gpt-6-astra 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 itemgpt-6-astragpt-6-sol
Model TypeLLMLLM
Context window1,050,000 tokens1,050,000 tokens
Maximum output128,000 tokens128,000 tokens
Input/output modalitiestext, image → texttext, image → text
Endpointopenai · openai-responseopenai · openai-response
Open weightsUnavailableUnavailable
LicenseProprietary hosted modelProprietary 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.

Evaluationgpt-6-astragpt-6-sol
LMArena Text Arena — Arena score (Elo)1480 ±12
LiveBench — Overall82.2
Artificial Analysis Intelligence Index — Intelligence Index
Retrieved on different dates
53 (retrieved 2026-09-21)48 (retrieved 2026-09-22)
Epoch Capabilities Index (ECI) — General ECI167 (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 — Coding80.4
LiveBench · Agentic Coding — Agentic Coding57.3
Artificial Analysis · Output Speed — Median output tokens/s58104
Artificial Analysis · GDPval-AA v2.1 — Agentic Real-World Work Tasks, (Elo-500)/2000 (%)
Retrieved on different dates
52 (retrieved 2026-09-21)49 (retrieved 2026-09-22)
Artificial Analysis · Terminal-Bench v4.0 — Agentic Coding & Terminal Use (%)
Retrieved on different dates
59 (retrieved 2026-09-21)44 (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-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.