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Anthropic's new Claude Opus 4.7 crushes performance benchmarks but costs more to run than previous versions.
- Token efficiency collapsed. The model consumes significantly more tokens per task, inflating inference costs for anyone deploying at scale.
- Performance gains don't offset operational overhead. Better outputs mean nothing if your infrastructure bill doubles.
- Enterprise AI economics just shifted. Teams choosing between raw capability and unit economics now face a harder calculus.
- This exposes the cost disease in AI. Larger models deliver diminishing returns on accuracy while multiplying compute expenses.
- Token pricing becomes the actual moat. Companies betting on AI infrastructure need cheap inference, not just smart models.
- The efficiency question matters more than the model release. Anthropic proved it can build capability. Now it needs to prove it can build affordably.
https://decrypt.co/364621/claude-opus-47-review-benchmarks-coding-test