Jev vs Fable 5.1 vs GPT-6 Astra: a spec-by-spec comparison
The author compares the three on specs, benchmarks and price, arguing Jev handles classification, routing and scoring while Fable and Astra generate, and outlines a workflow chaining all three.
JEV VS CLAUDE FABLE 5.1 VS GPT-6 ASTRA: I compared all 3 spec by spec, benchmark by benchmark, price by price Jev decides. Fable and Astra generate. They do different jobs Where Fable 5.1 wins: - CursorBench 3.2: 73.4% - 75% cheaper than before - Cache reads at $0.25 per 1M tokens - Deep coding, research, and analysis Where GPT-6 Astra wins: - OSWorld 2.0: 72.6% - Terminal-Bench 4.0: 57.9% vs Fable's 55.8% - Computer use and browsing built in, with 1.05M context Where Jev wins: - 70 to 500ms per decision - $0.042 per 1M input tokens, and output is free - Many questions in one call, with a confidence score on every answer Fable and Astra cost the EXACT same on the API: $10 in, $50 out per 1M tokens. So the job decides which one you use The real power comes when you use all 3 TOGETHER: 1. Jev classifies, routes and scores every incoming task 2. Fable takes anything that needs reasoning or code 3. Astra takes the long computer work: research, docs 4. Jev checks the output and gates on confidence before anything ships I would pick Astra over Fable if: - The job lives in a browser, in files, or in Drive, Gmail, Slack or GitHub - You're already on ChatGPT Pro (Plus chat has no Astra) - Your input stays under 272K tokens, because the price doubles past that Astra's and Jev's numbers are vendor-run so far, and all of this can change with the next update Pick the model by the job. Switch the day the job changes. Full cheat sheet below:
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