gpt-6-astra vs gpt-6-luna
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-astra | gpt-6-luna |
|---|---|---|
| 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.
A row marked as retrieved on different dates holds two readings taken on different days; a board re-fits or changes version as a whole between readings, so those two figures are not directly comparable.
| Evaluation | gpt-6-astra | gpt-6-luna |
|---|---|---|
| LMArena Text Arena — Arena score (Elo) | 1477 ±7 | 1443 ±7 |
| Artificial Analysis Intelligence Index — Intelligence Index Retrieved on different dates | 53 (retrieved 2026-09-21) | 37 (retrieved 2026-09-22) |
| Epoch Capabilities Index (ECI) — General ECI | 167 (163 - 171) | — |
| Vals Index — Accuracy (GDP-weighted finance, coding, legal and tax) | 65.02 ±1.10 | 52.32 ±1.12 |
| LMArena Text Arena · Chinese — Arena score (Elo) | 1510 ±22 | 1484 ±21 |
| LMArena Text Arena · Coding — Arena score (Elo) | 1543 ±13 | 1512 ±12 |
| Artificial Analysis · Output Speed — Median output tokens/s | 58 | 157 |
| Artificial Analysis · GDPval-AA v2.1 — Agentic Real-World Work Tasks, (Elo-500)/2000 (%) Retrieved on different dates | 52 (retrieved 2026-09-21) | 43 (retrieved 2026-09-22) |
| Artificial Analysis · Terminal-Bench v4.0 — Agentic Coding & Terminal Use (%) Retrieved on different dates | 59 (retrieved 2026-09-21) | 13 (retrieved 2026-09-22) |
| Artificial Analysis · MLCR-AA — Medical Long Context Reasoning (%) | 35.0 | — |
Real-task reports
gpt-6-astra
- Cortex AI: GPT-6 Astra, Gemini 3.8 Flash and Fable 5.1 on two real robot tasks — Load a sandwich into a microwave and close the door; attach a 5 mm tip to a pipette. Finding: GPT-6 Astra scored highest on both tasks, 80% on the microwave and 70% on the pipette, against 13.3% and 30% for Gemini 3.8 Flash and 6.7% and 20% for Fable 5.1; the lab credits it with retrying until the pipette tip docked and with checking that the microwave door latched. Limitation: The post does not say how many attempts each model had; scores are partial-credit judgements by the lab's evaluator, and the authors attribute the gap mainly to weaker depth perception in the lower-scoring models.
- 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 Astra took 6 min 3 s and used 16% of the five-hour quota, against 4 min 42 s and 2% for GPT-6 Sol; the author recommends Sol when quota matters. Limitation: One prompt; the quota shares are a subscription meter, not API cost, and the post scores no output quality for Astra.
- Switching a coding project from GPT-6 Astra to DeepSeek V4.1 Flash — The author's own coding project, first on GPT-6 Astra and then on DeepSeek V4.1 Flash. Finding: About a dozen GPT-6 Astra turns cost the author $130 in a day without meeting the requirements; they switched the project back to DeepSeek V4.1 Flash. Limitation: One uncontrolled self-report; the post does not describe what the Astra turns were asked to do.
- Four models build the Eiffel Tower in Three.js — Build the Eiffel Tower in Three.js. Finding: GPT-6 Astra cost $7.45 and took 8 minutes; the author preferred Claude Opus 5.5's result ($8.95, 10 minutes) for this task. 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.
- Four models build a lava lamp from one prompt — Build a lava lamp from a single prompt. Finding: GPT-6 Astra cost $0.63 and took 5 min 4 s, between Claude Opus 5.5 ($1.27, 9 min 35 s) and GPT-6 Sol ($0.08, 50 s). 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 Sol and GPT-6 Astra on a robot drawing benchmark — The author's robot-drawing-on-the-board benchmark. Finding: GPT-6 Astra used about 22k output tokens to Sol's 25k, but Sol's controller drew twice as fast and cost almost five times less to run, though the author had expected stronger spatial reasoning from Sol. 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 Astra at medium reasoning scored 56.03 ($0.69 and 3 min 8 s per run in Codex CLI), third of 47 rows behind both Claude Opus 5.5 settings. 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 Astra scored 58/60 on version 2.1, ahead of Fable 5.1 (54), Gemini 3.8 Flash (49) and Qwen3.8 Max 0902 (47) on the same version: all reasoning answers right and a near-flawless browser OS, with only a collision glitch in the racing game. 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. This row does not name a harness.
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.