A key new feature in the Opus 4.6 API is "Adaptive Thinking," which lets developers specify the level of effort the model applies to a task. Setting the effort to 'max' forces the model to think without constraints on depth, a powerful but resource-intensive option exclusive to the new version.

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.

Progress in complex, long-running agentic tasks is better measured by tokens consumed rather than raw time. Improving token efficiency, as seen from GPT-5 to 5.1, directly enables more tool calls and actions within a feasible operational budget, unlocking greater capabilities.

Many developers are failing to access key new features like "Agent Teams" in Anthropic's Opus 4.6. The issue is often a simple configuration oversight. You must manually enable experimental features in your settings.json file and ensure your packages are updated to leverage the model's full capabilities.

A paradox exists where the cost for a fixed level of AI capability (e.g., GPT-4 level) has dropped 100-1000x. However, overall enterprise spend is increasing because applications now use frontier models with massive contexts and multi-step agentic workflows, creating huge multipliers on token usage that drive up total costs.

Effective prompting requires adapting your language to the AI's core design. For Anthropic's agent-based Opus 4.6, the optimal prompt is to "create an agent team" with defined roles. For OpenAI's monolithic Codex 5.3, the equivalent prompt is to instruct it to "think deeply" about those same roles itself.

In a head-to-head test to build a Polymarket clone, Anthropic's Opus 4.6 produced a visually polished, feature-rich app. OpenAI's Codex 5.3 was faster but delivered a basic MVP that required multiple design revisions. The multi-agent "research first" approach of Opus resulted in a superior initial product.

While the cost to achieve a fixed capability level (e.g., GPT-4 at launch) has dropped over 100x, overall enterprise spending is increasing. This paradox is explained by powerful multipliers: demand for frontier models, longer reasoning chains, and multi-step agentic workflows that consume exponentially more tokens.

While the cost for GPT-4 level intelligence has dropped over 100x, total enterprise AI spend is rising. This is driven by multipliers: using larger frontier models for harder tasks, reasoning-heavy workflows that consume more tokens, and complex, multi-turn agentic systems.