# gpt-6-astra decision evidence

GPT-6 Astra is OpenAI's closed-weight flagship, released on 2026-09-03 as its most capable model, with a 1,050,000-token context window (922,000 input), 128,000 output tokens and text and image input; its tool calling requires the Responses API. It placed first on two real robot tasks in Cortex AI's evaluation and third on AI Coding Daily's coding leaderboard, behind only Claude Opus 5.5, while users report more cost and quota per task than GPT-6 Sol, and one moved a coding project back to DeepSeek V4.1 Flash after $130 of Astra turns.

Verified: 2026-09-24; snapshot: 2026-09-25.

## Published model card

As the vendor publishes it for the model and its weights; these are not the limits of a hosted endpoint.

- Context window: 1,050,000 tokens (up to 922,000 input)
- Maximum output: 128,000 tokens
- Modalities: Text and image input; text output
- Parameter count: Not disclosed

## Open weights and deployment

Status: Unavailable. OpenAI publishes no GPT-6 Astra weights. The model is served through the OpenAI API (Responses, Chat Completions and Batch) and on Amazon Bedrock in us-west-2.

Open-weight alternatives: [deepseek-flash](https://dash.chinaapi.ai/models/deepseek-flash/deploy), [glm-5.3-flash](https://dash.chinaapi.ai/models/glm-5.3-flash/deploy)

## Real-task reports

### [Cortex AI: GPT-6 Astra, Gemini 3.8 Flash and Fable 5.1 on two real robot tasks](https://cortexrobot.ai/research/llm-real-world-robotics-evals/)

By [Cortex AI Research](https://cortexrobot.ai/) (@cortexairobot). Robotics team that builds real-world robot datasets and runs the MolmoAct2 real-world evaluations

Published: 2026-09-17; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Load a sandwich into a microwave and close the door; attach a 5 mm tip to a pipette
- Method: Each model controlled a bimanual YAM robot through an overhead and two wrist cameras with depth images, at medium reasoning effort with up to 10 minutes per task; an evaluator scored each attempt with partial credit for completed steps.
- 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.
- Microwave: 80%
- Pipette: 70%

### [GPT-6 Astra, Sol and Luna on one SVG animation prompt](https://x.com/xueyu1125/status/2102692499710193960)

By [雪瑜](https://x.com/xueyu1125) (@xueyu1125). Programmer and AI-tools builder on X who posted the timings and quota readings

Published: 2026-09-23; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Generate an SVG animation of a pelican riding a bicycle, shown in H5
- Method: Same prompt on GPT-6 Astra, GPT-6 Sol, GPT-6 Luna and GPT-5.6 Sol; the author recorded time taken and the share of a five-hour subscription quota each used.
- 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.
- Time: 6 min 3 s
- Five-hour quota used: 16%

### [Switching a coding project from GPT-6 Astra to DeepSeek V4.1 Flash](https://www.reddit.com/r/DeepSeek/comments/1wklqt2/i_tried_gpt_astra_turned_to_deepseek_41_flash/)

By [OwlZealousideal4779](https://www.reddit.com/user/OwlZealousideal4779/) (u/OwlZealousideal4779). r/DeepSeek member reporting their own coding-agent use

Published: 2026-09-19; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: The author's own coding project, first on GPT-6 Astra and then on DeepSeek V4.1 Flash
- Method: Self-reported spend and outcome of about a dozen GPT-6 Astra turns, followed by the switch to 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.
- GPT-6 Astra spend: $130

### [Four models build the Eiffel Tower in Three.js](https://x.com/Bhavani_00007/status/2102795814355763277)

By [Bhavy](https://x.com/Bhavani_00007) (@Bhavani_00007). X user who ran the same Three.js task on four models and reported cost and time

Published: 2026-09-23; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Build the Eiffel Tower in Three.js
- Method: Same task on Claude Opus 5.5, GPT-6 Astra, GPT-6 Sol and Kimi K3; the author reported cost and time for each and compared the results by eye.
- 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 Astra: $7.45 · 8 min
- Claude Opus 5.5: $8.95 · 10 min

### [Four models build a lava lamp from one prompt](https://x.com/bridgebench/status/2102770788319310302)

By [Bridgebench](https://x.com/bridgebench) (@bridgebench). BridgeMind's benchmark account, which calls itself the official AI benchmark of the vibe coding movement

Published: 2026-09-23; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: Build a lava lamp from a single prompt
- Method: Same prompt on Claude Opus 5.5, GPT-6 Astra, GPT-6 Sol and GPT-6 Luna; the account reported cost and time, asked readers which did best and commented on their answers in replies.
- 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 Astra: $0.63 · 5m 4s

### [GPT-6 Sol and GPT-6 Astra on a robot drawing benchmark](https://x.com/dimentary/status/2102539444834402314)

By [Dmytro Hrybov](https://x.com/dimentary) (@dimentary). ML research engineer working on physical learning and evaluations

Published: 2026-09-22; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: The author's robot-drawing-on-the-board benchmark
- Method: The same benchmark on both models; the author compared output tokens, drawing speed and running cost.
- 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.
- Output tokens: ~22k vs ~25k (Sol)

