GPT-5.6 Terra vs Kimi K2.7: Verified Scores vs Open-Weight Price (2026)

GPT-5.6 Terra vs Kimi K2.7 in 2026: GPT-5.6 Terra is OpenAI's balanced tier, priced at USD 2.50 per million input tokens, USD 0.25 cached, and USD 15 per million output tokens, with a 1,050,000-token context window. It scores 55 on the independent Artificial Analysis Intelligence Index and 77 on the independent Artificial Analysis Coding Index. Kimi K2.7 is Moonshot AI's open-weight agentic coding model, priced at USD 0.95 per million input tokens, USD 0.19 cached, and USD 4 per million output tokens, with a 256,000-token context and downloadable weights under a Modified MIT license. Kimi K2.7 is not yet on any independent leaderboard: the coding figures Moonshot publishes are self-reported and have not been reproduced by a third party. Terra costs roughly 2.6 times more on input and roughly 3.75 times more on output — the smallest premium any independently scored flagship charges over Kimi. Terra takes the verdict, because here the price of proof is unusually cheap.

Quick Verdict

Most of the closed-versus-open matchups this year come down to the same trade: you pay ten or twenty times more for a model somebody outside the vendor has actually measured. That framing collapses in this particular pairing, and that is what makes it worth reading.

GPT-5.6 Terra is the closest thing in the closed field to Kimi K2.7's price. Input runs USD 2.50 against USD 0.95 — roughly 2.6 times. Output runs USD 15 against USD 4 — roughly 3.75 times. Cached input is USD 0.25 against USD 0.19, a gap of about 1.3 times, which is close to noise. Compare that with the frontier tier, where the same open model is ten to twelve times cheaper than the flagship it faces, and the shape of the decision changes completely.

Meanwhile the evidence gap does not shrink at all. Terra sits on two independent Artificial Analysis indexes with a published score on each. Kimi K2.7 sits on none. Not a low score — no score. Its capability case rests entirely on numbers Moonshot AI produced on its own harness.

So GPT-5.6 Terra takes the overall verdict, and unlike the frontier matchups, it does not take it by a hair. When independently verified capability plus roughly four times the context costs you about 2.6 times more on input, the premium for proof is small enough that most teams should simply pay it.

But Kimi K2.7 wins real things, and it wins them outright:

• Kimi K2.7 wins price, on every line. USD 0.95 against USD 2.50 on input, USD 0.19 against USD 0.25 cached, USD 4 against USD 15 on output. Cheaper is cheaper, and at volume it compounds.
• Kimi K2.7 wins control. Open weights under a Modified MIT license, downloadable on day one, runnable on your own hardware. Terra cannot do this at any price.
• Kimi K2.7 wins architectural transparency. Moonshot publishes the full design. OpenAI publishes nothing comparable for Terra.
• Terra wins independently verified intelligence. 55 on the Artificial Analysis Intelligence Index. Kimi K2.7 is not on it.
• Terra wins independently verified coding. 77 on the Artificial Analysis Coding Index. No third party has published a coding result for Kimi K2.7.
• Terra wins context. 1,050,000 tokens against 256,000 — roughly four times the room.

How We Compared Them, and the Rule We Hold To

We ran both models side by side on their respective APIs — the same refactoring passes, the same long-document work, the same agentic tool-calling loops — to get a feel for how each behaves in practice. That hands-on time informs the judgment calls here: how a model holds a long context, how it recovers when a tool call fails, how much supervision it needs before you can leave it running.

What it does not do is produce benchmark numbers. We do not publish our own scores, because a handful of prompts run by one team is not a benchmark. Every figure on this page comes either from a named independent evaluator or from the vendor, and we label which is which, every single time.

That labeling rule is usually a footnote. In this comparison it is the whole story, so here it is spelled out:

• Independent means measured by a third party with no stake in the result — Artificial Analysis, LMArena, or vals.ai. Those numbers are comparable across models, because the same harness ran them all under the same conditions.
• Vendor self-reported means the company that built the model ran the benchmark itself, on its own harness, and published the outcome. That is useful signal. It is not verification. It is not comparable to an independent score, and it is not reliably comparable to another vendor's self-reported score either, because no two vendor harnesses are alike.

