GPT-5.6 Sol vs GPT-5.6 Terra: Flagship or Balanced Tier? (2026)

GPT-5.6 Sol and GPT-5.6 Terra are the two upper capability tiers of OpenAI's GPT-5.6 generation, released together on July 9, 2026. They share the same 1,050,000-token context window, the same February 16, 2026 knowledge cutoff, the same 128,000-token output ceiling, and the same Programmatic Tool Calling toolbox — so the decision is purely capability against price. GPT-5.6 Sol is the flagship: it scores 59 on the independent Artificial Analysis Intelligence Index and is No.1 at 80 on the Coding Agent Index, and it alone gets the new ultra multi-agent reasoning mode, priced at $5 per million input tokens and $30 per million output tokens. GPT-5.6 Terra is the balanced tier: it scores 55 on the Intelligence Index and 77 on the Coding Agent Index, and it costs exactly half — $2.50 input and $15 output per million tokens. Best for the hardest problems and long-horizon agents: Sol. Best for high-volume business work at a tight budget: Terra. There is no single overall winner — you pick the tier, not the model.

Quick Verdict

This is a split verdict inside one model family: GPT-5.6 Sol owns the capability ceiling, GPT-5.6 Terra owns the price — and because they share everything else, the choice is unusually clean. Both went generally available on July 9, 2026, and we have API access to both. We ran them side-by-side through our own OpenAI API key, so we scope our hands-on claims to the first few days and lean on attributed third-party benchmarks — Artificial Analysis above all — wherever our own time is too short. Every figure below carries its source, and OpenAI's self-reported numbers are labeled as such. Here is the short version, per OpenAI's own positioning.

• Best for peak measured intelligence: GPT-5.6 Sol. Artificial Analysis scores it 59 on the Intelligence Index v4.1 against Terra's 55 — a four-point gap that widens on the hardest reasoning.
• Best on the agentic coding index: GPT-5.6 Sol. It ranks No.1 at 80 on the AA Coding Agent Index; Terra scores 77. A three-point difference on the same benchmark, in Sol's favor.
• Best for multi-agent and hardest-tier work: GPT-5.6 Sol. Its new ultra reasoning mode runs up to sixteen parallel agents; Terra's reasoning ceiling stops at max, one step below.
• Best for price: GPT-5.6 Terra, decisively. At $2.50 input and $15 output per million tokens it is exactly half of Sol on both sides. Both rate cards are vendor-verified.
• Best for cost per task: GPT-5.6 Terra, by roughly two to one. Artificial Analysis measures about $0.55 per task for Terra against about $1.04 for Sol on the same evaluation.
• Best value for high-volume business work: GPT-5.6 Terra. OpenAI positions it as GPT-5.5-competitive at two times lower cost, and it trails Sol only by single-digit index margins.
• Best consumer access: GPT-5.6 Sol. It is selectable in the ChatGPT app on paid plans; Terra is available through the API, Codex, and ChatGPT for Business and Enterprise only.
• Tied on everything structural: context window, output ceiling, knowledge cutoff, modalities, tokenizer, Programmatic Tool Calling, and fine-tuning support are identical across both tiers.

The honest caveats up front: both tiers are only days old at the time of writing, so we treat our hands-on notes as first impressions, not a settled verdict. Neither Sol nor Terra has been submitted to the independent SWE-bench Verified leaderboard, so there is no verified GitHub-issue coding score for either — a data gap we flag rather than paper over. And OpenAI's Terminal-Bench 2.1 figures for both models are self-reported; we label them so and keep them apart from the independent Artificial Analysis scores.

GPT-5.6 Sol vs GPT-5.6 Terra — Overview

What Is GPT-5.6 Sol?

GPT-5.6 Sol is the flagship capability tier of OpenAI's GPT-5.6 generation, generally available July 9, 2026 after a gated preview on June 26. In OpenAI's naming scheme the number is the generation and the names — Sol, Terra, and Luna — are durable capability tiers rather than sizes; Sol is the tier aimed at the hardest problems, from complex coding and long-horizon agents to cyber, science, and computer use, per OpenAI's announcement. Per OpenAI's model documentation, Sol runs a 1,050,000-token context window with up to 128,000 output tokens and a February 16, 2026 knowledge cutoff, handles text and image inputs to text output, and introduces two new reasoning levels above xhigh: max, and ultra, a multi-agent mode that runs up to sixteen reasoning agents in parallel. It also carries Programmatic Tool Calling, where the model writes and executes JavaScript in an isolated, ephemeral runtime to orchestrate its own tools. API pricing is $5 per million input tokens and $30 per million output tokens, with cached input at $0.50 per million. On the independent leaderboards it is the stronger of the two tiers: 59 on the Artificial Analysis Intelligence Index and No.1 at 80 on the Coding Agent Index.

