GPT-5.6 Terra vs Kimi K2.6: 11 Index Points Against a 2.6x Price Gap (2026)

GPT-5.6 Terra vs Kimi K2.6 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. Kimi K2.6 is Moonshot AI's open-weight flagship, released on April 20, 2026, priced at USD 0.95 per million input tokens, USD 0.16 cached, and USD 4 per million output tokens, with a 256,000-token context and downloadable weights under a Modified MIT license. It scores 44 on the same independent index. That is an eleven-point capability gap measured by the same evaluator on the same scale, against a price gap of roughly 2.6 times on input and 3.75 times on output. GPT-5.6 Terra takes the verdict on measured capability and context. Kimi K2.6 wins price, open weights, self-hosting, and agent orchestration outright — and this is the closest an open model has come to making the closed one look optional.

Quick Verdict

Nearly every closed-versus-open comparison this year has been fought on a technicality: the open model had no independent score, so there was nothing honest to line the two models up on. That excuse is gone here. Kimi K2.6 and GPT-5.6 Terra are both scored on the Artificial Analysis Intelligence Index, by the same evaluator, on the same version of the index. For once the comparison is a real one.

The numbers: Terra scores 55. Kimi K2.6 scores 44. Eleven points, measured externally, on identical terms. And Kimi charges roughly 2.6 times less on input (USD 0.95 against USD 2.50) and roughly 3.75 times less on output (USD 4 against USD 15).

So the question stops being "which one has been checked?" and becomes the harder one: is eleven points of independently measured intelligence worth paying about three times more?

Our answer is yes, and GPT-5.6 Terra takes the overall verdict — but it is the narrowest call we have made in this family. Eleven points is not cosmetic on this scale. The best score anyone has posted on this index to date is 60. Terra at 55 sits inside the leading group; Kimi K2.6 at 44 sits with the strong open-weight mid-tier. That is a difference in what the model can actually finish without supervision, and on agentic work an unfinished task gets retried, which costs tokens the cheaper model was supposed to save you.

But Kimi K2.6 wins real things, and it wins them outright:

• Kimi K2.6 wins price, on every line. USD 0.95 against USD 2.50 on input, USD 0.16 against USD 0.25 cached, USD 4 against USD 15 on output.
• Kimi K2.6 wins measured intelligence per dollar. Divide the index score by the output price and Kimi returns about eleven points per dollar against Terra's roughly four. It is beaten on capability and it still wins the efficiency argument by a factor of about three.
• Kimi K2.6 wins control. Open weights under a Modified MIT license, downloadable, runnable on your own hardware. Terra cannot do this at any price.
• Kimi K2.6 wins agent orchestration. Moonshot ships a swarm mode that coordinates up to 300 subagents across as many as 4,000 steps. OpenAI publishes no comparable orchestration ceiling for Terra.
• Terra wins independently measured intelligence. 55 against 44 on the same index.
• Terra wins context. 1,050,000 tokens against 256,000 — roughly four times the room.
• Terra wins independently measured coding. It carries a charted score on the Artificial Analysis Coding Index. Kimi K2.6 has no independent coding result at all, which is a separate matter we handle carefully below.

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 on this page: 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 unattended.

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.

• 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.

Two consequences follow, and both shape how this page is built.

First, the good news. On intelligence, both models have been measured by the same independent evaluator on the same version of the index. That comparison is legitimate, and we make it directly and without hedging: 55 against 44.

Second, the constraint. On coding, they have not. One of these models carries an independently charted coding score; the other's only published coding figure was produced by the company that built it. Those two numbers are not the same benchmark and they were not produced under the same evidentiary regime — two separate reasons, either sufficient on its own, never to place them side by side. So we never do: not in a table row, not in a sentence, not in the infographic, not in the FAQ. Any page that stacks them is telling you a lie by layout, and the lie always flatters whoever published first.

GPT-5.6 Terra and Kimi K2.6 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 instead of transparency is external measurement — Terra carries a score of 55 on the independent Artificial Analysis Intelligence Index, produced by an evaluator with no commercial stake in the outcome.

Kimi K2.6 is Moonshot AI's open-weight flagship, released on April 20, 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, with a native MoonViT vision encoder of around 400 million parameters. The weights ship under a Modified MIT license — 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) at USD 0.95 per million input tokens, USD 0.16 cached, and USD 4 per million output tokens. And, unlike its successor, it has been independently measured: 44 on the same Artificial Analysis Intelligence Index that scores Terra at 55.

