OpenAI Halves Inference Costs, Accelerating Path to Gross Margin Improvement
Alina Collins
OpenAI engineers have found a new optimization that slashes inference costs by more than half — putting the company's year-end 52% gross-margin target within reach and ratcheting up pricing pressure on rival Anthropic.
How did they cut inference costs in half?
OpenAI engineers disclosed internally this month a new approach that reduces inference — the computation a model performs each time it answers a query — costs by more than half.
The company has not detailed the methods, but likely candidates include quantization (compressing model parameters to lower numerical precision to save compute), key-value caching (reusing prior computation so the model doesn't repeat work), batching queries, and routing simpler requests to smaller sub-models.
In plain terms = imagine a restaurant that swaps to more efficient burners, pre-preps ingredients, and hands simple dishes to a sous chef — each saves a bit, and together costs drop by half.
Can gross margin really go from 39% to 52%?
Inference cost is the core variable in OpenAI's gross margin — every query burns GPU time, and that expense eats directly into margin.
As of Q1, OpenAI's gross margin stood at 39%, up from 33% a year earlier but still short of its year-end 52% target. This means → the company needs to average roughly 56% over the rest of the year to hit the full-year goal — a significant gap.
Cutting inference costs by half directly narrows that gap: same revenue, far less GPU spend, and margin climbs accordingly.
Will the savings go to users or to the bottom line?
OpenAI faces a choice: channel the savings into user benefits — higher query quotas for ChatGPT subscribers, lower API prices for developers — or book them as margin improvement.
This means → if OpenAI chooses to cut prices, Anthropic takes the hit first. Anthropic has already drawn market criticism for relatively high model pricing; an OpenAI price cut would widen the gap further.
This reflects a new phase in the AI industry: cost-optimization capability is becoming a competitive weapon, not just a technical metric.
How long can this advantage last?
Inference optimization is not unique to OpenAI. Anthropic CEO Dario Amodei has spoken publicly about "compute multipliers" since at least mid-2023 and has said the company deliberately limits who internally knows specific multiplier details — to prevent competitors from replicating them.
The uncertainty: larger next-generation models may erode this round of gains. OpenAI plans to release bigger models later this year, and bigger models typically cost more to run — current optimizations may lose some of their punch.
OpenAI is also developing a custom inference chip with Broadcom, aiming to reduce GPU dependence at the hardware level. In plain terms = the software layer just saved half; the hardware layer aims to save again — two tracks running in parallel.
Does the $1.8 billion chip deal still feel as urgent?
OpenAI is pursuing an $1.8 billion financing deal to co-develop a dedicated inference chip with Broadcom.
This means → the software breakthrough may reduce the urgency of that deal — if software alone can halve inference costs, the "must have hardware yesterday" pressure eases somewhat.
But the flip side holds: software gains may fade as models grow larger, making custom silicon the key long-term cost lever. The market's next focus will be whether the pace of that financing changes.
Content is for reference only, not financial advice.