charges you a fraction of the price for those repeated tokens. The key phrase is stable prefix . Caching works from the start of your prompt forward: the provider ...
Read more