There is a second rule that applies specifically to this pairing. Terra's charted coding score and Kimi's published coding claim are not the same benchmark, and they were not produced under the same evidentiary regime. Two independent reasons to never place them side by side, so we never do — not in a table row, not in a sentence, not in the infographic. Any page that stacks them is telling you a lie by layout.

GPT-5.6 Terra and Kimi K2.7 at a Glance

GPT-5.6 Terra is the middle tier of OpenAI's GPT-5.6 family — the balanced model, positioned beneath GPT-5.6 Sol and above GPT-5.6 Luna. It is priced at USD 2.50 per million input tokens, USD 0.25 per million cached input tokens, and USD 15 per million output tokens, with a 1,050,000-token context window. It is a closed model: no weights, no self-hosting, no published architecture. What OpenAI offers in place of transparency is external measurement. Terra carries a score of 55 on the independent Artificial Analysis Intelligence Index and 77 on the independent Artificial Analysis Coding Index — both produced by an evaluator with no commercial stake in the outcome.

Kimi K2.7 is Moonshot AI's open-weight agentic coding model, released on June 12, 2026 from the company's Beijing headquarters. It is a mixture-of-experts design: roughly 1 trillion total parameters with about 32 billion active per token, spread across 384 experts of which 8 are selected per token plus 1 shared, using multi-head latent attention and a MoonViT vision encoder for native image input. The weights shipped under a Modified MIT license on day one — you can download them, run them on your own hardware, and put them inside your own product. It offers a 256,000-token context window (262,144 tokens exactly) with automatic context caching, at USD 0.95 per million input tokens, USD 0.19 cached, and USD 4 per million output tokens.

Read those two paragraphs again and notice what is missing from each. OpenAI will not tell you how Terra is built. Moonshot will not show you a number anyone else produced. Each company is opaque about precisely the thing the other is open about — and which opacity you can live with is, in the end, this entire comparison.

The Part That Matters: One of These Models Has Been Checked

Here is the situation in plain terms.

GPT-5.6 Terra, independently measured:

• Artificial Analysis Intelligence Index: 55 (independent).
• Artificial Analysis Coding Index: 77 (independent).

Kimi K2.7, independently measured:

• Artificial Analysis Intelligence Index: not yet on the independent leaderboard (too new).
• LMArena: not yet ranked.
• SWE-bench Verified, SWE-bench Pro, Terminal-Bench, LiveCodeBench, GPQA, AIME: no independent third-party results exist as of June 15, 2026.

That second list is not a rhetorical device. It is the complete state of independent evidence for Kimi K2.7: there is none. Not a weak score — no score.

What Moonshot AI has published, on its own harness, is a SWE-bench Pro result of 58.6 and a SWE-bench Verified result of 60.4 percent, which the company describes as a new high-water mark for open-source models. Those figures may well be accurate. Moonshot has a reasonable track record, and the prior model in the line was independently evaluated in due course. But as of this writing, nobody outside Moonshot has reproduced them, and they belong to a different benchmark family from the coding index Terra is charted on — which is the second, separate reason we never set the two against each other.

One clarification that trips up a lot of coverage: Kimi K2.6, the predecessor, does carry an independent Artificial Analysis Intelligence score. K2.7 is a different model. That number does not transfer, and any article that quietly reuses it for K2.7 is inventing a data point. We are not going to do that, so where an independent figure for K2.7 would go, this page says the honest thing: it does not exist yet.

Why an unverified benchmark is not the same as a bad one

It would be lazy — and wrong — to read all of this as "Kimi K2.7 is worse." That is not what the evidence says. The evidence says we cannot tell yet, and those are very different claims.

The honest position on Kimi K2.7 today is that it might be excellent. Its architecture is serious, its price is aggressive, its predecessor scored respectably when independently tested, and its self-reported numbers are not outlandish for a model of this design. Nothing here suggests Moonshot is exaggerating.

What it means is that buying Kimi K2.7 today is buying on the vendor's word, while buying GPT-5.6 Terra is buying on someone else's proof. For a hobbyist, that distinction is close to meaningless: run it, see if you like it, the price makes the experiment nearly free. For an engineering lead who has to stand in front of a room and justify why a model was chosen for a production system, it is the whole ballgame.