### [AI Coding Daily LLM Coding Leaderboard](https://aicodingdaily.com/leaderboard)

By [AI Coding Daily](https://www.youtube.com/@AICodingDaily) (@AICodingDaily). Coding-model testing channel and website that runs its own leaderboard and sells premium tutorials and channel advertising

Published: 2026-09-24; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: 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
- Method: Each model and reasoning setting is a separate row run in a coding harness, with the average cost and time per run. Code quality is graded by a judge model against a published 100-point rubric and scaled to 20 points per project; behavioural reliability runs each of four projects five times and scores the checks every attempt fails.
- 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.
- Points (medium): 56.03
- Rank: 3 of 47

### [kzhu's six-question LLM test leaderboard](https://www.techgogogo.com/llm-benchmark/)

By [AI产品狙击手 kzhu](https://www.youtube.com/@kevinzhu9305) (@kevinzhu9305). Chinese YouTube reviewer who scores models on a six-question test and publishes every score with per-question notes

Published: 2026-09-24; retrieved: 2026-09-24; verified: 2026-09-24.

- Task: 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
- Method: Each row names the harness the model ran in (Pi, Claude Code, WorkBuddy and others) and the date; the author scores every question with a note, and warns that totals are only comparable within one test version.
- 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.
- Score: 58 / 60 (v2.1)

## Public leaderboard coverage

A board missing from this list has no figure for this model in this snapshot; missing coverage is not a zero.

- LMArena Text Arena — Arena score (Elo): 1477 ±7 (retrieved 2026-10-05) [source](https://arena.ai/leaderboard/text)
- LiveBench — Overall: 82.2 (retrieved 2026-10-04) [source](https://livebench.ai/)
- Artificial Analysis Intelligence Index — Intelligence Index: 53 (retrieved 2026-09-21) [source](https://artificialanalysis.ai/leaderboards/models)
- Epoch Capabilities Index (ECI) — General ECI: 167 (163 - 171) (retrieved 2026-10-04) [source](https://epoch.ai/eci)
- Vals Index — Accuracy (GDP-weighted finance, coding, legal and tax): 63.13 ±1.19 (retrieved 2026-10-04) [source](https://www.vals.ai/benchmarks/vals_index)
- LMArena Text Arena · Chinese — Arena score (Elo): 1510 ±22 (retrieved 2026-10-05) [source](https://arena.ai/leaderboard/text/chinese)
- LMArena Text Arena · Coding — Arena score (Elo): 1543 ±13 (retrieved 2026-10-05) [source](https://arena.ai/leaderboard/text/coding)
- LiveBench · Coding — Coding: 80.4 (retrieved 2026-10-04) [source](https://livebench.ai/)
- LiveBench · Agentic Coding — Agentic Coding: 57.3 (retrieved 2026-10-04) [source](https://livebench.ai/)
- Artificial Analysis · Output Speed — Median output tokens/s: 58 (retrieved 2026-09-22) [source](https://artificialanalysis.ai/leaderboards/models)
- Artificial Analysis · GDPval-AA v2.1 — Agentic Real-World Work Tasks, (Elo-500)/2000 (%): 52 (retrieved 2026-09-21) [source](https://artificialanalysis.ai/leaderboards/models)
- Artificial Analysis · Terminal-Bench v4.0 — Agentic Coding & Terminal Use (%): 59 (retrieved 2026-09-21) [source](https://artificialanalysis.ai/leaderboards/models)
- Artificial Analysis · MLCR-AA — Medical Long Context Reasoning (%): 35.0 (retrieved 2026-09-07) [source](https://artificialanalysis.ai/evaluations/mlcr-aa)

## Curated comparisons

- [claude-fable-5-1 vs gpt-6-astra](https://dash.chinaapi.ai/models/compare/claude-fable-5-1-vs-gpt-6-astra)
- [claude-opus-5-5 vs gpt-6-astra](https://dash.chinaapi.ai/models/compare/claude-opus-5-5-vs-gpt-6-astra)
- [deepseek-flash vs gpt-6-astra](https://dash.chinaapi.ai/models/compare/deepseek-flash-vs-gpt-6-astra)
- [gemini-3.8-flash vs gpt-6-astra](https://dash.chinaapi.ai/models/compare/gemini-3.8-flash-vs-gpt-6-astra)
- [gpt-6-astra vs gpt-6-luna](https://dash.chinaapi.ai/models/compare/gpt-6-astra-vs-gpt-6-luna)
- [gpt-6-astra vs gpt-6-sol](https://dash.chinaapi.ai/models/compare/gpt-6-astra-vs-gpt-6-sol)

## Official sources

- [GPT-6 Astra model documentation](https://developers.openai.com/api/docs/models/gpt-6-astra) — OpenAI, retrieved 2026-09-24
- [OpenAI API changelog](https://developers.openai.com/api/docs/changelog) — OpenAI, retrieved 2026-09-24
- [GPT-6 Astra System Card](https://deploymentsafety.openai.com/gpt-6-astra) — OpenAI, retrieved 2026-09-24