Opus 5.5
Anthropic's flagship large language model, built on a Mixture-of-Experts (MoE) architecture. It is designed for high-performance reasoning and multimodal tasks, and is positioned as a competitive alternative to top-tier models at a lower price point.
Core Facts
Opus 5.5 reaches the level of Fable 5.1 on most tasks, but with approximately 40% lower operating costs and faster speed.
On Terminal-Bench 4.0, Claude Opus 5.5 leads with 66.4%, followed by GPT-6 Astra at 61.8% and Argon at 57.4%.
Longer inference time does not always yield better results; models may fall into the trap of overthinking, and what truly determines output quality is the model's foundational capabilities and degree of alignment.
Timeline (last 90 days)
Jacob said that it was Fable 5 (and later 5.1) that made Spawn's degree of autonomy possible; Opus 5.5 was not sufficient for them.
After the user migrated the conversation to Opus 5.5, Opus 5.5 pointed out five major weaknesses in the previous plan, the first of which was "it will destroy your data."
Opus 5.5 is not an official model name publicly released by Anthropic; in the community it is generally used to refer to a version that is stronger and reasons more deeply than the default Claude.
The gap between different models on "logical consistency checks" comes mainly from reasoning depth and tool-calling strategy: stronger models first build a mapping between the device inventory and the target state before generating commands, then check the object each command acts on one by one; weaker models tend to fill in step-by-step templates and are prone to errors in the correspondence of quantities.
The video creator estimates Gemini 4 is about 70% cheaper than Opus 5.5.
Copilot Fusion's score on the coding agent index approaches Opus 5.5 Max, while cutting cost in half and reducing task time by 40%.
Google calls Gemini 4 Carbon a "new frontier model" and emphasizes its independent evaluation performance.
The uploader's own testing showed that when the prompts are written in great detail, the animations generated by DeepSeek and Opus 5.5 are almost identical.
Both Opus 5.5 and DeepSeek can directly output runnable front-end code (typically HTML/CSS/JavaScript or Canvas/WebGL animations) from natural-language prompts, and the more specific the prompt, the more stable the generated result.
After the blogger used Opus 5.5 to generate detailed prompts and handed them to DeepSeek in high-thinking mode, a PDF reading and annotation application called Folio was built from scratch, with a tech stack of Electron, React, TypeScript, and pdf.js.
40 more timeline events
All Facts (20)
Opus 5.5 reaches the level of Fable 5.1 on most tasks, but with approximately 40% lower operating costs and faster speed.
80%VerifiedOn Terminal-Bench 4.0, Claude Opus 5.5 leads with 66.4%, followed by GPT-6 Astra at 61.8% and Argon at 57.4%.
75%VerifiedLonger inference time does not always yield better results; models may fall into the trap of overthinking, and what truly determines output quality is the model's foundational capabilities and degree of alignment.
75%VerifiedOpus 5.5 scored 66 on the Terminal agent benchmark, higher than Sonnet's 55 and Astra's 57.9.
70%VerifiedAnthropic states that Opus 5.5 can reduce total costs for typical tasks by approximately 40% and improve output speed by more than 30%.
70%VerifiedClaude Opus 5.5 generates output more than 30% faster than Opus 5.
70%VerifiedClaude Duet is powered by Claude Opus 5.5.
65%VerifiedMistral Large 4 narrowly outperformed GPT-6 Astra on visual grounding tasks with a score of 42% versus 41%.
65%VerifiedHaiku 5.5 can work as a sub-agent alongside Claude Opus 5.5 and Sonnet 5.5 to complete programming tasks.
65%VerifiedIn the deep audit benchmark, Sonnet 5.5's cost is approximately half that of Opus, its score is slightly higher than Opus, and it takes about 5 minutes (roughly half the time Opus takes).
65%VerifiedArgon achieved a score of 77.9% on the DeepSWE benchmark, setting a new record
65%VerifiedOpus 5.5's release page directly uses GPT-6's Astra as its comparison target.
65%VerifiedGPT-6 Astra is notably different from previous model iterations, being powerful in certain respects while showing little improvement or even appearing to regress in other scenarios.
65%VerifiedThe cache read mechanism retains the KV cache for recurring long prompt prefixes, charging at an extremely low rate upon a cache hit; the leaked cache read price for Opus 5.5 is approximately 1/20 of the input price.
65%VerifiedThe core challenges of long-running Agents include tasks easily drifting from goals, errors cascading and amplifying, and resource consumption being difficult to estimate.
65%VerifiedOpus 5 is the flagship model of Anthropic's Claude series, known for its longer context window (capable of handling codebases of hundreds of thousands of tokens) and more cautious output style.
65%VerifiedEvaluating the per-task cost of large models requires considering both the token unit price and the number of tokens consumed to complete the task; the output token consumption of chain-of-thought reasoning models may increase by several times or even dozens of times.
65%VerifiedThe robustness of tool calls often determines an agent's success rate in real production tasks more than the model's own parameter scale.
65%VerifiedGPT-6 Sol and Anthropic's Opus 5.5 were released on the same day.
70%VerifiedAnthropic released Opus 5.5 at the same point in time.
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