Two mid-tier models landed inside a week of each other, and the first number everyone reaches for — output price — comes out a tie. GPT-5.6 Terra and Kimi K3 both bill output at $15 per million tokens. On an agent, where output volume is the bill, that means the sticker-price comparison is over before it starts. The interesting decision is what the identical $15 is buying you on each side.

The two, in one screen#

GPT-5.6 Terra is the middle tier of OpenAI's GPT-5.6 line — flagship Sol, mid Terra, cheap Luna — generally available since July 9. It runs $2.50 input / $15 output per million, carries a ~1.05M-token context with 128K max output, and lands at roughly half the cost of Sol while giving up little of its quality; independent trackers put its intelligence well above the median for its price tier. It is a closed, managed model, hosted by OpenAI and wired into Codex, ChatGPT Work, and the API.

Kimi K3 is Moonshot's 2.8-trillion-parameter (A50B active) flagship, and this week is when it got interesting for builders: the full open weights went public on July 26. It bills $3 input / $15 output per million with a $0.30 cache-hit input rate, carries a ~1M context, and reports near-Opus-4.8-class quality — the kind of number that used to require a closed frontier model. You can consume it via the Moonshot API or day-0 third-party hosts, or download it.

Where the tie breaks#

Since output is a wash, the decision moves to three places the price tag hides.

Cache-hit rate. Terra's input is nominally cheaper ($2.50 vs $3.00), but K3 prices a cache hit at $0.30 per million input tokens. If your agent replays a large stable prompt prefix — a long system prompt, a fixed toolset, a retrieved document set — the input side tilts hard toward K3. If your inputs are mostly fresh each call, Terra's flat $2.50 wins. This is the single most under-modeled number in a model switch, and it depends entirely on your traffic shape. It is also exactly the kind of thing one tokens-per-second headline hides.

Lock-in. This is what the open weights actually buy. With K3 you can fine-tune the base, run it air-gapped for residency or compliance, and — the quiet one — you can never be deprecated or repriced out from under a product you shipped. Terra gives you none of that; you get whatever OpenAI serves, on OpenAI's schedule. But be honest about the cost of collecting: the weights are ~1.4TB and need roughly 18 H100s to serve, a cluster that outspends an API line until you're pushing about a billion tokens a month. For nearly everyone, "open" is an option you hold, not one you exercise — the self-host math rarely clears the API.

Ecosystem gravity. If your stack already lives in Codex and the OpenAI API, Terra is a one-line default swap with the tooling, provider coverage, and latency profile you already depend on. K3 is a newer surface — excellent third-party hosting arrived day-0, but the integration debt is real if you're not already model-agnostic.

The move#

Don't pick a "winner." At the same output price, the correct posture is to route, not choose. Send latency- and tooling-sensitive traffic to Terra — managed, integrated, predictable. Send bulk, cache-heavy, or residency-constrained traffic to K3 — cheap on cache hits, portable, yours. Put both behind one interface, log cost per completed task per route, and let the split move on evidence.

If you only have room for one default this quarter, the tiebreaker is your risk posture, not your budget: pick Terra if you want the managed runtime and tight OpenAI tooling and you trust the vendor to keep serving it; pick K3 if the thing that keeps you up at night is a model you built on getting deprecated or repriced. The $15 is the same. What you're really buying is who controls the model you shipped — and this week, for the first time at this quality tier, that's an actual choice.

See also this week's Founder's Wire for the full context on the K3 weights drop, and Kimi K3 vs Opus 5 if your comparison is against the frontier default rather than the mid tier.