Data snapshot: 11 July 2026
Primary source: Artificial Analysis GPT‑5.6 launch analysis and its live LLM leaderboard
Scope: all 15 combinations of Sol, Terra, and Luna at low
, medium
, high
, xhigh
, and max
reasoning effort.
Short answer:Default toLunafor volume work andSolwhen quality matters. TreatTerra as a dominated middle tier, not a default. Raise reasoning effort before moving to a larger model when the next effort remains on the better price/intelligence frontier. Use Solmax
only where the last few quality points can change the outcome.
OpenAI has not created a simple “small / medium / large” ladder. It has created three overlapping ladders. A higher-effort Luna can meet or beat a lower-effort Terra; a higher-effort Terra can overlap lower-effort Sol; and, most importantly, Artificial Analysis finds that Terra is never on the Intelligence-vs-Cost Pareto frontier. At every Terra setting, Luna or Sol offers either more intelligence for no more money or similar intelligence for less.
That produces a much simpler practical policy than the 15 model/effort combinations suggest:
Luna is the economic default. Use it for triage, extraction, transformations, routine coding, subagents, and any workload run many times.Sol is the quality default. Use it for difficult implementation, synthesis, consequential analysis, polished deliverables, and tasks where retries or mistakes are expensive.Terra is a niche compatibility choice. Choose it only after your own workload test shows a specific latency, style, reliability, or provider constraint that the aggregate benchmark does not capture.Reasoning effort is a real purchasing decision. It changes measured capability and per-task cost substantially. Do not set every call tomax
by habit.Escalate by uncertainty and consequence, not by prompt length. Long but mechanical work may suit Luna; a short but ambiguous architectural decision may warrant Sol.
| Model | Reasoning | Intelligence Index | Cost per Intelligence Index task | Coding Agent Index | What it establishes |
|---|---|---|---|---|---|
| GPT‑5.6 Luna | max | 51 | |||
| $0.21 | |||||
| 75 | |||||
| Strong low-cost ceiling; matches/exceeds GLM‑5.2 max and Gemini 3.5 Flash intelligence at lower cost | |||||
| GPT‑5.6 Terra | max | 55 | |||
| $0.55 | |||||
| 77 | |||||
| A 4-point gain over Luna max, but 2.6× the general benchmark cost | |||||
| GPT‑5.6 Sol | max | 59 | |||
| $1.04 | |||||
| 80 | |||||
| Near-frontier quality; one point behind Claude Fable 5 at about one-third of Fable’s cost |
| Upgrade | Intelligence gain | Relative gain | Cost increase | Cost multiple | Marginal cost per extra Index point |
|---|---|---|---|---|---|
| Luna max → Terra max | +4 | +7.8% | +$0.34 | 2.62× | |
| $0.085 / point | |||||
| Terra max → Sol max | +4 | +7.3% | +$0.49 | 1.89× | |
| $0.123 / point | |||||
| Luna max → Sol max | +8 | +15.7% | +$0.83 | 4.95× | |
| $0.104 / point |
The economic shape is stark: Luna max buys 86% of Sol max’s measured intelligence for about 20% of its task cost. Sol is not a value upgrade in the ordinary sense; it is a premium paid for the difficult final band of capability.
The live leaderboard was filtered to exactly these 15 variants and no other models. These are the values displayed directly by Artificial Analysis—not estimates read from dot positions.
