Fable 5's advanced reasoning comes at a steep cost, consuming tokens and rate limits at twice the speed of previous models. This is presented as an intentional design choice, forcing users to strategically decide if a task's complexity justifies the significant increase in operational expense.

The true breakthrough of Fable 5 isn't just better benchmarks, but its ability to complete complex projects like building a full mobile app or redesigning a website from a single, high-level prompt. This "one-shot" capability for what were previously multi-day or multi-week tasks represents a paradigm shift in AI-driven development.

It's counterintuitive, but using a more expensive, intelligent model like Opus 4.5 can be cheaper than smaller models. Because the smarter model is more efficient and requires fewer interactions to solve a problem, it ends up using fewer tokens overall, offsetting its higher per-token price.

When evaluating AI agents, the total cost of task completion is what matters. A model with a higher per-token cost can be more economical if it resolves a user's query in fewer turns than a cheaper, less capable model. This makes "number of turns" a primary efficiency metric.

The binary distinction between "reasoning" and "non-reasoning" models is becoming obsolete. The more critical metric is now "token efficiency"—a model's ability to use more tokens only when a task's difficulty requires it. This dynamic token usage is a key differentiator for cost and performance.

OpenAI's GPT-5.5 is more expensive per token, but a new evaluation framework is emerging. The key metric isn't raw cost, but the model's efficiency in solving a problem. This 'intelligence per dollar' reframes cost analysis around performance and compute, where more expensive models can be cheaper overall if they solve tasks more efficiently.

A model with a low per-token price can be more expensive if it's inefficient, verbose, or requires multiple attempts ('overthinking'). The actual invoice depends on the total tokens needed to complete a task, making token efficiency a hidden multiplier that savvy enterprises are now tracking to determine the true cost.

In complex, multi-step tasks, overall cost is determined by tokens per turn and the total number of turns. A more intelligent, expensive model can be cheaper overall if it solves a problem in two turns, while a cheaper model might take ten turns, accumulating higher total costs. Future benchmarks must measure this turn efficiency.

A cost-effective AI strategy involves using a powerful, expensive model once to solve a complex task, then using a system like M0 to distill that solution into reusable "experience" and "skill" records. Cheaper models can then leverage this pre-packaged knowledge to execute the same task with higher success rates and significantly lower token costs.