What Is GPT-5.6 Terra?

GPT-5.6 Terra is the balanced capability tier of the same generation, released alongside Sol on July 9, 2026, and built for high-volume business work — customer support, document processing, and everyday automation. OpenAI positions it as GPT-5.5-competitive at two times lower cost, a claim we treat as vendor positioning and test against the independent numbers below. Per OpenAI's model documentation, Terra shares Sol's exact envelope: a 1,050,000-token context window, up to 128,000 output tokens, a February 16, 2026 cutoff, text-and-image input to text output, and the same Programmatic Tool Calling and agentic toolbox. Where it differs is the reasoning ceiling — Terra's effort scale runs from low to max and stops there, without Sol's ultra multi-agent mode — and the price. Per OpenAI's API pricing documentation, Terra costs $2.50 per million input tokens and $15 per million output tokens, exactly half of Sol, with cached input at $0.25 per million. On the independent Artificial Analysis indices it scores 55 on intelligence and 77 on the Coding Agent Index — a few points behind Sol on each, for half the money.

How We Compared Them — and What We Did Not Do

Method transparency matters here, because both tiers are only days old and the temptation is to over-read a handful of prompts. Here is exactly what we did and did not do, and where our numbers come from.

• Pricing: both rate cards are vendor-verified. We confirmed Sol's $5 input and $30 output per million tokens, and Terra's $2.50 input and $15 output, directly against OpenAI's API pricing documentation and cross-checked the specs on OpenAI's model docs. No relayed figures.
• Independent benchmarks: we lean on Artificial Analysis for the Intelligence Index, the Coding Agent Index, and cost per task, because it measures both tiers on the same harness. Where a model has not been measured — as neither Sol nor Terra has on SWE-bench Verified — we say so and do not substitute a self-reported number.
• Self-reported figures: OpenAI's Terminal-Bench 2.1 numbers for Sol (88.8 percent, 91.9 percent in ultra) and Terra (87.4 percent) are labeled as vendor-reported throughout and are not treated as head-to-head evidence against the independent indices.
• Hands-on: we ran both models side-by-side through our own OpenAI API key on the same business and coding tasks. Both returned successfully, and we report what we saw — but a few days of use is first impressions, not a controlled benchmark, and we scope every observation accordingly.
• Disclosure: we have no affiliate relationship with OpenAI. There are no sponsored links on this page. This is an internal comparison of two tiers of the same model, so there is no vendor axe to grind either way — only the question of which tier fits which job.

Features and Benchmarks Comparison

The table below lists every dimension we could verify or attribute. Read the Winner column carefully: it distinguishes vendor-verified pricing, independent benchmarks, self-reported figures, and genuine ties — and there are many ties here, because the two tiers share the same envelope. Sources for the independent scores are Artificial Analysis, and the specifications come from OpenAI's model documentation.

Synthesis: read top to bottom, the table tells a clear story. Every structural row is a tie — same context, same output ceiling, same cutoff, same modalities, same tooling, same tokenizer — because Sol and Terra are tiers of one generation, not rival architectures. The capability rows tilt to Sol by narrow, consistent margins: 59 to 55 on intelligence, 80 to 77 on the coding index, plus the ultra mode Terra lacks. The price rows tilt to Terra by a clean, uniform factor of two on every line: input, output, cached, batch, and cost per task. One nuance worth stating plainly: on LMArena's human-preference Elo, Sol's Xhigh configuration is charted at 1486 (No.8), while Terra has not been charted yet, so we do not treat human preference as a head-to-head row — only Sol has a number there. That leaves the decision exactly where the design intends it: capability versus cost.

Pricing — GPT-5.6 Sol vs GPT-5.6 Terra in 2026

Pricing is the cleanest part of this comparison, because the relationship is exact: Terra is half of Sol on every line. There is no context-length surcharge on either tier, and both use the same tokenizer, so a given prompt produces the same token count on both — the bill simply halves on Terra. For the mechanics of input, output, and cached-token billing, our AI model pricing explainer breaks down how these rate cards translate into real invoices. Both tables below come straight from OpenAI's API pricing documentation, cross-checked against the GPT-5.6 announcement.

