Anthropic releases Opus 5 and focuses on token efficiency

Anthropic has just rolled out Opus 5, the newest update for the Artificial Intelligence (AI) model that has recently become a popular choice for coding and other software development tasks, among other things. While this is a noteworthy bump for Opus, it doesn’t seem to be an Opus 4.5-level breakthrough in agentic coding performance.

A chart (made by Anthropic) with various benchmarks like Frontier-Bench and DeepSWE shows Opus 5 performing at about the same level or slightly ahead of the much-ballyhooed capabilities of Anthropic’s Fable model for coding tasks. It ostensibly beats Opus 4.8 and OpenAI’s competing GPT-5.6-Sol in just about every kind of task.

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The various benchmarks show an iterative increase in performance, but not a radical leap, while the main pitch is that it’s a model that offers something just shy of Fable at approximately half the cost.

It’s also worth mentioning that Anthropic specifically avoided giving Opus 5 cutting-edge training on cyber security tasks, so it lags way behind Fable and Mythos in that regard. Anthropic claims it is relatively good at finding cybersecurity vulnerabilities, but because of decisions made in training the model, it is “substantially behind Mythos 5 on the exploitation of those vulnerabilities.”

As such, Opus 5 doesn’t have all of the same controversial protections that Fable had, such as the policy of keeping data for review for 30 days in case of an incident. But this is really a cost story. From one model update to the next subsequent one, we’ve mostly been seeing modest performance improvements - though they look far more dramatic when you look at a whole year of updates. The discourse among software developers and engineering managers right now is mostly around dealing with cost, and there’s real movement in open-weight models, local models, and other alternatives to cutting-edge frontier models.

Opus 5 sits at $5 per million input tokens, or $25 per million output tokens - on par with its predecessor, but cheaper than Fable. That said, the recently announced Chinese open-weight model Kimi K3 comes in at just $15 per million output tokens at similar performance, so competition is fierce.

Lately, companies like Cursor and Meta have been building “Model Routers,” systems that automatically select models of varying size and capability from an array of options based on the nature of the prompt. The idea is that you save a lot of tokens, and therefore compute and or money, by not using something like Fable for every task.

Companies like Anthropic are going to have to keep bringing token costs down, or at least (as is the case here) offering more performance for no additional cost, to continue to see the growth they’ve seen. Otherwise, they will start seeing reduced usage as people turn to smaller or open models once those models get good enough to handle the less challenging development tasks.