And it is temporary. Independent evaluators typically reach a model of this profile within weeks of release, not months. Kimi K2.7 will very likely appear on the Artificial Analysis Intelligence Index and on LMArena before long, and when it does, this section becomes obsolete and this verdict should be revisited. If those independent numbers land close to what Moonshot claims, the case for a model at roughly a third of Terra's input price gets strong very quickly. We will update this page when that happens.

Pricing: The Narrowest Gap in the Closed Field

These are the published metered rates, per million tokens:

Kimi K2.7 is roughly 2.6 times cheaper on input, roughly 1.3 times cheaper on cached input, and roughly 3.75 times cheaper on output. It wins every line. It is genuinely the cheaper model and nothing below takes that away.

But hold those multiples next to the rest of the market for a second, because the comparison only makes sense in context. Against the premium closed flagships, Kimi K2.7 is ten to twelve times cheaper. Against Terra it is under three times cheaper on input and under four on output. On cached input, the two are within a rounding error of each other — USD 0.25 against USD 0.19 is a difference of six cents per million tokens.

Put a number on it. A workload burning 50 million output tokens in a month — not extreme for a team running coding agents continuously — costs USD 750 on Terra and USD 200 on Kimi K2.7. That is a gap of USD 550 a month. It is real money and you should not wave it away, but it is not the kind of gap that reorganizes a budget, and it is a fraction of one engineer's monthly cost. In the frontier tier the same workload produces a gap ten times larger.

This is the crux of the whole page. The question is never "is the closed model cheaper?" — it never is. The question is what you are paying for the difference, and what you get back. Here you are paying a couple of hundred dollars a month at moderate volume, and getting back two independently produced scores and roughly four times the context. That is an unusually good exchange rate for proof.

It stops being a good exchange rate at scale. At 500 million output tokens a month the gap becomes USD 5,500 a month, and at that point the arithmetic starts arguing for Kimi loudly. Multiply your own volume before you take our word for any of this.

Context Window: 1.05M Against 256K

Terra carries a 1,050,000-token context window. Kimi K2.7 carries 256,000 tokens (262,144 exactly), with automatic context caching that lowers the cost of repeated prefixes without you having to manage it.

Roughly four times the context is a real advantage, but be honest about when it actually binds. A 256,000-token window is already large enough for the overwhelming majority of coding work: a substantial repository slice, a long design document plus the code it describes, a full day of agent scrollback. If your workflow fits comfortably inside 256,000 tokens today, Terra's extra headroom is capacity you are paying for and not using.

Where it does bind: whole-monorepo reasoning, very long agentic sessions that accumulate hundreds of tool calls without compaction, and document work at the scale of full legal or research corpora. If that is your work, the gap is decisive — and Kimi's automatic caching does not close it. Caching makes a window cheaper to reuse; it does not make it bigger.

Open Weights, Self-Hosting, and What OpenAI Will Not Sell You

Kimi K2.7's weights are published under a Modified MIT license and were downloadable on day one. This is not a marketing detail. It changes what you are legally and operationally able to do:

• Run it on your own hardware. No inference data leaves your infrastructure. For teams with hard data-residency requirements, that is the difference between usable and not usable, and no vendor assurance substitutes for it.
• Fix your cost ceiling. Self-hosted inference converts a metered bill into a hardware amortization. At high volume that changes the economics entirely — and it is the one route by which Kimi's price advantage grows rather than shrinks.
• Never get deprecated out from under you. A downloaded weight file does not get sunset on a vendor's schedule. Anyone who has had a model retired mid-product knows what that is worth.
• Inspect and modify. Full architecture disclosure — mixture-of-experts, roughly 1 trillion total parameters with 32 billion active, 384 experts, multi-head latent attention, the MoonViT vision encoder — means you can reason about the model's behavior instead of guessing at it.

GPT-5.6 Terra offers none of this, and OpenAI does not pretend otherwise. It is a closed, API-only model with an undisclosed architecture. If open weights are a requirement rather than a preference, this comparison is over before it starts and Kimi K2.7 wins by default — Terra is not a candidate at any price.