GPT-5.6 Sol Pricing

GPT-5.6 Terra Pricing

Pricing verdict: Terra wins on price, exactly and everywhere. On a representative agentic call of 50,000 input tokens and 5,000 output tokens, Sol costs about $0.40 at the rate card ($5 times 0.05 input plus $30 times 0.005 output), while Terra costs about $0.20 ($2.50 times 0.05 plus $15 times 0.005) — half, on the nose. That ratio holds on Batch mode, on cached input, and on the Priority tier, because Terra's entire rate card is Sol's divided by two. The one place the sticker gap and the real gap diverge is cost per task, and it diverges in Terra's favor too: Artificial Analysis measures about $0.55 per task for Terra against about $1.04 for Sol on its Intelligence Index run, close to the two-to-one rate-card ratio because both tiers share a tokenizer and Terra simply emits somewhat fewer reasoning tokens at its lower ceiling. If budget is the binding constraint, Terra is not a compromise — it is half the invoice for a model that trails only by single-digit index points.

Hands-On Notes — Both Tiers Through Our Own API Key

We owe you precision about what this section is and is not. We ran GPT-5.6 Sol and GPT-5.6 Terra side-by-side through our own OpenAI API key on the same prompts within hours of their July 9 general availability, which gives us a few days of direct comparison at the time of writing — sharp first impressions, nowhere near a controlled benchmark. Take these observations as scoped and provisional, and weight the attributed Artificial Analysis numbers and OpenAI's own model documentation above them.

Where Sol stood out: the hardest single problems. On a deliberately tricky algorithm task, Sol wrote a correct implementation on the first try and reasoned cleanly through a multi-step logic puzzle; on a source-comprehension prompt it correctly refused to invent a fact the text withheld rather than guessing. Turned up to its higher reasoning levels, and especially in the ultra multi-agent mode, it produced visibly more thorough plans on a hard architecture task than Terra did — at a higher token bill for that call. This lines up with its 59 Intelligence Index and No.1 Coding Agent Index placement without proving either in a few days.

Where Terra held its ground: everyday business work at half the cost. On an identical cost-analysis task, Terra matched Sol's answer at roughly half the token cost and lower latency — the two-times-cheaper thesis held in our runs. It followed instructions with discipline: it obeyed "output only JSON" and "table only," respected word budgets, and did not pad across four business tasks, with responses landing in the low single-digit seconds. On routine transforms and structured extraction, the output gap between the two tiers was small to invisible, which is precisely where Terra's price advantage makes it the rational default.

What the split looked like in practice: the honest pattern was that Terra was good enough for most of what we threw at it, and Sol pulled ahead specifically on the hardest, longest, or most ambiguous tasks — the ones where an extra few index points and a parallel-agent reasoning mode actually change the result. That is exactly the tier design working as intended, and it is why we did not crown one winner.

What we cannot tell you yet: latency under controlled load, per-task economics across a real production mix, and whether either tier's early behavior holds up over weeks. We will update this comparison as our side-by-side time accumulates and as more independent harnesses publish results.

Winner per Category

Best for Peak Measured Intelligence: GPT-5.6 Sol

On the Artificial Analysis Intelligence Index v4.1, GPT-5.6 Sol scores 59 against GPT-5.6 Terra's 55 — a four-point gap on the same independent harness. On aggregate that is modest, but it is not evenly distributed: index gaps of this kind tend to concentrate on the hardest reasoning tasks, where a few points of headroom can be the difference between a correct multi-step answer and a plausible wrong one. If your workload leans on the toughest reasoning you have, Sol is the pick on the independent evidence. For everyday prompts, the four points rarely surface.

Best on the Agentic Coding Index: GPT-5.6 Sol

The AA Coding Agent Index is the sharpest capability separator here: Sol ranks No.1 at 80, while Terra scores 77. Three points, same benchmark, Sol ahead. Neither tier has an independent SWE-bench Verified score — OpenAI has not submitted either — so the Coding Agent Index is the best like-for-like coding signal available, and it favors Sol. Our explainer on agentic coding models covers why an agent index measures something different from a single-shot coding score. For the hardest agentic coding and long-horizon refactors, Sol's lead plus its ultra mode make it the pick; for routine coding assistance, Terra's 77 is close enough that price wins.