That last sentence is what makes this pairing worth your time. Kimi K2.6 is not a model asking you to take a vendor's word for it. It is a model that walked onto the same scoreboard as the closed field, posted a real number, and then asked why you are paying three times more.

The Eleven Points: One Index, Two Models, No Excuses

Here is the part that separates this comparison from every other closed-versus-open page on this site.

Artificial Analysis Intelligence Index, version 4.1 — the same index, the same version, the same evaluator:

• GPT-5.6 Terra: 55 (independent).
• Kimi K2.6: 44 (independent).

No asterisk, no vendor harness, no "self-reported" caveat, no missing row. Both models were run by a third party under the same conditions and both came back with a number. This is what an honest comparison looks like, and it is rarer than it should be.

Eleven points on this scale is a real gap. To calibrate it: the highest score any model has posted on this index to date is 60. Terra at 55 sits inside the leading group, a handful of points off the top. Kimi K2.6 at 44 sits at the head of the strong open-weight mid-tier — respectable, genuinely useful, and clearly a tier below the frontier. Anyone telling you those two numbers are close is selling something.

What eleven points buys you in practice is completion. On single-shot work — draft this, summarize that, answer a question — the difference is often invisible, and a well-prompted 44 will do the job. The gap opens on long, unsupervised chains: multi-file refactors, agentic loops that run for an hour, tasks where an error in step 3 quietly corrupts step 40. The stronger model needs fewer interventions and fails less often, and on agentic work a failure is not free — it gets retried, and the retry burns the tokens the cheaper model was supposed to save you.

One correction, because this number is misreported constantly

You will see Kimi K2.6 credited with an index score of 54 in a good deal of coverage. That figure is wrong, and it is wrong in a specific and traceable way: 44, not 54. The 54 comes from a previous version of the Artificial Analysis index, not the current one. Artificial Analysis re-baselines its methodology periodically, and scores from an older version are not comparable to scores from the current one — that is the entire point of versioning a benchmark.

The current, version-matched number for Kimi K2.6 is 44, and that is the only number we use on this page. It matters because the difference is not academic: 54 against Terra's 55 would be a statistical dead heat and would flip this verdict on its head. The real gap is eleven points, and any page quoting 54 against a current-version score for a closed model is comparing two different rulers and calling it a race.

Pricing: The Narrowest Gap in the Closed Field

These are the published metered rates, per million tokens:

Kimi K2.6 is roughly 2.6 times cheaper on input, roughly 1.6 times cheaper on cached input, and roughly 3.75 times cheaper on output. It wins every line, and nothing below takes that away.

But read those multiples against the rest of the market, because they only mean something in context. Against the premium closed flagships, Kimi K2.6 is eight to twelve times cheaper. Against Terra it is under three times cheaper on input and under four on output. GPT-5.6 Terra is the closest a closed, independently scored model gets to Kimi K2.6's price — which is precisely why this is the matchup worth running, and why we would not send you to the frontier tier to fight this fight.

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 K2.6. That is a gap of USD 550 a month: real money, but less than a day of an engineer's time. Multiply the workload by ten and the gap becomes USD 5,500 a month, and at that point the arithmetic starts arguing loudly for Kimi. Run your own volume through it before you take our word for anything.

The one argument Kimi K2.6 wins on the numbers alone

Here is the calculation that should keep OpenAI's pricing team up at night. Take each model's independently measured index score and divide it by its output price. Terra returns about 4 index points per dollar. Kimi K2.6 returns about 11. On measured intelligence per dollar, the open model wins by roughly a factor of three — and both sides of that ratio come from a third party, so it is not a marketing claim.

That ratio is the honest case for Kimi K2.6, and we are not going to bury it. Terra wins on the ceiling. Kimi wins on the exchange rate. Which of those two facts governs your decision depends entirely on whether your workload is bounded by capability or by budget — and most teams know perfectly well which one they are.

Coding: Two Numbers That Do Not Belong in the Same Row

Both vendors want to sell you a coding model, so both have published a coding figure. They are not comparable, and this section exists to explain why rather than to quietly stack them.

GPT-5.6 Terra, independently measured. Terra carries a score of 77 on the Artificial Analysis Coding Index — an independent evaluation, produced by the same third party that runs the intelligence index, on a published harness that is applied identically to every model on the chart. It is a number nobody at OpenAI touched, and it can be compared with the charted coding scores of other models on that same index.

That is the whole of the independent coding evidence in this comparison. There is no second entry, because Kimi K2.6 has no independently charted coding score. No third party has published a coding result for it, and we are not going to invent one, borrow one from a sibling model, or dress up a vendor figure as though it were an external one.