One honest caveat on the openness point, because it cuts both ways: open weights under a Modified MIT license is a genuine grant, but open-weight is not the same as open-source. Moonshot publishes the finished model, not the training data or the training code. You get the artifact, not the recipe. For deployment freedom that distinction rarely matters; for auditability and reproducibility, it does.

A second caveat that matters for the vision claim: Kimi K2.7 ships a native vision encoder, and Terra's published specification does not describe its multimodal handling in comparable terms. We are not going to score that as a clean win for either side, because we would be comparing a disclosed capability against an undisclosed one — the same mistake as stacking an independent score against a vendor one.

Winner by Category

Pros and Cons

GPT-5.6 Terra

Pros

• Independently verified on two separate indexes — 55 on the Artificial Analysis Intelligence Index and 77 on the Artificial Analysis Coding Index — by an evaluator with no stake in the result.
• The smallest price premium of any independently scored flagship over Kimi K2.7: roughly 2.6 times on input, roughly 3.75 times on output.
• Cached input at USD 0.25 per million tokens is nearly level with Kimi K2.7's USD 0.19, which makes prompt-heavy, cache-friendly workloads cost almost the same on both.
• 1,050,000-token context window, roughly four times Kimi K2.7's.
• Managed API with no infrastructure to run, and a clear ladder to Sol above it or Luna below it if your needs shift.

Cons

• Still the more expensive model on every single line, and output at USD 15 per million tokens hurts on agentic workloads that generate long traces.
• Closed weights. No self-hosting, no data residency through hardware control, no protection against deprecation.
• Undisclosed architecture — you cannot inspect or reason about how it works.
• At very high volume the cost gap compounds into real money, and there is no self-hosted escape hatch.

Kimi K2.7

Pros

• Cheaper on every line: USD 0.95 per million input tokens, USD 0.19 cached, USD 4 per million output tokens.
• Open weights under a Modified MIT license, downloadable from day one and self-hostable on your own hardware.
• Full architecture disclosure — mixture-of-experts, roughly 1 trillion total parameters with 32 billion active, 384 experts, multi-head latent attention.
• Native vision through the MoonViT encoder.
• Purpose-built for agentic coding, with automatic context caching that needs no management.

Cons

• No independent verification of any kind. Not on the Artificial Analysis Intelligence Index, not on LMArena, no third-party coding result.
• Its published coding figures are self-reported by Moonshot AI on its own harness and have not been reproduced by anyone else.
• 256,000-token context, roughly a quarter of Terra's.
• Its cost advantage over Terra specifically is narrower than its reputation suggests — under three times on input, and only about 1.3 times on cached input.
• The hosted API is operated from Beijing, which carries its own compliance considerations. Self-hosting the weights avoids that; using the hosted API does not.

When to Pick GPT-5.6 Terra

• You have to justify the choice to someone. A review board, a client, a regulator, a CTO who asks how you know the model is any good. Terra is the only one of the two where the answer is a third-party number rather than a vendor's announcement.
• Your volume is moderate. At tens of millions of output tokens a month, the price gap is in the hundreds of dollars. Verified capability plus four times the context for that is a bargain, and you should take it.
• Your workload is cache-heavy. If most of your tokens are repeated prefixes hitting the cache, you are comparing USD 0.25 against USD 0.19 — and at that point the price argument for Kimi has nearly evaporated.
• You routinely exceed 256,000 tokens of context. Whole-monorepo work, very long agentic sessions, large document corpora.
• You cannot run your own evaluations. If you have no internal harness, the independent scores are the only real information you have, and only one of these models has any.

When to Pick Kimi K2.7

• You need to self-host. Data residency, air-gapped environments, regulated industries. Terra simply cannot do this, so the comparison ends there.
• Your volume is very high. At 500 million output tokens a month the gap is USD 5,500 a month, and self-hosting the weights caps it entirely. Scale is where Kimi's case gets loud.
• You can run your own evaluations. This is the key one. The missing independent scores hurt far less if you have an internal harness on your own tasks, because then you are not taking Moonshot's word for anything — you are measuring it yourself, on the only benchmark that truly matters, which is yours.
• You want protection from deprecation. A downloaded weight file is yours. An API endpoint is not.
• You need to inspect or modify the model. The architecture is public and the license permits it. Terra offers neither.

