gpt-6-luna vs gpt-6-sol
Parameters, independent leaderboard coverage, real-task reports and open-weight availability are shown side by side; missing coverage is not a zero.
Parameters
| Comparison item | gpt-6-luna | gpt-6-sol |
|---|---|---|
| Model Type | LLM | LLM |
| Context window | 1,050,000 tokens | 1,050,000 tokens |
| Maximum output | 128,000 tokens | 128,000 tokens |
| Input/output modalities | text, image → text | text, image → text |
| Endpoint | openai · openai-response | openai · openai-response |
| Open weights | Unavailable | Unavailable |
| License | Proprietary hosted model | Proprietary hosted model |
Leaderboard coverage
Every independent board covering either model is retained; an em dash means that board has not published a figure for that model.
Real-task reports
gpt-6-luna
- GPT-6 Astra, Sol and Luna on one SVG animation prompt — Generate an SVG animation of a pelican riding a bicycle, shown in H5. Finding: GPT-6 Luna was the quickest and lightest of the four at 2 min 18 s and 1% of the five-hour quota, but the author dismissed its result as a flop (“拉完了”), ranking it alongside MiMo-V2.6-Flash. Limitation: One prompt judged by the author without a score; the quota shares are a subscription meter, not API cost. X's automatic translation renders the verdict as its opposite, so read the original Chinese.
- Four models build a lava lamp from one prompt — Build a lava lamp from a single prompt. Finding: GPT-6 Luna cost under $0.01 and took 43 seconds, the cheapest and fastest of the four (Claude Opus 5.5: $1.27 and 9 min 35 s); the account left the quality verdict to readers. Limitation: One prompt with the quality verdict left mostly to readers; the account is BridgeMind's own benchmark and does not say how the models were accessed.
- GPT-6 Luna at max reasoning on a personal chip benchmark — The author's chip benchmark. Finding: GPT-6 Luna at max reasoning searched for and generated its own images, launched about 15 sub-agents to research and judge the result, and took almost twice as long as Astra while using about 1% of the weekly usage on the ChatGPT x20 plan; the author expects to make it their default model. Limitation: The benchmark and its scoring are not described and no score is given; usage is a subscription meter, not API cost.
- AI Coding Daily LLM Coding Leaderboard — Implement and harden production-shaped projects in several stacks, including a Laravel app, a Go service and a React/TypeScript app, from the same prompts. Finding: GPT-6 Luna scored 51.99 at max reasoning for about $0.03 and 11 min per run in Codex CLI, 11th of 47 rows and just above GPT-5.6 Luna at max (51.13); at medium reasoning it fell to 40.20. Limitation: One author's benchmark whose code-quality half is graded by a judge model; rows run in different harnesses, the page is updated as models are added (read on 2026-09-24, 47 rows), and the author sells premium tutorials and advertising.
- kzhu's six-question LLM test leaderboard — Six questions scored out of 10: constrained sentence writing, two reasoning puzzles (replaced by pelican and compound-bow SVG animations in version 2.2), a browser OS with a space shooter, a 3D racing game and a Trello-style board. Finding: GPT-6 Luna scored 40/60 on version 2.2 in Pi (the author's note says 41): full marks on the constrained sentences, but physically wrong SVG animations (4 and 6), a flat 2D space shooter, a cluttered racing track and small text on the Trello board. Limitation: One author's scores on six questions; rows use different harnesses and test versions (2.1 or 2.2), and a few rows' table total differs by one point from the author's note.
gpt-6-sol
- Claude Opus 5.5 and GPT-6 Sol on one interactive website prompt — Build an interactive website about imaginary planets from a single prompt. Finding: GPT-6 Sol took 10 minutes and built three planets, using 1% of the $200 plan's usage; Claude Opus 5.5 took 26 minutes for a fuller, explorable solar system with more detail, using 16% of the $20 plan's usage. The author found the planets surprisingly similar: Sol faster, Opus bigger and more detailed. Limitation: One prompt judged by the author; the usage shares are meters of two different subscription plans, not API costs.
- Four models build the Eiffel Tower in Three.js — Build the Eiffel Tower in Three.js. Finding: GPT-6 Sol was the cheapest and fastest of the four at $3.90 and 5 minutes, but the author found its result a bit disappointing; they preferred Claude Opus 5.5 ($8.95, 10 minutes) and rated Kimi K3 ($4.04, 6 minutes) strong for the price. Limitation: One task judged by eye by the author; the post does not say how the models were run or how the costs were counted.