Best for Multi-Agent and Hardest-Tier Work: GPT-5.6 Sol

Sol owns the reasoning ceiling outright. Per OpenAI's documentation, the effort scale runs low through xhigh, then adds max — and Sol goes one step further to ultra, a multi-agent mode that runs up to sixteen reasoning agents in parallel (four by default). Terra stops at max and does not offer ultra. OpenAI reports Sol reaching 91.9 percent on Terminal-Bench 2.1 in ultra versus 88.8 percent standard, though that is self-reported. For long-horizon, hardest-tier problems that benefit from parallel reasoning, ultra is a concrete, Terra-unavailable reason to choose Sol.

Best for Price and Cost per Task: GPT-5.6 Terra

This one is not close, and it is exact. Terra costs $2.50 per million input tokens against Sol's $5, and $15 per million output against $30 — half on both sides, vendor-verified on OpenAI's pricing documentation. Batch mode halves each again. And on the one third-party cost-per-task measurement, Artificial Analysis puts Terra at about $0.55 to run its Intelligence Index against about $1.04 for Sol. Unless your tasks demonstrably need Sol's capability lead, Terra delivers roughly twice the output per dollar — the single largest advantage in this matchup.

Best Value for High-Volume Business Work: GPT-5.6 Terra

Value is capability per dollar, and on that axis Terra wins for the bulk of real workloads. It trails Sol by four points on intelligence and three on the coding index while costing half as much — and OpenAI positions it as GPT-5.5-competitive at two times lower cost, aimed squarely at customer support, document processing, and everyday automation. For high-volume pipelines where the marginal capability gap rarely changes the output but the halved bill compounds on every call, Terra is the rational default. Start workloads on Terra and promote to Sol only the tasks that measurably need the ceiling.

Best Consumer Access: GPT-5.6 Sol

One narrow but real edge for Sol sits outside the benchmarks: reach. Per OpenAI's rollout, GPT-5.6 Sol is selectable inside the ChatGPT app on the Plus, Pro, Business, and Enterprise plans, while Terra is available through the API, Codex, and ChatGPT for Business and Enterprise only — it is not a pickable model in the consumer ChatGPT experience. For developer teams consuming models by API, this is irrelevant; both are one model ID away. For non-developer users who want to select the model by name inside ChatGPT, Sol is the one they can reach, and Terra is not.

Pros and Cons

GPT-5.6 Sol Pros and Cons

What we like about GPT-5.6 Sol

• Highest measured capability of the two. 59 on the Artificial Analysis Intelligence Index against Terra's 55, and No.1 at 80 on the Coding Agent Index against Terra's 77.
• Exclusive ultra multi-agent reasoning mode. Up to sixteen parallel reasoning agents for the hardest long-horizon problems — a ceiling Terra does not reach.
• Disciplined hands-on behavior. In our runs it wrote a correct hard algorithm on the first try and refused to hallucinate a withheld fact rather than guessing.
• Selectable in the ChatGPT app. Reachable by name on paid consumer plans, which Terra is not.
• Same envelope as Terra with more headroom. Identical 1,050,000-token context, 128,000-token output, and toolbox, plus the extra reasoning tiers.

Where GPT-5.6 Sol falls short

• Twice the price of Terra on every line. $5 input and $30 output per million tokens against $2.50 and $15, and about $1.04 per task against $0.55.
• Capability lead is narrow on aggregate. Four intelligence points and three coding-index points rarely change the outcome on everyday work.
• Absent from independent SWE-bench Verified. Not submitted, so it has no independent verified-coding number — the same gap as Terra.
• Headline coding figures are self-reported. Terminal-Bench 2.1 and its ultra-mode number come from OpenAI, not an independent harness.
• Days old at the time of writing. Its production behavior over weeks is unproven, so our hands-on notes are first impressions.

GPT-5.6 Terra Pros and Cons

What we like about GPT-5.6 Terra

• Exactly half the price of Sol. $2.50 input and $15 output per million tokens, with cached input at $0.25 and Batch mode at $1.25 and $7.50.
• Roughly half the cost per task. Artificial Analysis measures about $0.55 against Sol's $1.04 on the same evaluation.
• Most of the capability for the money. 55 on the Intelligence Index and 77 on the Coding Agent Index — single-digit margins behind Sol.
• Identical envelope and full toolbox. Same 1,050,000-token context, 128,000-token output, Programmatic Tool Calling, function calling, structured outputs, web and file search, code interpreter, computer use, and MCP.
• Disciplined instruction-following in our runs. Obeyed "output only JSON" and "table only," respected word budgets, and answered in the low single-digit seconds.

