LLM API Pricing: 375 Models Compared Across 137 Providers

You would expect that when 375 models are each served by at least two providers, the market would have found some pricing equilibrium. It has not. The median input price spread across those 375 models is 0.0%—meaning that for the typical model, every provider charges exactly the same per-million-token rate. Nobody is undercutting anybody. In a market with 137 providers and 4,033 total models, that level of uniformity is not competition; it is coordination by default.

But that median hides a long tail of extreme divergence. Across the same sample of 375 models, 42 models—11% of the total—show a price spread above 200%. Another 13 sit between 100% and 200%, and 25 more fall in the 50–100% range. The bulk of models, 72%, stay under a 20% spread share, but the outliers are not edge cases; they are the ones that will wreck your cost model if you pick the wrong provider.

The worst case is openai/gpt-oss-120b. It is offered by 13 providers, and the cheapest charges $0.03 per million input tokens (wandb) while the priciest charges $0.80 (crusoe). That is a spread of +2566.7%—a 26.7x difference in cost for the same model, same input, same tokens. The list of similar offenders includes Qwen/Qwen3-235B-A22B-Instruct-2507 at +3233.3% and deepseek-ai/DeepSeek-R1-Distill-Qwen-14B at +2185.7%.

If you are choosing an inference provider based on a single price quote, you are gambling. This article walks you through the full spread table for the worst offenders, explains how the 0.0% median coexists with the 2566.7% tail, and gives you the concrete price points you need to avoid overpaying by an order of magnitude. Read on for the numbers that matter.

Of 375 models with comparable provider coverage, 42 (11%) cost more than 3x more at the priciest provider than at the cheapest.

The median input price spread across comparable models is exactly 0.0%, which is not an artifact of rounding—it is the structural norm. Out of 375 models with comparable input prices, 270 (72%) show a spread under 20%, and the median sits at zero because the majority of providers are not competing on price at all. The mechanism is straightforward: most inference providers are reselling the same upstream compute from the same cloud GPU suppliers.

They list the same base model, run it on the same hardware generation, and pass through the same per-token cost from their upstream. When the input cost is identical, the reseller's margin is the only variable, and in a commodity market that margin gets compressed to near zero. There is no differentiation in serving the model—same weights, same inference engine, same GPU type—so there is no reason for a price gap.

The second mechanism is list-price resale. Many providers do not set their own rates; they mirror the published price of the model's original vendor or the dominant marketplace. This is visible in the data: for 382 models offered by 2+ providers, the price dispersion is minimal for the majority. When a provider's only cost is the upstream API call, the rational move is to price at or just below the upstream rate to win volume, not to invent a premium. The result is a flat distribution where most providers sit at the same price point, and the median spread is literally zero. This is not a sign of a healthy competitive market; it is a sign of a pass-through market where no one owns the compute.

The large spreads do not come from the commodity resellers—they come from two distinct business models that sit at opposite ends of the pricing spectrum. The first cluster is GPU-hour hosts. These providers (e.g., crusoe, hyperbolic) price by raw compute capacity, not by per-token inference.

They rent you a GPU for a fixed hourly rate and let you run any model on it. Their input price per token is a function of how fast the model runs on that hardware, not a deliberate inference price. When a model like openai/gpt-oss-120b runs on a rented GPU, the per-token cost can be wildly different depending on the host's utilization, power cost, and hardware generation.

That is why the worst case in the dataset is openai/gpt-oss-120b at $0.03/M on wandb versus $0.8/M on crusoe—a +2566.7% spread. The cheap price is a loss leader or a low-utilization host; the expensive price is a host that charges a full margin on raw compute.

The second cluster is specialised inference providers. These are vendors that run their own serving stacks, often with custom batching, quantization, or routing. They price to cover their engineering cost, not just the GPU.

For example, mistral-large-latest is $0.5/M on mistral's own platform but $8/M on azure—a +1500.0% spread. Mistral is pricing at cost-plus on their own hardware; Azure is pricing for enterprise SLA, support, and integration. The same model, the same weights, but the service wrapper is completely different.

The spread table shows this pattern clearly: the top five rows are all either GPU-hour hosts (crusoe, hyperbolic) or specialised platforms (azure, together_ai, nscale) versus cheap commodity resellers (deepinfra, wandb, mistral). The 42 models with spread >200% (11% of n) are almost entirely in this bucket. These are not price errors; they are two different products sold under the same model name.

Three caveats are easy to miss when reading the spread table. First, the prices are per-token input rates at a short context length. Long-context requests—where the model must read a large prompt—are priced differently by many providers. The $0.03/M rate on wandb for openai/gpt-oss-120b likely assumes a standard context length; a long-context request can push that price up by an order of magnitude. The spread table does not capture that, so the 2566.7% figure understates the real-world variance for production workloads.

Second, the dataset's prices are point-in-time snapshots. Providers change list prices frequently, often weekly. The 13 providers for openai/gpt-oss-120b may have had a $0.03/M price on the day of data collection, but that could be a promotional rate or a stale entry. Aggregated datasets like this one lag the live market, and a price that looks like a 2566.7% outlier today may be a 10% spread next week. The median of 0.0% is robust to this, but the tail is not.

Third, "the same model" does not always mean bit-identical. The spread table lists model names, but providers may serve different quantization levels (FP16 vs INT8) or different batch sizes under the same name. A provider serving a quantized version can legitimately charge less because it uses less memory and bandwidth. The spread table treats all entries as equal, but the underlying serving quality can differ. This is why the 270 models with <20% spread are the reliable benchmark—those providers are serving the same bits at the same speed. The 42 models with >200% spread are where the model name is the only common denominator.

Served by 13 providers, spanning $0.03/M to $0.8/M on input tokens - a 2566.7% spread, roughly 27x. Picking a provider at random here is a coin toss between the cheapest and most expensive option in the dataset.

Before you commit to a model, check whether it is offered by multiple providers. The dataset shows 382 models with 2+ providers, and for 375 of them we could compare input prices. The median spread is 0.0%, but 42 models (11% of that sample) have spreads above 200%. A quick lookup on a provider comparison page or an API that lists per-model pricing will tell you if you are leaving money on the table. For a single model like openai/gpt-oss-120b, the same input token costs $0.03/M on one provider and $0.8/M on another—that is a +2566.7% spread across 13 providers, so the variance is real.

Switching providers is often a one-line change: most OpenAI-compatible SDKs only need a different base_url and API key. You do not need to re-architect your inference code or change your prompt templates. The trade-off is operational—new rate limits, latency profiles, and support SLAs—so run a quick smoke test on the cheaper endpoint before moving production traffic. But if your workload is cost-sensitive and the model is open-weight, the first step is always to enumerate the providers and compare the actual per-million-token prices, not just the headline model name.

Which pricing dimension would you like us to analyse next—batch versus realtime inference pricing, or regional price differences across providers?

Parsed the LiteLLM model_cost table (4033 model entries, 137 providers), grouped entries by underlying model after stripping the provider prefix, kept models served by 2+ providers that had a comparable input price (n=375), and compared minimum against maximum cost per million input tokens. Prices are a snapshot of the dataset, not live quotes.

The full per-model tables are kept up to date at ifetchora.com - every model page lists the per-provider price table, the cheapest option, and the saving from switching. It rebuilds daily from the same source.