Kimi K2.6, vendor self-reported. What Moonshot AI publishes for its own model is a SWE-bench Pro result of 58.6, produced on the company's own harness. Moonshot notes that this beats the figures it obtained for two well-known closed models on that same benchmark. Those numbers may well be accurate — Moonshot has a reasonable track record and its intelligence-index result held up when an independent evaluator checked it, which is more than most vendors can say. But a vendor-run benchmark is a claim, not a verification, and it belongs to a different benchmark family entirely from the coding index Terra is charted on.

So there is no coding row in our comparison table, and no coding row in the infographic. Two independent reasons, either sufficient alone: different benchmarks, and different evidentiary regimes. Placing the two figures next to each other would manufacture a like-for-like measurement that does not exist. It is the single most common way comparison pages mislead people, and it is usually done by accident, which does not make the reader any less misled.

What we can say honestly is this: on the one coding-relevant axis where both models have been externally measured — general intelligence, which correlates strongly with coding performance — Terra leads by eleven points. And on the vendor's own account, Kimi K2.6 is a serious coding model. Both of those statements are true, and neither requires stacking two incompatible benchmarks to make its point.

Context Window: 1.05M Against 256K

Terra carries a 1,050,000-token context window. Kimi K2.6 carries 256,000 tokens — 262,144 exactly. Roughly four times the room, and it 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 binds: 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 it is worth noting that Kimi's own headline feature, its swarm of up to 300 subagents, is in part an answer to the smaller window. Distributing a long task across many agents is a way of getting around a context ceiling rather than raising it. It works, but it is more machinery, and machinery is where agentic systems break.

Open Weights, Self-Hosting, and the 300-Agent Swarm

Kimi K2.6's weights are published under a Modified MIT license and are downloadable. 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. The full architecture is public — mixture-of-experts, roughly 1 trillion total parameters with 32 billion active, 384 experts with 8 selected per token plus 1 shared, and a MoonViT vision encoder of around 400 million parameters for native image input. You can reason about the model's behavior instead of guessing at it.

Then there is the swarm. Moonshot ships an agent orchestration mode that coordinates up to 300 subagents across as many as 4,000 steps, which is an unusually explicit ceiling for a vendor to publish. OpenAI publishes no comparable figure for Terra — not a lower one, none at all. We are not going to score that as a clean capability win, because an undisclosed ceiling is not the same as a low one, and we would be making exactly the mistake we spent a whole section refusing to make. What we will say is that if your architecture depends on fanning work across a large number of coordinated agents, Kimi K2.6 has told you what it supports and OpenAI has not.

GPT-5.6 Terra offers none of the openness, 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 K2.6 wins by default — Terra is not a candidate at any price.

One honest caveat, because it cuts both ways: open-weight is not 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. And the hosted Kimi API is operated from Beijing, which carries its own compliance considerations — self-hosting the weights avoids that entirely, using the hosted endpoint does not.

Winner by Category

Pros and Cons

GPT-5.6 Terra

Pros

• Scores 55 on the independent Artificial Analysis Intelligence Index — eleven points clear of Kimi K2.6 on the same version of the same index, measured by an evaluator with no stake in the result.
• Carries an independently charted coding score, which Kimi K2.6 does not have at all.
• A 1,050,000-token context window, roughly four times Kimi K2.6's.
• The smallest price premium of any independently scored closed model over Kimi K2.6: roughly 2.6 times on input, roughly 3.75 times on output.
• Managed API with no infrastructure to run, and a clear ladder to Sol above it or Luna below it if your needs shift.

Cons

• More expensive on every single line, and output at USD 15 per million tokens hurts on agentic workloads that generate long traces.
• Loses the measured-intelligence-per-dollar argument by roughly a factor of three, and both sides of that ratio are independently sourced.
• Closed weights. No self-hosting, no data residency through hardware control, no protection against deprecation.
• Undisclosed architecture, and no published agent orchestration ceiling to compare against Kimi's.

Kimi K2.6

Pros

• Independently measured, which is the thing most open-weight releases cannot claim: 44 on the Artificial Analysis Intelligence Index, on the same version that scores Terra.
• Cheaper on every line: USD 0.95 per million input tokens, USD 0.16 cached, USD 4 per million output tokens.
• Roughly three times more measured intelligence per dollar of output than Terra.
• Open weights under a Modified MIT license, downloadable and self-hostable on your own hardware.
• Full architecture disclosure — mixture-of-experts, roughly 1 trillion total parameters with 32 billion active, 384 experts — plus a native MoonViT vision encoder of around 400 million parameters.
• A published agent orchestration ceiling: up to 300 subagents across as many as 4,000 steps.

