Did Elon catch up? (Grok 4.7 is here)
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Read Video · Transkript & Insights
Diese Folge hat ein vollständiges Transkript + KI-Insights
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- 📄 Vollständiges Transkript mit Zeitstempeln
- ✨ KI-Zusammenfassung, Keywords & Mindmap
- 💡 Kernaussagen & Zitate
Episoden-Zeitlinie
Elon Musk's prediction that Grok 4.7 would match Opus 5.0 and the model's release at the same price and speed as Grok 4.6
- Elon tweeted about a week before release that Grok 4.7 should be roughly on par with Opus 5.0, not 5.1, setting a specific benchmark expectation.
- Grok 4.7 launched as a notable improvement over Grok 4.6 at the same price and speed, with xAI claiming it works longer on difficult tasks and checks its own work more carefully.
- The blog post describes Grok 4.7 as xAI's most capable model for coding and knowledge work with their best calibrated safeguards to date.
Cursorbench 4.0 results show Grok 4.7 is competitive with Opus 5 but not beating it, at roughly half the cost
- On Cursorbench 4.0, Grok 4.7 sits just behind Opus 5 on max thinking settings but costs about half as much to run the benchmark.
- There is a major performance delta between low thinking effort at 33% and extra-high thinking effort at 46.3%, one of the biggest effort curves outside of GPT 5.6 Soul.
- The host notes xAI now owns Cursor, so the benchmark should be viewed with that ownership relationship in mind.
Token efficiency and steps-per-task comparisons show Fable 5.1 leading on quality while Grok 4.7 wins on cost-effectiveness
- On average output tokens per task, Grok 4.7 is very comparable to Opus 5, and at lowest thinking effort it uses few tokens but scores poorly.
- Fable 5.1 is the quality winner but is multiple times more expensive than Grok 4.7, so its cost per completed task is much higher despite similar token usage.
- GPT 5.6 Soul appears most efficient on steps taken per task, which matches the host's personal experience of it having the most direct shot to task completion.
Schlüsselkonzepte
- Grok 4.7— The newly released xAI model that is the central subject of the episode.
- cost per task completed— The key metric the host argues matters most when evaluating models for real workloads.
- CursorBench— The coding benchmark used to compare Grok 4.7 against Opus 5 and other frontier models.
Bemerkenswerte Zitate
Grok 4.7 should be roughly on par with Opus 50, not 5.1.
🔥— Elon's own prediction sets a high bar that the host then tests, revealing whether the claim holds up.
It is not beating it. It's just behind it on the max thinking setting for both models but it is much less expensive about half the cost to run that benchmark.
🤯— Shows that near-frontier performance at half the cost is the real value proposition, not outright superiority.
Konkrete Handlungen
📊AI Model Evaluation
Cost per task completed is often more important than raw benchmark scores for real-world use.
This week, calculate the cost per task for your most common AI workflow using your current model and compare it to Grok 4.7's pricing.
Benchmark tables can be cherry-picked; always look for missing models or metrics.
Next time you see a model comparison, check if all relevant competitors are included and look for independent evaluations like Artificial Analysis.
💼Business Strategy
Open-weight models are gaining enterprise traction due to cost, control, and privacy.
Evaluate whether an open-weight model like Muse Spark 1.3 could replace a proprietary model in your pipeline this quarter.
Near-frontier models offer a sweet spot for industries that don't need the absolute best answer.
Identify tasks in your organization where a 5-10% performance drop is acceptable in exchange for 50% cost savings.
Transkript und Insights werden KI-generiert und können Fehler enthalten. Die Genauigkeit hängt von der Audioqualität und der Deutlichkeit der Sprecher ab — bei Unklarheiten ist das Originalaudio die maßgebliche Quelle.
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