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) | 1480 ±12 | — |
| LiveBench — Overall | 82.2 | — |
| 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 - 172) | — |
| Vals Index — Accuracy (GDP-weighted finance, coding and legal) | 66.61 ±1.09 | — |
| LMArena Text Arena · Coding — Arena score (Elo) | 1543 ±23 | — |
| LiveBench · Coding — Coding | 80.4 | — |
| LiveBench · Agentic Coding — Agentic Coding | 57.3 | — |
| 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.
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.