Cons

• Eleven index points behind Terra on the same independent scale, and on long unsupervised chains that gap shows up as tasks that do not finish.
• Its only published coding figure is self-reported by Moonshot AI on its own harness, and no third party has reproduced it.
• A 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 1.6 times on cached input.
• Widely misreported with an inflated index score taken from an older version of the benchmark, which makes it hard to evaluate from coverage alone.
• The hosted API is operated from Beijing, which carries compliance considerations that self-hosting avoids and the hosted endpoint does not.

When to Pick GPT-5.6 Terra

• Your work is long-horizon and unsupervised. Multi-hour agentic runs, multi-file refactors, chains where a mistake at step 3 poisons step 40. Eleven index points is exactly where completion rates live, and a retry costs more than the tokens you saved.
• Your volume is moderate. At tens of millions of output tokens a month the price gap is in the hundreds of dollars. The stronger model plus four times the context for that is a bargain.
• You routinely exceed 256,000 tokens of context. Whole-monorepo work, very long sessions, large document corpora. Kimi's swarm is a workaround, not a bigger window.
• Your workload is cache-heavy. If most of your tokens are repeated prefixes hitting the cache, you are comparing USD 0.25 against USD 0.16, and the price argument for Kimi has largely evaporated.
• You need the strongest coding evidence you can cite. Terra is the only one of the two with an independently charted coding score.

When to Pick Kimi K2.6

• You need to self-host. Data residency, air-gapped environments, regulated industries. Terra simply cannot do this, so the comparison ends there.
• Budget is your binding constraint, not capability. If a 44 on the index does the job you actually have — and for a great deal of real work it does — then paying roughly three times more for a 55 is buying headroom you will not use.
• 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 are fanning work across many agents. A published ceiling of up to 300 subagents across as many as 4,000 steps is a real architectural commitment, and OpenAI has published nothing comparable for Terra.
• You want protection from deprecation, or you need to inspect and modify the model. A downloaded weight file is yours. An API endpoint is not.

Final Verdict

GPT-5.6 Terra wins this comparison, and it is the closest thing to a genuine contest we have run all year.

Kimi K2.6 takes more rows in our category table than Terra does, and we still call it for Terra, so we owe you the reasoning rather than a shrug.

Several of Kimi's row wins — input, cached input, output, intelligence per dollar — are the same axis counted four ways. It is one advantage, cost, and it is a large one. Two more, open weights and self-hosting, are also closely related: they are the control axis. Kimi K2.6's case, honestly stated, is that it is much cheaper, you can own it, and it has the receipts to prove it is good. That third clause is new, and it is what makes this page different from every other closed-versus-open comparison on this site. Kimi K2.6 is not asking for the benefit of the doubt. It went and got measured.

Terra's win rests on a single fact that the price cannot argue with: on the one axis where both models were measured by the same evaluator under the same conditions, Terra is eleven points ahead — 55 against 44 — and on this scale, where the very top is 60, eleven points is not a rounding error. It is the difference between a model in the leading group and a model at the head of the tier below. On short tasks you will not feel it. On the long, unsupervised, high-stakes work that people actually buy frontier models for, you feel it as tasks that finish versus tasks that need you.

And the premium for it is unusually small. GPT-5.6 Terra is the closest an independently scored closed model comes to Kimi K2.6 on price: roughly 2.6 times on input, roughly 3.75 times on output, and cached input close to level. Elsewhere in the market the same open model faces flagships charging eight to twelve times more. At moderate volume that premium is a few hundred dollars a month, and it buys you eleven measured points and four times the context.

So the rule we would give you. If budget is your binding constraint, or you must self-host, or your volume runs to hundreds of millions of output tokens a month, pick Kimi K2.6 — it is a genuinely good model with a genuinely good score and it costs a third as much, and nobody should apologize for that trade. Otherwise pick GPT-5.6 Terra, because eleven independently measured points at this price is the best exchange rate on the board.

Where our verdict is wrong: if your workload lives entirely in the range where a 44 is sufficient — and a great deal of production work does — then we have just talked you into paying three times more for capability you will never call on. That is a real risk, and the only honest way to settle it is to run both models on your tasks. The index is a proxy. Your workload is the benchmark.