| Model | Effort | Intelligence Index | Cost / Index task | Output tokens / task | Output speed | Decode time / task |
|---|---|---|---|---|---|---|
| Luna | low | 33 | ||||
| $0.04 | ||||||
| 2k | ||||||
| 204 tok/s | ||||||
| 0.2 min | ||||||
| Luna | medium | 38 | ||||
| $0.05 | ||||||
| 4k | ||||||
| 195 tok/s | ||||||
| 0.3 min | ||||||
| Luna | high | 46 | ||||
| $0.09 | ||||||
| 8k | ||||||
| 191 tok/s | ||||||
| 0.7 min | ||||||
| Luna | xhigh | 49 | ||||
| $0.14 | ||||||
| 12k | ||||||
| 191 tok/s | ||||||
| 1.0 min | ||||||
| Luna | max | 51 | ||||
| $0.21 | ||||||
| 19k | ||||||
| 206 tok/s | ||||||
| 1.5 min | ||||||
| Terra | low | 40 | ||||
| $0.10 | ||||||
| 2k | ||||||
| 123 tok/s | ||||||
| 0.3 min | ||||||
| Terra | medium | 46 | ||||
| $0.13 | ||||||
| 4k | ||||||
| 116 tok/s | ||||||
| 0.5 min | ||||||
| Terra | high | 49 | ||||
| $0.24 | ||||||
| 8k | ||||||
| 120 tok/s | ||||||
| 1.1 min | ||||||
| Terra | xhigh | 52 | ||||
| $0.33 | ||||||
| 11k | ||||||
| 136 tok/s | ||||||
| 1.3 min | ||||||
| Terra | max | 55 | ||||
| $0.55 | ||||||
| 19k | ||||||
| 142 tok/s | ||||||
| 2.2 min | ||||||
| Sol | low | 49 | ||||
| $0.20 | ||||||
| 3k | ||||||
| 64 tok/s | ||||||
| 0.6 min | ||||||
| Sol | medium | 54 | ||||
| $0.31 | ||||||
| 4k | ||||||
| 61 tok/s | ||||||
| 1.1 min | ||||||
| Sol | high | 56 | ||||
| $0.45 | ||||||
| 7k | ||||||
| 63 tok/s | ||||||
| 1.8 min | ||||||
| Sol | xhigh | 58 | ||||
| $0.68 | ||||||
| 10k | ||||||
| 69 tok/s | ||||||
| 2.4 min | ||||||
| Sol | max | 59 | ||||
| $1.04 | ||||||
| 15k | ||||||
| 74 tok/s | ||||||
| 3.3 min |
Output tokens / task
and decode times are rounded as displayed by AA. Decode time excludes time to first token and other overhead. Output speed is the first-party API’s generation speed after streaming begins.
Do not miss the overlap:
- Luna
high
equals Terramedium
at 46, for $0.09 instead of $0.13. - Luna
xhigh
equals Terrahigh
and Sollow
at 49, for $0.14 instead of $0.24 or $0.20. - Luna
max
reaches 51 for $0.21; Terraxhigh
buys one more point for another $0.12. - Sol
medium
scores 54 at $0.31, beating Terraxhigh
at 52/$0.33. - Sol
high
scores 56 at $0.45, beating Terramax
at 55/$0.55.
Those comparisons explain why Luna and Sol form the frontier while Terra does not.
Using the displayed integer Index scores and cent-rounded task costs, a point is dominated when another point is at least as intelligent and no more expensive.
| Dominated setting | Better displayed alternative | Result |
|---|---|---|
| Terra low — 40 / $0.10 | Luna high — 46 / $0.09 | +6 points, save $0.01 |
| Terra medium — 46 / $0.13 | Luna high — 46 / $0.09 | Same score, save 31% |
| Terra high — 49 / $0.24 | Luna xhigh — 49 / $0.14 | Same score, save 42% |
| Terra xhigh — 52 / $0.33 | Sol medium — 54 / $0.31 | +2 points, save $0.02 |
| Terra max — 55 / $0.55 | Sol high — 56 / $0.45 | +1 point, save 18% |
| Sol low — 49 / $0.20 | Luna xhigh — 49 / $0.14 | Same displayed score, save 30% |
The resulting displayed frontier is:
Luna low → Luna medium → Luna high → Luna xhigh → Luna max → Sol medium → Sol high → Sol xhigh → Sol max
Scores and costs are rounded for display, so equal-looking points may differ beneath the shown precision. That is a reason to A/B test close crossovers—not a reason to ignore a large visible price gap.
| Step | Index gain | Task-cost increase | Marginal cost / displayed point |
|---|---|---|---|
| Luna low → medium | +5 | +$0.01 | $0.002 |
| Luna medium → high | +8 | +$0.04 | $0.005 |
| Luna high → xhigh | +3 | +$0.05 | $0.017 |
| Luna xhigh → max | +2 | +$0.07 | $0.035 |
| Terra low → medium | +6 | +$0.03 | $0.005 |
| Terra medium → high | +3 | +$0.11 | $0.037 |
| Terra high → xhigh | +3 | +$0.09 | $0.030 |
| Terra xhigh → max | +3 | +$0.22 | $0.073 |
| Sol low → medium | +5 | +$0.11 | $0.022 |
| Sol medium → high | +2 | +$0.14 | $0.070 |
| Sol high → xhigh | +2 | +$0.23 | $0.115 |
| Sol xhigh → max | +1 | +$0.36 | $0.360 |
This is why max
should not be the reflexive setting. Diminishing returns are especially severe at the top of Sol.
