gemini-3.8-flash 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 | gemini-3.8-flash | gpt-6-astra |
|---|---|---|
| Model Type | LLM | LLM |
| Context window | 1,048,576 tokens | 1,050,000 tokens |
| Maximum output | 65,536 tokens | 128,000 tokens |
| Input/output modalities | text, image, video, audio, pdf → text | text, image → text |
| Endpoint | openai · gemini | 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.
| Evaluation | gemini-3.8-flash | gpt-6-astra |
|---|---|---|
| LMArena Text Arena — Arena score (Elo) | 1495 ±5 | 1477 ±7 |
| Artificial Analysis Intelligence Index — Intelligence Index | 41 | 53 |
| Epoch Capabilities Index (ECI) — General ECI | 157 (155 - 160) | 167 (163 - 171) |
| Vals Index — Accuracy (GDP-weighted finance, coding, legal and tax) | 55.95 ±1.14 | 65.02 ±1.10 |
| LMArena Text Arena · Chinese — Arena score (Elo) | 1536 ±14 | 1510 ±22 |
| LMArena Text Arena · Coding — Arena score (Elo) | 1530 ±8 | 1543 ±13 |
| Artificial Analysis · Output Speed — Median output tokens/s | 297 | 58 |
| Artificial Analysis · GDPval-AA v2.1 — Agentic Real-World Work Tasks, (Elo-500)/2000 (%) | 46 | 52 |
| Artificial Analysis · Terminal-Bench v4.0 — Agentic Coding & Terminal Use (%) | 20 | 59 |
| Artificial Analysis · MLCR-AA — Medical Long Context Reasoning (%) | 21.7 | 35.0 |
Real-task reports
gemini-3.8-flash
- RemakeBench Launch 005: four Flash models on 14 fixtures — Fourteen fixtures spanning game building, 3D scenes, CAD, drawing and robot control, run on Qwen 3.8 Flash, DeepSeek V4.1 Flash, GLM 5.3 Flash and Gemini 3.8 Flash. Finding: No model won overall. Gemini 3.8 Flash made the fastest single-turn Rubik's Cube pass (15 min 35 s, $2.09) and was the editorial winner of the robot Eiffel Tower drawing (8 min 25 s, $1.36), but did not complete the three-move extension ($5.65) and did not deliver the required Turbofan CAD assembly. Limitation: Pass/fail labels are video judgments without a machine-readable validator, and single attempts do not establish reliability; the lab publishes no aggregate score or overall winner. Gemini's displayed costs are several times the other models' on most fixtures (for example $2.09 against $0.0957–$0.3414 on the single-turn cube), and its CAD presentation clip is operator work after the run.
- 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: Gemini 3.8 Flash scored 13.3% on the microwave task and 30% on the pipette task, second to GPT-6 Astra (80% and 70%) and ahead of Fable 5.1 (6.7% and 20%). 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.
- 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: Gemini 3.8 Flash at high reasoning scored 41.67 in Antigravity, 40th of 47 rows; it was tested on a subscription, so the board shows no cost. 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: Gemini 3.8 Flash scored 49/60 on version 2.1 in the DeepSeek harness: perfect constrained sentences and reasoning, but weaker coding, with a space-shooter control bug, a crude racing game with drifting cars and Trello lists that could not be reordered. 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.