Here's the trap the early-July model launches set for founders: whichever LLM API you commit to this week will look overpriced within a quarter. GPT-5.6 just undercut GPT-5.5. Grok 4.5 undercut Opus. Open-weight GLM-5.2 undercut all of them on parts of the coding suite. The prices are falling unevenly — which model is cheapest-for-your-quality-bar keeps changing.

So the goal isn't to pick the right API. It's to stay able to switch. Here's the playbook.

Step 1: Route through an OpenAI-compatible interface#

The single most valuable decision is to make the provider a value, not a code path. Nearly every serious model — OpenAI, Anthropic, Grok, and open-weight models served by vLLM — now speaks the OpenAI Chat Completions format. Point your client at a gateway and the provider becomes one string.

from openai import OpenAI

# Same SDK, same call shape — only base_url + model change.
client = OpenAI(
    base_url="http://localhost:4000",   # a LiteLLM gateway (or https://openrouter.ai/api/v1)
    api_key="sk-...",
)

resp = client.chat.completions.create(
    model="gpt-5.6-luna",               # swap to "grok-4.5" or "glm-5.2" with no other change
    messages=[{"role": "user", "content": "Summarize this ticket."}],
)

A gateway like LiteLLM (self-hosted) or a marketplace like OpenRouter (hosted) gives you one key, one bill, one place to log spend, and — critically — one line to change when a cheaper model clears your bar. If you're pre-launch and don't want the extra hop yet, that's fine: just keep your calls OpenAI-shaped so adding the gateway later is a base_url change, not a migration.

Step 2: Build the eval set that makes switching safe#

A cheaper model is only cheaper if it still does the job. You can't know that from a leaderboard — benchmarks aren't your workload. Before you're tempted by the next price cut, capture 20–50 real examples of your actual task (real tickets, real code diffs, real extractions) with the outputs you'd accept.

Then switching becomes a measurement, not a leap of faith:

for case in eval_set:
    out = client.chat.completions.create(model=CANDIDATE, messages=case.messages)
    case.record(out, model=CANDIDATE)   # score against your accept/reject bar
# Ship the swap only if pass-rate holds AND cost/latency improve.

This is the difference between "we heard Grok is cheaper" and "Grok holds 96% of our pass rate at 40% of the cost, so we're routing tier-2 traffic to it." One is a rumor; the other is a decision.

Step 3: Do the math on output tokens, not the sticker price#

Input price is the number vendors advertise; output price is the one that bills you. Across the current tiers, output runs 3–6x input — GPT-5.6 Luna is $1 in / $6 out, Grok 4.5 is $2 / $6. Agent and summarization workloads are output-heavy, so a model with a low input price and a high output price can be the expensive choice for you.

Estimate real spend per request:

cost ≈ (input_tokens × input_price + output_tokens × output_price) ÷ 1,000,000

Multiply by your request volume before you fall for a headline input price. For a chat product that generates long replies, the output column decides everything.

Step 4: Know when open weights win#

Hosted APIs are the right default — until one of three things is true:

  1. Predictable high volume. Above a steady throughput threshold, self-hosting an open-weight model like GLM-5.2 on vLLM beats per-token pricing — if your GPUs stay busy. Idle GPUs erase the savings instantly.
  2. Data residency or compliance. If data can't leave your infrastructure, an open-weight model on your own hardware isn't a cost decision — it's the only decision.
  3. Capping vendor power. Even if you never self-host, the existence of a near-frontier open model (MIT-licensed, $1.40/$4.40 hosted) caps how much any closed vendor can charge you for comparable quality. Keep one in your eval set as a live threat.

Below those thresholds, rent the open weights through a host (Together, Fireworks, OpenRouter) and skip the ops entirely.

The takeaway#

Treat the model as a swappable input from day one. Route through an OpenAI-compatible layer, keep an eval set that turns "is it cheaper?" into a number, price on output tokens, and hold an open-weight option in reserve. Do that, and every price war — and there will be one every quarter — becomes a tailwind instead of a migration. For the market context driving all of this, see our founder's read on why the model got cheap while the money concentrated; for the developer-level tier breakdown, GPT-5.6 Sol vs Terra vs Luna.