Major LLM API pricing models
LLM APIs today charge through three main structures. Per-token pricing bills separately for input and output tokens—the dominant model used by OpenAI, Anthropic, and Google. Per-request pricing charges a flat fee per API call regardless of length, which simplifies budgeting but penalizes short interactions. Subscription or commitment tiers bundle a monthly token allowance at a discount, requiring upfront volume commitments.
In practice, most production workloads land on per-token billing. The critical variable is the spread between input and output rates: output tokens from flagship models like GPT-4o or Claude Opus can cost 3-5x more than input tokens, making generation-heavy workflows disproportionately expensive.
Why flagship-only pricing does not scale
Running every API call through a single flagship model is the simplest architecture—and the most expensive one. Teams that route all traffic to Claude Opus or GPT-4o typically see monthly bills climb faster than usage, because not every request requires frontier-grade reasoning.
Consider a typical SaaS product: user-facing chat completions need high-quality responses, but background tasks like summarization, classification, embedding generation, and draft assembly can tolerate lighter models. Sending all of these through a flagship endpoint means paying premium rates for work that a value-tier model handles equally well. Understanding how LLM tokens work is the first step toward identifying where you are overspending.
The hybrid approach: blending tiers for real savings
Hybrid token architectures solve the scaling problem by splitting traffic across model tiers. A-tier (premium-path) tokens handle the moments that define user experience—first replies, tool calls, complex reasoning. Value-tier tokens cover bulk workloads where latency and nuance matter less.
The routing decision happens at the API gateway level, not in your application code. With an OpenAI-compatible interface, your existing integration stays unchanged while the policy layer assigns each request to the most cost-effective model that meets its quality threshold.
Teams using blended routing typically report 40-60% cost reductions on total LLM spend while maintaining the same user-perceived quality on critical paths. The savings compound as volume grows, because the percentage of bulk-eligible traffic tends to increase with scale.
Cost optimization strategies beyond model selection
Model routing is the highest-leverage optimization, but several complementary tactics push costs further down. Prompt compression reduces input token counts by trimming verbose system prompts and deduplicating context across turns. Response capping sets maximum output lengths per route, preventing runaway generation on open-ended completions. Caching and deduplication serve identical or near-identical requests from cache rather than recomputing them.
For a deeper dive into these techniques, see our LLM cost optimization guide. The key insight: pricing comparison is not just about sticker rates—it is about effective cost per useful output, which depends on your architecture as much as your provider.
Choosing the right pricing model for your team
If your workload is dominated by user-facing chat, prioritize low output-token rates and consider hybrid routing to offload background processing. If you run heavy batch pipelines, look for volume discounts or commitment tiers on value models. If you need predictable budgets, a blended token package with explicit tier allocation removes the guesswork.
Token Landing packages tokens as an explicit blend—you see exactly how many A-tier and value-tier tokens you are buying. No hidden model swaps, no surprise bills when traffic spikes hit premium endpoints.