While faster model versions like Opus 4.6 Fast offer significant speed improvements, they come at a steep cost—six times the price of the standard model. This creates a new strategic layer for developers, who must now consciously decide which tasks justify the high expense to avoid unexpectedly large bills.

The cost to run an autonomous AI coding agent is surprisingly low, reframing the value of developer time. A single coding iteration can cost as little as $3, meaning a complete feature built over 10 iterations could be completed for around $30, making complex software development radically more accessible.

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.

The release of models like Sonnet 4.6 shows that the industry is moving beyond singular 'state-of-the-art' benchmarks. The conversation now focuses on a more practical, multi-factor evaluation. Teams now analyze a model's specific capabilities, cost, and context window performance to determine its value for discrete tasks like agentic workflows, rather than just its raw intelligence.

The new multi-agent architecture in Opus 4.6, while powerful, dramatically increases token consumption. Each agent runs its own process, multiplying token usage for a single prompt. This is a savvy business strategy, as the model's most advanced feature is also its most lucrative for Anthropic.

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.

The high operational cost of using proprietary LLMs creates 'token junkies' who burn through cash rapidly. This intense cost pressure is a primary driver for power users to adopt cheaper, local, open-source models they can run on their own hardware, creating a distinct market segment.

Tasklet's CEO points to pricing as the ultimate proof of an LLM's value. Despite GPT-4o being cheaper, Anthropic's Sonnet maintains a higher price, indicating customers pay a premium for its superior performance on multi-turn agentic tasks—a value not fully captured by benchmarks.

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.