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) | 1493 ±9 | 1480 ±12 |
| LiveBench — Overall | 75.8 | 82.2 |
| Artificial Analysis Intelligence Index — Intelligence Index | 41 | 53 |
| Epoch Capabilities Index (ECI) — General ECI | 157 (155 - 161) | 167 (163 - 172) |
| Vals Index — Accuracy (GDP-weighted finance, coding and legal) | 62.25 ±1.01 | 66.61 ±1.09 |
| LMArena Text Arena · Chinese — Arena score (Elo) | 1543 ±32 | — |
| LMArena Text Arena · Coding — Arena score (Elo) | 1535 ±16 | 1543 ±23 |
| LiveBench · Coding — Coding | 72.5 | 80.4 |
| LiveBench · Agentic Coding — Agentic Coding | 54.2 | 57.3 |
| 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.
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.