To see how each model fares against the rest of the field, we have Kimi K2.6 against Claude Sonnet 5, and its successor Kimi K2.7 against Claude Opus 4.8 — worth reading, because K2.7 has no independent score at all, which makes K2.6 the better-evidenced of the two Moonshot models today. 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 K2.6 cheaper?

Kimi K2.6, 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 closer than anywhere else: USD 0.16 against USD 0.25, a gap of about 1.6 times. That makes GPT-5.6 Terra the closest an independently scored closed model comes to Kimi K2.6 on price — elsewhere in the market the same open model faces flagships charging eight to twelve times more.

What does Kimi K2.6 score on the Artificial Analysis Intelligence Index?

It scores 44 on version 4.1 of the index, which is the current version and the same one that scores GPT-5.6 Terra at 55. This is an independent measurement, not a vendor claim. You will often see 54 quoted instead — that figure is wrong: 44, not 54. The 54 comes from a previous version of the index, and scores from an older version are not comparable to current-version scores. Any comparison quoting 54 for Kimi K2.6 against a current score for a closed model is measuring with two different rulers.

Is an eleven-point gap on the index actually significant?

Yes, on this scale. The highest score any model has posted on this index to date is 60, so the whole leading group is compressed into a narrow band near the top. Terra at 55 sits inside that group; Kimi K2.6 at 44 sits at the head of the tier below. In practice the gap is invisible on short single-shot tasks and becomes very visible on long, unsupervised chains — multi-file refactors, hour-long agentic loops — where the stronger model needs fewer interventions and fails less often. On agentic work a failure is not free: it gets retried, and the retry spends the tokens you saved.

Why is there no coding row in your comparison table?

Because only one of the two models has an independently charted coding score. GPT-5.6 Terra is on the Artificial Analysis Coding Index, an independent evaluation. Kimi K2.6 is not on that index, and no third party has published a coding result for it — the only coding figure Moonshot AI offers is one it produced on its own harness. Those are different benchmarks and different evidentiary regimes, two separate reasons either of which alone would be enough. Putting them in one row would manufacture a like-for-like measurement that does not exist, so you will not find them side by side anywhere on this page.

Which model won your overall verdict, and why?

GPT-5.6 Terra, and it is the narrowest call we have made in this family. On the one axis where both models were measured by the same independent evaluator on the same version of the same index, Terra leads 55 to 44. Eleven points is not a rounding error on a scale whose top is 60. It also carries roughly four times the context. The premium for that is unusually small — about 2.6 times on input and 3.75 times on output, where the frontier tier charges eight to twelve times more than Kimi. Kimi K2.6 still wins price on every line, measured intelligence per dollar, open weights, self-hosting, and agent orchestration outright.

Which model gives more intelligence per dollar?

Kimi K2.6, by roughly a factor of three, and this is its strongest argument. Divide each model's independently measured index score by its output price: Terra returns about 4 index points per dollar, Kimi K2.6 returns about 11. Both sides of that ratio come from a third party, so it is not a marketing claim. Terra wins on the capability ceiling; Kimi wins on the exchange rate. Which of those governs your choice depends on whether your workload is bounded by capability or by budget.

Can I self-host either of these models?

Kimi K2.6, yes. Its weights are published under a Modified MIT license 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 K2.6's favor.

What is the context window on each model?

GPT-5.6 Terra offers a 1,050,000-token context window. Kimi K2.6 offers 256,000 tokens, or 262,144 exactly. 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. Note that Kimi's swarm mode, which distributes a task across many subagents, is partly a way of working around a smaller window rather than a way of enlarging it.

What is Kimi K2.6'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 ships a native vision encoder called MoonViT of around 400 million parameters. This level of architectural detail is public for Kimi K2.6 and has no equivalent for GPT-5.6 Terra, whose architecture OpenAI does not disclose — one of the few areas where Moonshot AI is clearly the more transparent of the two companies.

Is Kimi K2.6 open source?

It is open-weight, which is not quite the same thing. Moonshot AI publishes the model weights under a Modified MIT license — 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.

How is Kimi K2.6 different from Kimi K2.7?

They are different models and their evidence is very different. Kimi K2.6 was released on April 20, 2026 and has been independently measured: 44 on the Artificial Analysis Intelligence Index. Kimi K2.7 came later and, as things stand, has no independent score of any kind — nothing on the Artificial Analysis indexes, nothing on LMArena. Do not carry a number from one to the other in either direction. If you want an open-weight Moonshot model with third-party evidence behind it today, K2.6 is the one that has it.

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 K2.6 that the premium for eleven additional independently measured points stops being an argument.