Where GPT-5.6 Terra falls short

• Behind Sol on every capability index. 55 to 59 on intelligence and 77 to 80 on the coding index — small but consistent.
• No ultra multi-agent mode. Its reasoning ceiling stops at max, so the hardest parallel-reasoning workloads belong to Sol.
• Not selectable in the consumer ChatGPT app. Available through the API, Codex, and ChatGPT for Business and Enterprise only.
• Absent from independent SWE-bench Verified. Not submitted, leaving an agentic-coding data gap at launch — the Coding Agent Index of 77 is the closest public reference.
• Days old at the time of writing. Like Sol, its long-run production behavior is not yet proven.

When to Pick GPT-5.6 Sol vs GPT-5.6 Terra

Pick GPT-5.6 Sol if...

• Your workload is the hardest coding, long-horizon agents, science, or computer use, where the four-point intelligence gap and No.1 coding index actually change outcomes.
• You need the ultra multi-agent reasoning mode (up to sixteen parallel agents) that Terra does not offer.
• You want the peak of OpenAI's GPT-5.6 capability curve and the price difference is not the binding constraint.
• Non-developer users on your team need to select the model by name inside the ChatGPT app.
• You are promoting a specific task from Terra because you measured that it needs the extra headroom.

Pick GPT-5.6 Terra if...

• Price-performance is the deciding factor — half the input and output rates, and about half the cost per task on Artificial Analysis's runs.
• Your workload is high-volume business work — customer support, document processing, everyday automation — where a single-digit index gap is marginal.
• You want a GPT-5.5-competitive model, as OpenAI positions Terra, at two times lower cost.
• You consume models through the API or Codex, where Terra's lack of consumer-app selection is irrelevant.
• You want a sensible default tier and plan to promote only the tasks that measurably need Sol.

Frequently Asked Questions

Is GPT-5.6 Sol better than GPT-5.6 Terra in 2026?

On raw capability, yes, but not by a landslide, and it costs twice as much. On the independent Artificial Analysis Intelligence Index, Sol scores 59 to Terra's 55, and on the Coding Agent Index it is No.1 at 80 against Terra's 77. Sol also gets the new ultra multi-agent reasoning mode that Terra does not. But Terra costs exactly half — $2.50 input and $15 output per million tokens versus $5 and $30 — and runs about $0.55 per task against Sol's $1.04 on Artificial Analysis's own measurements. For the hardest problems, Sol is the better model; for high-volume production where a four-point index gap is marginal, Terra delivers most of the capability at half the price. There is no single winner here — it depends on your workload.

How much do GPT-5.6 Sol and GPT-5.6 Terra cost?

GPT-5.6 Sol costs $5 per million input tokens and $30 per million output tokens, with cached input at $0.50 per million; GPT-5.6 Terra is exactly half at $2.50 input and $15 output, with cached input at $0.25 per million. We confirmed both rate cards directly on OpenAI's API pricing documentation. Batch mode halves each again — Sol to $2.50 input and $15 output, Terra to $1.25 input and $7.50 output. Neither has a context-length surcharge; pricing is flat across the full 1,050,000-token window. The headline is simple: for any given prompt, Terra bills half of what Sol bills, on both input and output.

What is the difference between GPT-5.6 Sol and GPT-5.6 Terra?

They are two capability tiers of the same GPT-5.6 generation, not two different models. OpenAI's naming scheme uses the number for the generation and the names — Sol, Terra, Luna — as durable capability tiers. Sol is the flagship aimed at the hardest problems: complex coding, long-horizon agents, science, and computer use. Terra is the balanced tier aimed at high-volume business work, which OpenAI positions as GPT-5.5-competitive at two times lower cost. They share the same 1,050,000-token context, the same February 16, 2026 cutoff, the same 128,000-token output ceiling, and the same tool suite. The differences are capability (Sol leads the independent indices), reasoning ceiling (Sol adds ultra multi-agent mode), consumer access (Sol is selectable in ChatGPT, Terra is not), and price (Terra is half).

Do GPT-5.6 Sol and Terra share the same context window and knowledge cutoff?

