claude-fable-5-1 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-fable-5-1 | 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.
| Evaluation | claude-fable-5-1 | gpt-6-astra |
|---|---|---|
| LMArena Text Arena — Arena score (Elo) | 1498 ±8 | 1480 ±12 |
| LiveBench — Overall | 83.4 | 82.2 |
| Artificial Analysis Intelligence Index — Intelligence Index | 53 | 53 |
| Epoch Capabilities Index (ECI) — General ECI | 165 (162 - 170) | 167 (163 - 172) |
| Vals Index — Accuracy (GDP-weighted finance, coding and legal) | 68.83 ±1.08 | 66.61 ±1.09 |
| LMArena Text Arena · Chinese — Arena score (Elo) | 1592 ±32 | — |
| LMArena Text Arena · Coding — Arena score (Elo) | 1519 ±18 | 1543 ±23 |
| LiveBench · Coding — Coding | 86.4 | 80.4 |
| LiveBench · Agentic Coding — Agentic Coding | 66.1 | 57.3 |
| Artificial Analysis · Output Speed — Median output tokens/s | 65 | 58 |
| Artificial Analysis · GDPval-AA v2.1 — Agentic Real-World Work Tasks, (Elo-500)/2000 (%) | 62 | 52 |
| Artificial Analysis · Terminal-Bench v4.0 — Agentic Coding & Terminal Use (%) | 52 | 59 |
| Artificial Analysis · MLCR-AA — Medical Long Context Reasoning (%) | 71.1 | 35.0 |
Real-task reports
claude-fable-5-1
- Step 5 Preview and Claude Fable 5.1 on one front-end prompt — Build a front-end page from a single prompt. Finding: Claude Fable 5.1 took 40 minutes and $17 on the front-end prompt that Step 5 Preview finished in 10 minutes for about $0.50; the author left the quality comparison to readers. Limitation: One prompt; the post leaves the quality comparison to readers rather than scoring it, and the cost gap follows each vendor's list prices.
- 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: Fable 5.1 scored 6.7% on the microwave task and 20% on the pipette task, behind GPT-6 Astra (80% and 70%) and Gemini 3.8 Flash (13.3% and 30%). 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.