This initially looked like the important crossover, but the direct values resolve it: Luna xhigh and Sol low both display 49, while Luna costs $0.14/task against Sol’s $0.20. Luna is also about 3× faster in output generation (191 vs 64 tok/s), although it uses far more output tokens on the benchmark (12k vs 3k). Luna max scores 51 at $0.21, only one cent above Sol low.
Default from the aggregate numbers: Luna xhigh if 49 is sufficient; Luna max if it is not.
Test Sol low anyway when: concise outputs, lower token use, or stronger-base-model behaviour matters more than streamed speed and displayed cost efficiency. At displayed AA precision, Sol low is not on the cost/intelligence frontier.
Terra max scores 55 at $0.55/task. Sol high scores 56 at $0.45/task. Aggregate data therefore gives no economic reason to pick Terra max over Sol high.
Default decision: Sol high.
Exception: a measured workload-specific Terra advantage.
Moving from Luna max to Terra max buys four Index points while multiplying cost by 2.62. That only makes sense if the workload has a threshold between those capability levels—e.g. Luna often fails a class of tasks that Terra reliably completes. If both still require review, the upgrade may buy little operational value.
The next four points cost another $0.49/task. Sol max is justified when those points affect acceptance rate, prevent expensive retries, or unlock tasks beneath other models. It is difficult to justify for ordinary throughput work.
There is exactly one displayed Index point between Sol xhigh and max, while task cost rises from $0.68 to $1.04: 53% more cost for a 1.7% Index increase. Decode time also rises from 2.4 to 3.3 minutes. This makes max
a deliberate specialist setting, not a sensible universal default.
Use `max`
for genuinely hard, long-horizon, consequential work. Use `xhigh`
for most “best model” production calls unless your own evals show a meaningful max advantage.
| Workload | First choice | Escalate to | Why |
|---|---|---|---|
| Classification, routing, tagging | Luna low | Luna medium | Cheap; extra depth usually has low value |
| Extraction, reformatting, deterministic transformations | Luna low/medium | Luna high | Scale matters more than frontier reasoning |
| Summaries and straightforward research synthesis | Luna medium | Luna high | Good balance; reserve max for genuinely tangled sources |
| Routine code edits with clear acceptance tests | Luna high | Luna max or Sol medium | Verification catches errors; skip the visibly dominated Sol-low point by default |
| Repository exploration / subagents | Luna medium/high | Luna max or Sol medium | Many calls make Luna economics compelling |
| Difficult debugging | Sol medium | Sol high | Strong base model and additional reasoning both matter |
| Architecture and cross-system changes | Sol high | Sol xhigh/max | Errors propagate and review is expensive |
| High-stakes analysis or irreversible decisions | Sol xhigh | Sol max + human review | Pay for the tail, but do not mistake benchmark strength for certainty |
| Polished reports, slides, spreadsheets | Sol high/xhigh | Sol max | Sol max has the highest Presentation Elo measured in AA‑Briefcase |
| Long-horizon knowledge work | Sol xhigh/max | — | Sol is second to Fable 5 in AA‑Briefcase, though Fable still leads rubric and analytical quality |
| Coding-agent benchmark chasing | Sol max in Codex | — | Leads the AA Coding Agent Index at 80 |
| Very high-volume mixed workload | Luna medium/high with escalation | Sol medium/high | Two-stage routing captures most value |
If 15 options are too many, expose only these internally:
Fast: Lunamedium
Standard: Lunahigh
Deep: Solmedium
orhigh
Exceptional override: Solmax
This removes Terra and most effort settings while preserving the useful economic frontier.
- Start with the cheapest tier appropriate to the consequence of failure.
- Require the model to identify uncertainty, missing evidence, and failed checks.
- Verify with tools/tests rather than asking the same model whether it is correct.
- Escalate once when evidence indicates a capability failure—not merely because the answer is stylistically imperfect.
- Send the stronger model the original evidence and failure state, not only the weaker model’s summary.
- Measure accepted outcomes, retries, latency, and total task cost.
A cheap call followed by an expensive retry can cost more than beginning with Sol. Conversely, using Sol max on thousands of easily verified subtasks wastes money without improving the final result.
Artificial Analysis reports:
Model (max , Codex harness) |
Coding Agent Index | Relative cost statement |
|---|---|---|
| Sol | 80 |
Baseline; leads all three included coding evaluations |
| Terra | 77 |
~60% lower cost/task than Sol |
| Luna | 75 |
~80% lower cost/task than Sol |
Sol max leads DeepSWE, Terminal‑Bench v2, and SWE‑Atlas‑QnA (tying Grok 4.5/Grok Build on the latter). Yet the practical difference is only five Index points from Luna max while Luna costs roughly one-fifth as much per coding task.