Yes, exactly. Per OpenAI's model documentation, both GPT-5.6 Sol and GPT-5.6 Terra run a 1,050,000-token context window, a 128,000-token maximum output, and a February 16, 2026 knowledge cutoff. Both accept text and image input and return text output, both support Programmatic Tool Calling in an isolated runtime, and neither supports fine-tuning at launch. This is deliberate: within the GPT-5.6 generation, the context, cutoff, and modalities are constant across tiers, and only the underlying capability, the reasoning ceiling, and the price change. So if your decision hinges on context size or freshness, the two are identical and you should choose on capability and cost instead.

Which is better for coding: GPT-5.6 Sol or GPT-5.6 Terra?

Sol, on the independent measurement available, but the gap is narrow. On the Artificial Analysis Coding Agent Index, Sol is No.1 at 80 while Terra scores 77 — a three-point difference on the same benchmark. Neither model has been submitted to the independent SWE-bench Verified leaderboard, so there is no verified GitHub-issue score for either, which is a data gap we flag rather than fill. OpenAI's own materials report Sol at 88.8 percent on Terminal-Bench 2.1 and Terra at 87.4 percent, but those are self-reported. For the hardest agentic coding and long-horizon refactors, Sol's lead plus its ultra multi-agent mode make it the pick; for routine, high-volume coding assistance where three index points rarely change the outcome, Terra does the job at half the cost.

Is GPT-5.6 Terra good enough to replace the flagship Sol?

For most high-volume business work, yes — that is exactly the tier OpenAI built it for. Terra scores 55 on the Artificial Analysis Intelligence Index against Sol's 59, and 77 on the Coding Agent Index against Sol's 80: it trails, but by single-digit margins, while costing half as much and running about $0.55 per task against Sol's $1.04. OpenAI positions Terra as GPT-5.5-competitive at two times lower cost, and in our own testing it matched Sol's answer on an identical cost-analysis task at roughly half the token cost. Where Terra cannot replace Sol is the genuinely hard end: the four-point intelligence gap, the No.1 coding index, and the ultra multi-agent mode are Sol-only. Route the ceiling work to Sol, the volume work to Terra.

What is the ultra reasoning mode, and does GPT-5.6 Terra have it?

Ultra is a new multi-agent reasoning setting in the GPT-5.6 generation, and it is a Sol feature — Terra does not offer it. Per OpenAI's documentation, the reasoning-effort scale runs from low through xhigh, then adds a new max level; on Sol it goes one step further to ultra, which spawns multiple reasoning agents in parallel — four by default, up to sixteen — to attack a single hard problem. Terra's reasoning ceiling stops at max. OpenAI reports Sol reaching 91.9 percent on Terminal-Bench 2.1 in ultra mode versus 88.8 percent standard, though that figure is self-reported. If your workloads include long-horizon, hardest-tier problems that benefit from parallel reasoning, ultra is a concrete reason to choose Sol over Terra; for everyday work it is not.

Which model is cheaper per task: GPT-5.6 Sol or GPT-5.6 Terra?

Terra, by roughly half, on the one independent measurement available. Artificial Analysis publishes the cost to run its Intelligence Index evaluation, and it lists about $0.55 per task for GPT-5.6 Terra versus about $1.04 for GPT-5.6 Sol. That tracks the rate cards almost exactly, because Terra's per-token prices are precisely half of Sol's on both input and output, and the two tiers use the same tokenizer, so there is no hidden token-count penalty on either side. In our own runs on an identical business task, Terra produced a comparable answer at roughly half the token cost and lower latency. For cost-sensitive, high-throughput pipelines, Terra is the clear per-task value; Sol's premium only pays off where its capability lead matters.

Can I use GPT-5.6 Terra in the ChatGPT app?

Not directly in the consumer app. Per OpenAI's rollout, GPT-5.6 Sol is selectable inside ChatGPT on the Plus, Pro, Business, and Enterprise plans, while Terra and Luna are available through the API, Codex, and ChatGPT for Business and Enterprise only — they are not offered as pickable models in the consumer ChatGPT experience. So if your team lives inside the ChatGPT app and wants to select the model by name, Sol is the one you can reach; if you consume models through the API or Codex, both Sol and Terra are one model-ID away. This access difference is a genuine, if narrow, point in Sol's favor for non-developer users.

Does GPT-5.6 Terra support Programmatic Tool Calling?

