At maximum effort settings, Artificial Analysis placed Sonnet 5.5 at a score of 56 on its Intelligence Index, trailing only Opus 5.5 at 58. The middle-tier model nearly matched Opus on office evaluation workloads such as GDPval-AA and AA-Briefcase, and scored 71% compared to Opus’s 70% on AutomationBench-AA. On Terminal-Bench 4.0, a coding test, Artificial Analysis measured 64% for Sonnet, outperforming both Opus and GPT-6 Astra at roughly 60%. Those benchmark figures remain provisional, however, as testers noted a structured output bug in the prerelease deployment and scheduled reruns.
Cost, Speed, and Operational Trade-Offs
Anthropic reports that Sonnet 5.5 delivers output at least 30% faster than Sonnet 5, with task costs reduced by up to 30%. Input and output token rates for Sonnet 5.5 are half those of Opus 5.5, which itself arrived with a 20% price reduction across input and output tokens. Yet, real-world deployment costs depend heavily on token consumption. Artificial Analysis measured maximum-effort tasks at about $7.60, roughly 50% higher than Sonnet 5, illustrating that maximum reasoning effort increases token overhead.
Extensive testing by The New Stack comparing Opus 5.5 and competitor Fable 5.1 highlights the nuanced trade-offs developers face between reasoning depth and execution shortcuts. While Opus models excel at rigorous multi-step tasks, their tendency to overthink can drive up token counts and wall-clock times. Conversely, faster models risk bypassing critical integration checks to deliver quicker completions. Sonnet 5.5 introduces new cyber safeguards capable of routing high-risk requests back to Sonnet 5, requiring engineering teams to audit how automated fallbacks influence application behavior.
Strategic Timing Ahead of Industry Events
The dual September releases from Anthropic arrived just as OpenAI prepared to open its DevDay keynote. With pricing structures shifting and middle-tier models narrowing performance gaps, engineering teams are forced to weigh completion rates, billed tokens, latency, and human review requirements before projecting infrastructure savings. As benchmark providers complete scheduled reruns and competitor platforms announce updates, developers must continuously benchmark models against specific production workloads rather than relying solely on static marketing scores.