That suggests:
- Use Luna for parallel discovery, mechanical edits, test generation, migrations with strong checks, and bounded issues. - Use Sol for ambiguous bugs, planning that spans systems, difficult review, security-sensitive changes, and rescue after a weaker attempt fails. - Evaluate whole-task economics, not token price: accepted PR rate, number of retries, human review time, and regressions matter more than the nominal call cost. - Keep the harness fixed when comparing models. The Coding Agent Index evaluates model-plus-harness combinations; it is not a pure model score.
Published API prices per million tokens:
| Model | Input | Output | Cache read | Cache write |
|---|---|---|---|---|
| Luna | $1.00 | $6.00 | $0.10 | $1.25 |
| Terra | $2.50 | $15.00 | $0.25 | $3.125 |
| Sol | $5.00 | $30.00 | $0.50 | $6.25 |
Cache reads retain a 90% discount. GPT‑5.6 introduces OpenAI cache-write charges at 1.25× input price. Therefore:
- Stable, repeatedly reused prefixes can make caching highly valuable.
- Frequently changing “cached” context may incur writes without enough subsequent hits.
- Output-heavy agent loops are expensive: output is 6× input for every family member.
- A model that solves the task with fewer reasoning/output tokens can beat a cheaper token rate.
Sol max uses about 15k output tokens per Intelligence Index task, versus 16k for GPT‑5.5, and defines AA’s best Intelligence-vs-Output-Tokens frontier. It also uses fewer tokens while scoring higher than Claude Opus 4.8 max, GLM‑5.2 max, and Gemini 3.5 Flash high. This is why per-token price alone is misleading.
The Artificial Analysis Intelligence Index v4.1 combines nine evaluations: GDPval‑AA v2, τ³‑Banking, Terminal‑Bench v2.1, SciCode, Humanity’s Last Exam, GPQA Diamond, CritPt, AA‑Omniscience, and AA‑LCR.
Important qualifications:
- An Index point is an aggregate, not a guaranteed percentage improvement on your task.
- Close scores may not be operationally distinguishable without repeated workload-specific tests.
- Cost per task depends on AA’s prompts, token use, cache assumptions, and benchmark weights.
- Reasoning settings can change latency and output volume as well as score.
- Agent results depend on the harness, tools, prompt, context management, and stopping policy.
- Sol max’s AA‑Briefcase presentation strength does not imply the best analytical content. Claude Fable 5 still leads overall, including 56% vs 42% rubric score and1764 vs 1592 Analytical Quality Elo. - AA reports a small GPT‑5.6 Sol uplift over GPT‑5.5 in AA‑Omniscience accompanied by an increase in hallucination rate. More capable does not mean self-verifying.
Run a small blind evaluation before standardising. Sample 20–50 real tasks, preserve the original task distribution, and compare only plausible frontier settings:
- Luna medium
- Luna high
- Luna max
- Sol low
- Sol medium
- Sol high
- Sol xhigh or max for the hardest slice
Do not spend evaluation budget exhaustively testing Terra unless there is a concrete reason.
Record:
- pass/fail against an external rubric or tests;
- human correction minutes;
- number of retries and escalations;
- end-to-end latency;
- input, cache-write, cache-hit, reasoning, and answer tokens;
- actual cost per accepted result; - severity of failures, not only average score.
Then pick the cheapest setting that clears the required reliability threshold. The key metric is:
Total cost per accepted outcome = model cost + retry cost + reviewer time + expected failure cost.
A tiny benchmark gain can be worth a lot in a costly workflow and essentially nothing in a reversible one.
Best economic default: Luna high.Best family-transition pair: Luna max vs Sol medium.Best serious-work default: Sol high.Best near-frontier default: Sol xhigh.Best absolute GPT‑5.6 result: Sol max—but reserve it for tasks where the premium tail matters.Hardest tier to justify from aggregate data: every Terra setting.
The family becomes much less confusing once treated as two useful curves rather than three product tiers: Luna buys throughput; Sol buys capability; reasoning effort chooses how far up either curve to travel.
- Artificial Analysis, “ GPT‑5.6 benchmarks across Intelligence, Speed and Cost,” 9 July 2026. - Artificial Analysis live LLM leaderboard, inspected 11 July 2026. - All 15 variants were explicitly selected in the live model picker. The all-effort table transcribes AA’s directly displayed values; token and time values retain AA’s display rounding.
- Prices exclude platform subscriptions, human labour, and failure impact.