What Is a “Good” $/Token for LLM Inference in 2026?

When teams start running large language model inference in production, one of the first questions they ask is deceptively simple:

What is a good cost per token?

It sounds like a pricing question. In reality, it is a systems question.

Most early cost estimates are built around GPU hourly rates or benchmarked tokens-per-second numbers. Those metrics are easy to compare and easy to reason about, but they rarely survive first contact with real traffic. Once latency targets, variable prompts, memory pressure, and uneven demand are introduced, the numbers that look clean on paper stop lining up with actual spend.

In 2026, cost per token is less about which GPU you rent and more about how effectively your inference stack converts GPU time into useful output.

Why $/Token Is the Right Metric and Why It’s Often Miscalculated

For inference-heavy workloads, $/token is more informative than $/GPU-hour because it reflects what the system actually produces. It implicitly captures:

• Effective batching behavior
• Idle and underutilized GPU time
• Memory fragmentation and KV cache pressure
• Scheduler and runtime efficiency

—all of which are invisible when looking only at infrastructure pricing.

The problem is that many teams calculate $/token using assumptions that only hold under ideal conditions.

Peak throughput benchmarks typically assume:

• Large, stable batch sizes
• Uniform sequence lengths
• Minimal latency constraints
• Warm caches and steady-state execution
  

Production systems rarely look like that.

Once user-facing latency SLAs are introduced, batching becomes constrained. Once real traffic arrives, prompt and output lengths vary widely. Once concurrency increases, KV cache memory accumulates and fragments VRAM. Each of these factors reduces effective throughput, even if the GPU itself is theoretically capable of much more.

At that point, the advertised tokens-per-second number becomes a ceiling, not an expectation.

Why GPU Hour Pricing Is a Poor Proxy for Inference Cost

Two teams can pay the same amount per GPU hour and end up with very different costs per token.

The difference usually has little to do with hardware specs and everything to do with utilization under real workload conditions.

A fast GPU that spends large portions of the day:

• waiting for requests,
• running at suboptimal batch sizes, or
• constrained by memory rather than compute
• is expensive regardless of its peak performance

In practice, most inference stacks are constrained by a combination of:

• latency targets (p50 / p95 / p99)
• batching limits
• and memory behavior (especially KV cache growth)

GPUs are provisioned for peak demand but operate below peak utilization for most of the day. That unused capacity dominates cost.

This is why “cheaper” GPUs sometimes produce higher $/token outcomes than more expensive ones, and why simply switching hardware rarely fixes inference economics on its own.

What $/Token Looks Like in Practice

In practice, cost per token for LLM inference can vary by order of magnitude even for the same model and hardware class. Teams running similar setups commonly see 5–10× differences in $/token depending on utilization, batching effectiveness, latency constraints, and memory behavior.

Well-utilized deployments with stable traffic and effective batching may operate in single-digit microdollars per token, while deployments provisioned primarily for peak demand or constrained by tight latency requirements can drift into tens of microdollars per token.

These figures are intentionally directional rather than prescriptive, and are meant to illustrate how sensitive inference cost is to workload shape and system behavior, not to serve as a universal benchmark.

Batching Helps, Until Latency Pushes Back

Batching is the most powerful lever for lowering inference cost, but it is also one of the most misunderstood.

In controlled environments, larger batches dramatically improve efficiency. In production, batching is bounded by how long users are willing to wait.
Once batching delays push responses past acceptable latency thresholds, systems are forced to reduce batch size or execute requests individually. At that point, throughput collapses relative to benchmark expectations.

This is why many teams see excellent cost efficiency during load testing and disappointing results once the system goes live. The workload changes, but the cost model does not.

Memory Pressure Is Often the Real Bottleneck

Inference cost models frequently focus on compute, but memory is often the first limiting factor.

As concurrency increases and sequence lengths grow, KV cache memory accumulates. This reduces the amount of available VRAM for batching and can trigger out-of-memory conditions long before compute capacity is saturated. When that happens, teams are forced to scale out to additional GPUs or artificially cap concurrency, both of which increase cost per token.

This behavior is well documented in modern inference frameworks such as vLLM and SGLang, but it is easy to miss when cost calculations are based primarily on peak compute throughput rather than runtime behavior.

What “Good” $/Token Actually Means in Practice

There is no single $/token target that applies universally. What is considered “good” depends on model size/archtecture, latency requirements, traffic shape, and tolerance for variability.

What is consistent is that teams with well-optimized inference stacks tend to cluster within predictable ranges for a given workload, while teams with high costs almost always share the same underlying issues:

• low utilization
• overprovisioning for peaks
• memory constraints that prevent effective batching

The important takeaway is that $/token is an outcome, not a configuration. It reflects how well the system aligns capacity with real demand, not how powerful the hardware is in isolation.

The More Useful Question to Ask

Rather than asking, “What is a good $/token?”
A more useful question is:
What is preventing our system from turning GPU time into tokens efficiently?

That question leads teams to examine:

• scheduling and admission control
• traffic patterns and burstiness
• KV cache behavior and memory fragmentation
• deployment strategy across regions and replicas

In production inference, cost efficiency comes from utilization and orchestration, not from peak benchmarks.

Closing Thoughts

In 2026, inference cost is dominated by how systems behave under real workloads. The teams that achieve consistently low cost per token are not necessarily running the fastest GPUs.

They are running the most efficient systems.

Understanding that distinction is the first step toward building inference infrastructure that scales economically instead of just technically.

Further reading

• NVIDIA CUDA Programming Guide (memory and execution model)
• vLLM runtime architecture documentation
• Hugging Face Text Generation Inference (TGI) docs