Final Verdict

GPT-5.6 Terra wins this comparison, and it wins it on an exchange rate rather than on a knockout.

Kimi K2.7 takes more rows in our table than Terra does, and we still call it for Terra, so we owe you the reasoning rather than a shrug.

Three of Kimi's row wins — input, cached input, output — are the same axis counted three times. It is one advantage, cost, and it is a genuine one. Two more, open weights and self-hosting, are also closely related: they are the control axis. Kimi K2.7's case, honestly stated, is that it is cheaper and you can own it. For a large number of teams that is decisive, and we are not going to pretend otherwise.

Terra's row wins are of a different kind. They are the rows that answer the question "does this model actually do the job?" — and both of the capability rows are only answerable because somebody other than the vendor checked. Against a competitor that no independent evaluator has looked at yet, that is not a tie-breaker. It is the only hard information on the table.

What tips it decisively here, more than in any other closed-versus-open pairing this year, is the size of the bill. Terra is the closest a verified model gets to Kimi K2.7's price. Roughly 2.6 times on input and 3.75 times on output is a premium most teams can absorb without a conversation, and cached input is close to level. You are buying proof and four times the context for a few hundred dollars a month at moderate volume. Elsewhere in the market that same proof costs ten to twelve times more. Take the deal.

So the rule we would give you. If you can measure the model yourself on your own tasks, or you need to self-host, or your volume is enormous, pick Kimi K2.7 — your own evaluation replaces the missing independent one, and the weights are yours. Otherwise pick GPT-5.6 Terra, because the price of proof has rarely been this low.

Where our verdict is wrong: if independent results land for Kimi K2.7 in the coming weeks and confirm what Moonshot claims, then an open-weight model at roughly a third of Terra's input price with credible verified numbers reshapes this comparison and quite a few others. That outcome is plausible. It has simply not happened yet, and we do not publish verdicts on things that have not happened.

To see how each model fares against the rest of the field, we have Kimi K2.7 against Claude Opus 4.8, against GPT-5.5, and against DeepSeek V4 — the other open-weight contender, whose page is worth reading if price is your binding constraint. For the wider field, see our roundup of the best AI coding tools in 2026.

Frequently Asked Questions

Is GPT-5.6 Terra or Kimi K2.7 cheaper?

Kimi K2.7, on every line, but by less than its reputation suggests. It charges USD 0.95 per million input tokens against Terra's USD 2.50, which is roughly 2.6 times cheaper. On output it charges USD 4 per million tokens against USD 15, roughly 3.75 times cheaper. On cached input the two are nearly level: USD 0.19 against USD 0.25. That makes GPT-5.6 Terra the closest an independently scored closed model comes to Kimi K2.7 on price — elsewhere in the market the same open model is ten to twelve times cheaper than the flagship it faces.

Does Kimi K2.7 have an Artificial Analysis Intelligence Index score?

No. As of June 15, 2026, Kimi K2.7 is not yet on the independent leaderboard (too new). There are no third-party results for it on the Artificial Analysis Intelligence Index, LMArena, SWE-bench Verified, SWE-bench Pro, Terminal-Bench, LiveCodeBench, GPQA, or AIME. Be careful with articles that quote an index score for it — figures in that range belong to Kimi K2.6, the predecessor model, and they do not transfer to K2.7. GPT-5.6 Terra, by contrast, scores 55 on the independent Artificial Analysis Intelligence Index.

Why do you not compare the two models' coding scores directly?

Two separate reasons, and either one alone would be enough. First, they are not the same benchmark: GPT-5.6 Terra is charted on the Artificial Analysis Coding Index, while Moonshot AI reports SWE-bench results for Kimi K2.7. Second, and more importantly, they were not produced the same way: Terra's figure comes from an independent evaluator, and Kimi's comes from the company that built the model, running its own harness. Putting the two numbers in one row would imply a like-for-like measurement that does not exist, so you will not find them side by side anywhere on this page.

Does the absence of independent scores mean Kimi K2.7 is a bad model?