Yes. Programmatic Tool Calling ships across the GPT-5.6 generation, so GPT-5.6 Terra has it just as GPT-5.6 Sol does. Per OpenAI's documentation, the model writes and executes JavaScript inside an isolated, ephemeral runtime to orchestrate its own tool use — batching, looping, and combining tool calls in code rather than emitting them one at a time — and it is compatible with zero-data-retention deployments. Terra also carries the full agentic toolbox: function calling, structured outputs, web and file search, code interpreter, computer use, and MCP. This is one of the clearest ways to see the tier design: the tooling is constant across Sol and Terra, and only the underlying reasoning capability and the price change between them.

Should I run GPT-5.6 Sol and Terra together in the same stack?

Yes, and for many teams a split stack is the rational setup. Because Sol and Terra share the same context window, cutoff, output ceiling, tokenizer, and tool suite, a prompt written for one runs on the other with no changes — only the model ID and the bill differ. A practical routing pattern sends the hardest coding, long-horizon agents, and anything that benefits from the ultra multi-agent mode to Sol, and sends high-volume, cost-sensitive business work to Terra at half the price. Abstraction layers such as the Vercel AI SDK, LangChain, or LiteLLM turn that routing into a configuration choice rather than a rewrite. Since the two tiers are drop-in compatible, you can even start every workload on Terra and promote only the tasks that measurably need Sol.

What are the alternatives to GPT-5.6 Sol and GPT-5.6 Terra?

Several sit close by. Below Terra, GPT-5.6 Luna is the cheapest tier in the same family at $1 input and $6 output per million tokens, for summarization and routine automation. OpenAI's prior flagship GPT-5.5 remains active and is a fair reference point for Terra, which OpenAI positions as GPT-5.5-competitive. Outside OpenAI, Claude Opus 4.8 at $5 input and $25 output per million tokens is a flagship rival to Sol with an independently verified SWE-bench Verified score, Claude Sonnet 5 is the balanced-tier rival to Terra, and Google's Gemini 3.1 Pro is the value play for high-volume retrieval. Our GPT-5.5 review and our Claude Opus 4.8 review cover the adjacent trade-offs, and the pricing mechanics are broken down in our AI model pricing explainer.

Final Verdict — Ceiling vs Bill, a True Split

After running both tiers side-by-side through our own OpenAI API key, verifying pricing on OpenAI's own documentation, and holding every capability claim to independent benchmarks, our verdict is a genuine split — and an unusually clean one, because Sol and Terra share everything except capability and price. GPT-5.6 Sol is the capability leader: 59 to 55 on the Artificial Analysis Intelligence Index, No.1 at 80 on the Coding Agent Index against Terra's 77, and the only one of the two with the ultra multi-agent reasoning mode. GPT-5.6 Terra is the value leader: exactly half the price on every line, about $0.55 per task against Sol's $1.04, and single-digit index margins behind for a model OpenAI positions as GPT-5.5-competitive. Both run the same 1,050,000-token context, the same February 16, 2026 cutoff, the same 128,000-token output, and the same Programmatic Tool Calling toolbox.

We did not crown a single overall winner because the evidence does not support one honestly: Sol's capability lead is real but narrow on aggregate, and it comes at double the price; Terra's value is real but it cannot buy the top of the measured intelligence and coding curves or the ultra mode. If your work is the hardest reasoning, longest-horizon agents, or most ambiguous coding — pick GPT-5.6 Sol and pay for the ceiling. If your work is high-volume business processing at a tight budget — pick GPT-5.6 Terra and bank the difference. Because the two tiers are drop-in compatible, the pragmatic endgame for many teams is to default to Terra and route only the tasks that measurably need the extra headroom to Sol. For the tiers and rivals around this matchup, see our GPT-5.5 review, our Claude Opus 4.8 review, our Claude Sonnet 5 review, and our Claude Opus 4.8 vs GPT-5.5 comparison.

Sources

Every figure in this comparison is attributed to a primary or independent source. Pricing and specifications come from OpenAI's own documentation; capability scores come from the independent Artificial Analysis; self-reported figures are labeled as such throughout.

• OpenAI — GPT-5.6 announcement, tiers, and positioning
• OpenAI — GPT-5.6 Sol and Terra model documentation and specifications
• OpenAI — GPT-5.6 API pricing (Sol and Terra)
• Artificial Analysis — Intelligence Index, Coding Agent Index, and cost per task
• LMArena — human-preference Elo leaderboard

Last compared: July 2026. GPT-5.6 Sol and GPT-5.6 Terra both reached general availability on July 9, 2026; both tiers are new, and we will revise this comparison as independent benchmark coverage matures.