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How to Choose an LLM API for Your SaaS in 2026

Choose an LLM API by the cost and reliability of your actual task, not the headline price per token. Test real jobs, measure what one completed task costs, then check caching, batch tiers, context limits, retention and jurisdiction risk.

ZeroRaised Team @zeroraised

Editorial title card reading "How to Choose an LLM API for Your SaaS in 2026" beside the orange ZeroRaised mark.

Key takeaways

  • Build a small evaluation set from real requests and score pass or fail before comparing vendors on price.
  • Compare cost per completed task, including retries, cache writes, long context and tool calls, not the rate for one token.
  • Use batch or flex for work that can wait: OpenAI prices both at half the standard rate, while Kimi charges a flat rate with no context tiers.
  • Ask about retention and jurisdiction. Kimi offers zero data retention, and the US DeepSeek order covers third-party products built with its technology.
  • Keep a thin abstraction and a second tested model so a price change or procurement rule does not force a rewrite.
On this page 9
  1. What should you test before you compare prices?
  2. How do you calculate cost per task?
  3. Which pricing levers matter most?
  4. Why is the discount war relevant to a small team?
  5. What data retention and jurisdiction risks should you check?
  6. How do you keep your stack portable?
  7. What this means for bootstrapped founders
  8. Where ZeroRaised fits
  9. Sources

Choose an LLM API by the cost and reliability of your actual task, not by the headline price per token. Test a few models on real jobs, measure what one completed task costs, then check caching, batch pricing, context limits, retention and jurisdiction risk. The list price is the start of the comparison.

TL;DR

  • Write a pass or fail test for a real task before you compare vendors.
  • Compare cost per completed task, including retries and every model call.
  • Use batch or flex for work that can wait; both are half the standard OpenAI price.
  • Check context pricing, retention, regional processing and procurement exposure.
  • Keep a second tested model and a thin abstraction so switching stays cheap.

Disclosure: ZeroRaised publishes this analysis and is a launch site for bootstrapped products. Facts come from the linked sources as checked on 26 Oct 2026; ZeroRaised has no commercial relationship with the companies named.

Cost per task is the total amount an API charges to complete one real job, including input, output, cache writes, retries and tool calls, rather than the advertised rate for a single token. Model portability is the practical ability to move a workload to another provider without rewriting the product around one vendor's API, prompts or pricing assumptions.

What should you test before you compare prices?#

Start with twenty or fifty real requests, a written pass or fail test, and the same set for every model. Vendor benchmarks are context, not proof. Kimi reports Terminal-Bench 4.0 scores of 65.7% for K3.1 Pro, 58.2% for GPT-6 Astra and 66.4% for Claude Opus 5.5, all vendor-reported (Kimi). Artificial Analysis scored Kimi at 55, behind Claude Opus 5.5 at 58 and ahead of three models at 53, according to Simon Willison, who calls it "the first time a (soon-to-be) open weight model has scored this highly against the frontier" (Simon Willison).

Those numbers do not tell you whether a model can classify your support tickets. Your test set does.

How do you calculate cost per task?#

Count input, output, cache writes, cache reads, retries and tool calls, then divide by passing tasks. OpenAI lists gpt-6.1-sol at $2.00 input and $10.00 output per million short-context tokens, cached input $0.10; above 272K input the rate is $4.00 and $15.00 (OpenAI pricing). Regional processing adds 10% for models released on or after 5 Mar 2026 (OpenAI pricing). Kimi lists K3.1 Pro at $2.00 and $10.00, cached input $0.20, a 1,048,576-token context and "flat per-token pricing without context-length tiers" (Kimi pricing).

Which pricing levers matter most?#

Batch and flex are the easiest savings for work that can wait. OpenAI prices both at half the standard rate, so gpt-6.1-sol is $1.00 input and $5.00 output against $2.00 and $10.00 (OpenAI pricing). Nightly summaries, backfills and evaluation runs fit this pattern.

Caching matters when requests begin with the same instructions: cached input for gpt-6.1-sol is $0.10 per million tokens against $2.00 uncached (OpenAI pricing). Put stable instructions first and variable content later. Our guide to reducing LLM costs covers the mechanics.

Model tier is the biggest lever: gpt-6-luna lists $0.10 input and $0.50 output, against $2.00 and $10.00 for gpt-6.1-sol and $10.00 and $50.00 for gpt-6-astra (OpenAI pricing). Watch dates: "GPT-5.6 Sol's promotional pricing is available at least through November 21, 2026" (OpenAI pricing). Kimi K3.1 Pro standard is one third lower than K3, listed at $3.00 input and $15.00 output (Kimi pricing).