- Claude Pro, Codex Plus and SuperGrok on the same work at maximum settings — One piece of work run on each $20 subscription; the task itself is shown only in the author's video. Finding: GPT-6 Sol on Codex Plus used 9% of the weekly limit (29 million tokens) over 69 minutes and scored 7/10, the highest of the three plans, against 5/10 for Opus 5.5 and 6/10 for Grok 4.7; the author still recommends Claude at $20 because it yields far more tokens, and puts Codex Plus at about $330 of API usage a month. Limitation: The post does not describe the task, the quality score is the author's own, and the numbers are subscription meters rather than API usage.
- GPT-6 Astra, Sol and Luna on one SVG animation prompt — Generate an SVG animation of a pelican riding a bicycle, shown in H5. Finding: GPT-6 Sol took 4 min 42 s and used 2% of the five-hour quota, against 6 min 3 s and 16% for GPT-6 Astra; the author puts Sol's quota use at an eighth of Astra's and recommends Sol when quota matters. Limitation: One prompt; the quota shares are a subscription meter, not API cost, and the post gives no quality score for Sol.
- Four models build a lava lamp from one prompt — Build a lava lamp from a single prompt. Finding: GPT-6 Sol cost $0.08 and took 50 seconds, against $1.27 and 9 min 35 s for Claude Opus 5.5 and $0.63 and 5 min 4 s for GPT-6 Astra; in replies the account agreed Opus had a rough showing on this test. Limitation: One prompt with the quality verdict left mostly to readers; the account is BridgeMind's own benchmark and does not say how the models were accessed.
- Claude Opus 5.5 and GPT-6 Sol on a black hole merger test — The account's black hole merger visualisation test. Finding: GPT-6 Sol cost $0.08 and took 1 min 33 s, an eighth of Claude Opus 5.5's cost and four times faster, but the account judged Opus 5.5 'a significant leap for design. It's not close.' Limitation: One test judged by the account; the task is shown only in the attached video.
- Claude Opus 5.5 and GPT-6 Sol on one motion-graphic prompt — Make a 20-second motion graphic explaining how a language model turns a prompt into an answer and why long answers cost more. Finding: GPT-6 Sol took 11.4 minutes and $1.09 against Claude Opus 5.5's 12.7 minutes and $2.86; the author called the gap 'not even close' and agreed with a reply that Sol's result was far worse. Limitation: One subjective prompt; the verdict comes from the author's post and replies rather than a score.
- GPT-6 Sol and GPT-6 Astra on a robot drawing benchmark — The author's robot-drawing-on-the-board benchmark. Finding: GPT-6 Sol used about 25k output tokens against Astra's 22k and its controller drew twice as fast, at almost a fifth of the running cost; the author had expected stronger spatial reasoning from it. Limitation: One run of a benchmark the post describes only by name, with no score.
- AI Coding Daily LLM Coding Leaderboard — Implement and harden production-shaped projects in several stacks, including a Laravel app, a Go service and a React/TypeScript app, from the same prompts. Finding: GPT-6 Sol scored 52.42 at high reasoning ($0.31 and 5 min 18 s per run in Codex CLI) and 50.07 at medium, 8th and 16th of 47 rows; Claude Opus 5.5 led with 57.83 and GPT-6 Astra at medium scored 56.03. Limitation: One author's benchmark whose code-quality half is graded by a judge model; rows run in different harnesses, the page is updated as models are added (read on 2026-09-24, 47 rows), and the author sells premium tutorials and advertising.
- kzhu's six-question LLM test leaderboard — Six questions scored out of 10: constrained sentence writing, two reasoning puzzles (replaced by pelican and compound-bow SVG animations in version 2.2), a browser OS with a space shooter, a 3D racing game and a Trello-style board. Finding: GPT-6 Sol scored 44/60 on version 2.2 in Pi: full marks on the constrained sentences, but SVG animations with reversed wheels and an arrow that broke physics (6 each), a space shooter that only moved sideways, and an English interface for a Chinese Trello brief; Claude Opus 5.5 scored 55 on the same version. Limitation: One author's scores on six questions; rows use different harnesses and test versions (2.1 or 2.2), and a few rows' table total differs by one point from the author's note.