No, and it is important to be precise. It means Kimi K2.7 is currently unverifiable, not that it is weak. Its architecture is serious, its self-reported figures are not implausible for a model of its design, and its predecessor performed respectably when independently tested. The honest position is that we cannot yet tell how good it is from outside evidence. Independent evaluators usually reach models of this profile within weeks of release, so this is likely a temporary state, and we will revisit this comparison when third-party results appear.

Which model won your overall verdict, and why?

GPT-5.6 Terra, and less narrowly than in most closed-versus-open matchups. It is the only model of the two whose capability anyone outside the vendor has measured: 55 on the independent Artificial Analysis Intelligence Index and 77 on the independent Artificial Analysis Coding Index. What tips it is the price. Terra is roughly 2.6 times more expensive on input and 3.75 times on output — the smallest premium any independently scored flagship charges over Kimi K2.7. Verified numbers plus roughly four times the context for that surcharge is a good exchange rate. Kimi K2.7 still wins price, open weights, self-hosting, and architectural transparency outright.

Can I self-host either of these models?

Kimi K2.7, yes. Its weights were published under a Modified MIT license on release day and can be downloaded and run on your own hardware, which makes it viable for data-residency requirements, air-gapped environments, and fixed-cost inference. GPT-5.6 Terra, no. It is a closed, API-only model from OpenAI with no downloadable weights and no self-hosting path. If self-hosting is a hard requirement, Terra is not a candidate at any price and this comparison resolves immediately in Kimi K2.7's favor.

What is the context window on each model?

GPT-5.6 Terra offers a 1,050,000-token context window. Kimi K2.7 offers 256,000 tokens (262,144 exactly), with automatic context caching that lowers the cost of reusing repeated prefixes. Terra therefore has roughly four times the room. Whether that matters depends entirely on your work: 256,000 tokens already covers most coding tasks comfortably, and if your workflow fits inside it, Terra's extra headroom is capacity you are paying for and not using.

At what volume does Kimi K2.7 become the obvious choice on cost?

Run your own arithmetic, but the shape is this. A workload generating 50 million output tokens a month costs about USD 750 on GPT-5.6 Terra and about USD 200 on Kimi K2.7 — a gap of roughly USD 550, real but not budget-reorganizing. Multiply by ten and the gap becomes about USD 5,500 a month, at which point the cost argument gets loud, and self-hosting the open weights caps it entirely. Below moderate volume, the difference is small enough that the verified scores and the larger context are worth more than the savings.

Is Kimi K2.7 open source?

It is open-weight, which is not quite the same thing. Moonshot AI publishes the model weights under a Modified MIT license, and they were downloadable from day one — you can use them commercially, run them on your own hardware, and ship them inside your own product. What Moonshot does not publish is the training data or the training code. You get the finished model, not the recipe. For deployment freedom that distinction rarely matters in practice; for auditability and reproducibility, it does.

What is Kimi K2.7's architecture?

It is a mixture-of-experts model with roughly 1 trillion total parameters and about 32 billion active per token. It uses 384 experts, of which 8 are selected per token plus 1 shared expert, and it employs multi-head latent attention. It also ships a native vision encoder called MoonViT. Notably, this level of architectural detail is public for Kimi K2.7 and has no equivalent for GPT-5.6 Terra, whose architecture OpenAI does not disclose — one of the few areas where Moonshot is clearly the more transparent of the two companies.

How does GPT-5.6 Terra compare to the other models in its family?

Terra is the balanced middle tier of the GPT-5.6 family. GPT-5.6 Sol sits above it, priced higher and scoring higher on the independent Artificial Analysis indexes; GPT-5.6 Luna sits below it as the cheaper, lighter option. Terra is the tier we would point most teams at for this particular matchup, because it is the one whose price lands close enough to Kimi K2.7 that the premium for independently verified numbers stops being an argument.

Will this verdict change?

Quite possibly, and we will say so plainly when it does. The verdict rests on Kimi K2.7 having no independent verification as of June 15, 2026. Independent evaluators typically reach models of this profile within weeks of release, so Kimi K2.7 is likely to appear on the Artificial Analysis Intelligence Index and LMArena before long. If those results confirm Moonshot AI's self-reported figures, then an open-weight model at roughly a third of GPT-5.6 Terra's input price with credible verified numbers changes this comparison substantially. We will update this page when independent results land.