claude-opus-5-5 vs gpt-6-astra
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 | claude-opus-5-5 | gpt-6-astra |
|---|---|---|
| Model Type | LLM | LLM |
| Context window | 1,000,000 tokens | 1,050,000 tokens |
| Maximum output | 128,000 tokens | 128,000 tokens |
| Input/output modalities | text, image → text | text, image → text |
| Endpoint | anthropic · openai | 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 | claude-opus-5-5 | gpt-6-astra |
|---|---|---|
| LMArena Text Arena — Arena score (Elo) | 1504 ±9 | 1477 ±7 |
| Artificial Analysis Intelligence Index — Intelligence Index Retrieved on different dates | 58 (retrieved 2026-09-22) | 53 (retrieved 2026-09-21) |
| Epoch Capabilities Index (ECI) — General ECI | 167 (164 - 172) | 167 (163 - 171) |
| Vals Index — Accuracy (GDP-weighted finance, coding, legal and tax) | 70.18 ±1.00 | 65.02 ±1.10 |
| LMArena Text Arena · Chinese — Arena score (Elo) | 1571 ±29 | 1510 ±22 |
| LMArena Text Arena · Coding — Arena score (Elo) | 1538 ±18 | 1543 ±13 |
| Artificial Analysis · Output Speed — Median output tokens/s | — | 58 |
| Artificial Analysis · GDPval-AA v2.1 — Agentic Real-World Work Tasks, (Elo-500)/2000 (%) Retrieved on different dates | 67 (retrieved 2026-09-22) | 52 (retrieved 2026-09-21) |
| Artificial Analysis · Terminal-Bench v4.0 — Agentic Coding & Terminal Use (%) Retrieved on different dates | 60 (retrieved 2026-09-22) | 59 (retrieved 2026-09-21) |
| Artificial Analysis · MLCR-AA — Medical Long Context Reasoning (%) | — | 35.0 |
Real-task reports
claude-opus-5-5
- 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: Opus 5.5 took 26 minutes and built a fuller, explorable solar system with more detail, using 16% of the $20 plan's usage; GPT-6 Sol took 10 minutes for three planets and 1% of the $200 plan's usage. 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: Opus 5.5 cost $8.95 and took 10 minutes; the author preferred its frontend and 3D detailing to GPT-6 Astra's ($7.45, 8 minutes), found GPT-6 Sol ($3.90, 5 minutes) disappointing and 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: Opus 5.5 used 10% of the weekly limit (115 million tokens) over 2 hours and scored 5/10, behind GPT-6 Sol at 7/10 (29 million tokens, 69 minutes) and Grok 4.7 at 6/10; the author still recommends Claude at $20 for its usage allowance. 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.
- Eight weeks of Claude Code usage re-priced at Opus 5 and Opus 5.5 rates — Price every request from eight weeks of the author's own Claude Code transcripts at Opus 5 and Opus 5.5 list prices. Finding: 97% of the tokens were cache reads, so the same usage came to $2,849 at Opus 5.5 rates against $5,071 at Opus 5 rates — 44% less, rather than the 20% the headline input and output prices suggest. Limitation: It re-prices the same token counts and does not measure whether Opus 5.5 uses more or fewer tokens on the same work; list prices, not subscription billing; the author built the tool that read the transcripts.
- Four models build a lava lamp from one prompt — Build a lava lamp from a single prompt. Finding: Claude Opus 5.5 was the slowest and most expensive at $1.27 and 9 min 35 s, and in replies the account agreed it had a rough showing, answering 'Not on this test' to a claim that Opus beat Sol. 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: Claude Opus 5.5 cost $0.69 and took 6 min 8 s; the account called it 'a significant leap for design', while GPT-6 Sol did the test for $0.08 in 1 min 33 s. 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: Claude Opus 5.5 took 12.7 minutes and $2.86 against GPT-6 Sol's 11.4 minutes and $1.09; the author called the gap 'not even close' and, in replies, agreed 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.
- 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: Claude Opus 5.5 led the board at 57.83 on high reasoning ($0.79 and 3 min 10 s per run in Claude Code), with its medium setting second at 57.37. 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: Claude Opus 5.5 scored 55/60 on version 2.2 in Pi (54 in the author's note), 9 or 10 on every question; the author called its pelican SVG the best implementation seen and found nothing to fault in the browser OS, racing game or 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-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.