Why is the discount war relevant to a small team?#

Because list prices move, and the models behind them move too. The Decoder, citing The Information, reports OpenAI pulling major customers from Anthropic with discounts of up to 90% (The Decoder). Rippling CEO Parker Conrad said OpenAI's share of its coding workload rose from 16 to 52 percent while Anthropic's fell from 57 to 18 percent, and Replit switched after more than 30 percent off a contract worth over $100 million a year (The Decoder).

OpenAI also cut prices through its Sol and Luna lines, while Anthropic's growth slowed from Q2 to Q3 (The Decoder). A bootstrapped team will not get a custom contract. The lesson is to avoid a margin that depends on one vendor's promotional rate, and to keep a second model tested.

What data retention and jurisdiction risks should you check?#

Ask what happens to your data. Kimi says zero data retention is available for API customers (Kimi), a concrete answer for a security review.

Then ask where the model can be sold. The US Department of Homeland Security order requires federal civilian agencies to remove DeepSeek-related technology and bars procuring DeepSeek products and services, "including third-party products and services developed using its technology" (CNBC). The Federal Register notice removes "third-party products and services developed using DeepSeek implementations" from civilian agency IT (Federal Register).

The order was issued 24 July and made public on 19 Oct. It covers agencies such as Treasury, State and Justice, excludes the Department of Defense, and allowed exceptions within 60 days (CNBC). If you sell to government or regulated buyers, a model bill of materials is the minimum evidence.

How do you keep your stack portable?#

Keep model choice out of hard code. Put prompts, model names and fallback order in configuration, and route calls through one internal function or a gateway. Our guide to rotating API keys covers credentials.

Weigh open weights carefully. Kimi calls K3.1 Pro "our most capable 3T-class open-weight model to date, with a 1-million-token context window" and says full weights will be released by 4 Nov 2026 (Kimi). As of 23 Oct they had not been released, and VentureBeat reported allegations that influencers were asked to promote it without disclosure (VentureBeat). Open weights reduce lock-in only if you can run and maintain them.

What this means for bootstrapped founders#

  1. Pick one task and write a pass or fail test.
  2. Measure cost per completed task on two or three models.
  3. Put stable instructions first; send work that can wait to batch or flex.
  4. Check long-context pricing, regional uplift and promotional end dates.
  5. Ask vendors about retention and jurisdiction, and record the answers.
  6. Keep a model bill of materials and a second tested model ready.
  7. Recheck the bill and the rules each quarter.

For pricing, see how to price a SaaS product. If you are still deciding how much autonomy to build, AI agent vs AI workflow covers the architecture choice.

Where ZeroRaised fits#

ZeroRaised is a launch and discovery site for products built without institutional venture capital or accelerator equity. If you have a product people can try, submit it here. The newsletter carries new analysis, and the pricing page explains the listing options. A launch page is a good place to state which models you use and what data you retain, because careful buyers ask.

Sources#

Frequently asked questions

How do I compare LLM APIs fairly?

Start with a set of real tasks and a pass or fail test for each. Run the same set on each candidate model, record input tokens, output tokens, cache use, retries and calls per task, then calculate cost per completed task. A model that is cheaper per token can still cost more per job if it uses more tokens or needs more attempts.

Is the cheapest API per token the cheapest to run?

Not necessarily. Cost per task depends on how many tokens the model reads and writes, how much context you have to resend, whether caching works and how many calls the task needs. Long context is priced higher on some APIs, and a cheaper model that fails more often can cost more once retries are counted.

Should I use an open-weight model to save money?

Test it against your own tasks first. Kimi K3.1 Pro lists $2 per million input tokens and $10 per million output tokens with a 1,048,576-token context and flat per-token pricing, and its full weights are due by 4 Nov 2026. Check the quality benchmarks, who hosts it, data handling and the operations burden before moving production traffic.

What data retention questions should I ask an LLM vendor?

Ask whether inputs and outputs are retained, for how long, and whether they are used to improve models. Kimi says zero data retention is available for API customers. Also ask where processing happens, because regional processing can change both the price and the procurement review.

How do I protect my product from a model ban?

Keep a model bill of materials that lists every model and provider in the product, including models reached through third-party tools. Test a second model on the same evaluation set and keep prompts and model choice in configuration rather than hard-coded across the codebase. The US DeepSeek order covers third-party products developed using its technology, which is the kind of rule a buyer may ask you to evidence.