The one-line version: OpenAI's July 30 price cut dropped GPT-5.6 Luna to $0.20 / $1.20 per million input/output tokens — and because a coding agent's bill is dominated by output tokens, that single change makes the cheap tier roughly 12x cheaper on output than Kimi K3 and 25x cheaper than GPT-5.6 Sol. It doesn't crown a new winner. It moves the price floor, and it re-opens the one lane — moderate-difficulty, high-output bulk — that Kimi K3 used to win on price alone. Before you route real work there, measure **cost per completed task**, not cost per token.
What actually changed on July 30#
OpenAI cut API prices on the two cheaper tiers of its GPT-5.6 family (launched July 9). Luna fell about 80%, to $0.20/M input and $1.20/M output (from roughly $1/$6). Terra fell about 20%, to $2/$12. The flagship Sol tier held at $5/$30 (OpenAI; CNBC; VentureBeat).
Three days earlier, on July 27, the mid-tier decision was a tie: GPT-5.6 Terra and Kimi K3 both billed output at $15/M, so that comparison turned on optionality, not price. The July 30 cut broke that tie from below — not by moving Terra or Kimi K3, but by dropping a cheaper tier, Luna, to $1.20/M output. And our July flagship comparison, Kimi K3 vs Opus 4.8 vs GPT-5.6 Sol, priced Luna out of the frame entirely at its old rate. Both premises just changed: there is now a frontier-lab tier sitting below Kimi K3's hosted output rate. That's the news, and it's worth recomputing.
Why output price is still the number that matters#
A coding agent reads a few files and then writes — diffs, new tests, retries, explanations. The bill is dominated by output tokens, not input. So the tier that's cheapest on output is the one that changes your unit economics. Here is the whole decision on one card, with the two closed cheap tiers now in it:
| Model / tier | Input $/M | Output $/M | Weights | Where it earns its slot |
|---|---|---|---|---|
| GPT-5.6 Luna | $0.20 | $1.20 | Closed | New cheap floor — moderate-difficulty, high-output bulk (pending your eval) |
| GPT-5.6 Terra | $2.00 | $12.00 | Closed | Mid capability at mid price; the hedge |
| Kimi K3 | $3.00 ($0.30 cache) | $15.00 | Open | Frontend Arena #1; self-hostable; no lock-in |
| Claude Opus 4.8 | $5.00 | $25.00 | Closed | Hardest real-repo fixes — SWE-bench Pro 69.2% |
| GPT-5.6 Sol | $5.00 | $30.00 | Closed | Terminal / multi-agent loops — Terminal-Bench 88.8% |
On output alone, Luna's $1.20/M is about 12x below Kimi K3's $15, 21x below Opus 4.8's $25, and 25x below Sol's $30. For a token-heavy generation workload, that is not a rounding difference — it's the kind of gap that turns a shelved always-on feature back on.
The number the price card can't show you#
Here's the trap, and it's the same one every cheap tier sets: the card shows price, not capability. Luna is the entry tier of the GPT-5.6 family. In our July comparison it was the flagship Sol — not Luna — that topped Terminal-Bench 2.1 at 88.8%, and Claude Opus 4.8 that led real-repository bug-fixing on SWE-bench Pro at 69.2% (CodingFleet). The cheap tier trades some of that capability for the lower rate. How much it trades on your codebase is the only question that matters, and no pricing page answers it.
So the metric to switch on is **cost per completed task, including retries** — not cost per token. A model that's 25x cheaper per token but needs three attempts to land a multi-file fix can be more expensive, in tokens and in wall-clock, than one that lands it first try. This is exactly where cheap models fail quietly: they look strong on single-shot generation and then degrade silently inside a long agent loop. Instrument before you migrate.
The recomputed routing rule#
Don't switch your whole agent to Luna. Route by task, with the price floor updated:
- Moderate-difficulty, high-output bulk → pilot GPT-5.6 Luna. This is the lane Kimi K3 owned on price. Luna now undercuts it; whether it keeps the quality on frontend, boilerplate, and refactors is what your pilot decides.
- Portability or self-hosting matters → Kimi K3. Open weights are a floor a closed cheap tier can't give you: no vendor can reprice or retire a model you hold. If lock-in is the risk you're managing, K3 still wins this slot even at $15/M output.
- Hard, multi-file repo fixes → Claude Opus 4.8. Top SWE-bench Pro; fewer retries on the tasks you can't afford to get wrong.
- Terminal / orchestration loops → GPT-5.6 Sol. Unchanged by the cut, still top Terminal-Bench.
The prerequisite hasn't changed: make the pipeline model-swappable so routing is a config change, not a refactor. If your agent is hardwired to one vendor's SDK and prompt format, you can't cost-route — and a week like this one, where the cheapest capable option flips overnight, is exactly when that rigidity costs you the most.
What to do this week#
- Split your token bill by task class, input vs output. You can't route what you can't measure.
- Pilot Luna on your highest-output, lowest-risk class — frontend or boilerplate — and compare cost-per-completed-task against whatever you run there now.
- Keep your hardest eval set on Opus 4.8 or Sol as the quality floor, and re-test the cheap tiers against it monthly. Prices move weekly; your eval is the thing that keeps the savings honest.
- Re-check the raw prices against each vendor's page before you hard-code them into a budget. The figures here were current at the July 30 cut; this market does not sit still.
