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Anthropic

New Claude models consume more tokens but cost less per solved task

Written by an AI model from the linked sources and checked against them by a second model. How we work

All about: Claude prices and limits →
Image: signoz.io

A benchmark of 10 Terminal-Bench tasks run through Claude Code across Sonnet 4.6, Opus 4.7, and Opus 4.8 shows a shift in efficiency. Each run was instrumented using OpenTelemetry and SigNoz.

In terms of accuracy, Sonnet solved 5 tasks, Opus 4.7 solved 7, and Opus 4.8 solved 8. While Sonnet was the cheapest in raw cost at $6.30, Opus 4.8 proved cheaper than Opus 4.7 at $8.06 versus $8.51.

The critical metric is cost per solved task: $1.26 for Sonnet, $1.22 for Opus 4.7, and $1.01 for Opus 4.8. Opus 4.8 consumed the most tokens at 6.06 million compared to 2.88 million for Sonnet, but nearly all were cache reads, the least expensive category.

Tool error rates dropped from 1.72% for Sonnet to 0.85% for Opus 4.8. Sonnet failures were primarily timeouts, while the single Opus 4.8 failure was a fast, incorrect answer on a complex task.

Open models

Empero releases Qwythos-9B trained on Claude traces

Empero released Qwythos-9B, a Qwen3.5-9B variant fine-tuned on 500 million tokens of Claude Mythos and Fable traces. It gains +34 points on MMLU over the base model and supports a 1,048,576-token context window via YaRN scaling. The model is uncensored and optimized for specialized fields like cybersecurity and pharmacology.

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