Top LLM Inference Providers Compared - GPT-OSS-120B | Clarifai Blog

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TL;DR

In this post, we explore how leading inference providers perform on the GPT-OSS-120B model using benchmarks from Artificial Analysis. You will learn what matters most when evaluating inference platforms including throughput, time to first token, and cost efficiency. We compare Vertex AI, Azure, AWS, Databricks, Clarifai, Together AI, Fireworks, Nebius, CompactifAI, and Hyperbolic on their performance and deployment efficiency.

Introduction

Large language models (LLMs) like GPT-OSS-120B, an open-weight 120-billion-parameter mixture-of-experts model, are designed for advanced reasoning and multi-step generation. Reasoning workloads consume tokens rapidly and place high demands on compute, so deploying these models in production requires inference infrastructure that delivers low latency, high throughput, and lower cost.

Differences in hardware, software optimizations, and resource allocation strategies can lead to large variations in latency, efficiency, and cost. These differences directly affect real-world applications such as reasoning agents, document understanding systems, or copilots, where even small delays can impact overall responsiveness and throughput.

To evaluate these differences objectively, independent benchmarks have become essential. Instead of relying on internal performance claims, open and data-driven evaluations now offer a more transparent way to assess how different platforms perform under real workloads.

In this post, we compare leading GPU-based inference providers using the GPT-OSS-120B model as a reference benchmark. We examine how each platform performs across key inference metrics such as throughput, time to first token, and cost efficiency, and how these trade-offs impact performance and scalability for reasoning-heavy workloads.

Before diving into the results, let’s take a quick look at Artificial Analysis and how their benchmarking framework works.

Artificial Analysis Benchmarks

Artificial Analysis (AA) is an independent benchmarking initiative that runs standardized tests across inference providers to measure how models like GPT-OSS-120B perform in real conditions. Their evaluations focus on realistic workloads involving long contexts, streaming outputs, and reasoning-heavy prompts rather than short, synthetic samples.

You can explore the full GPT-OSS-120B benchmark results here.

Artificial Analysis evaluates a range of performance metrics, but here we focus on the three key factors that matter when choosing an inference platform for GPT-OSS-120B: time to first token, throughput, and cost per million tokens.

Benchmark Methodology

With this methodology in mind, we can now compare how different GPU-based platforms perform on GPT‑OSS‑120B and what these results imply for reasoning-heavy workloads.

Provider Comparison (GPT‑OSS‑120B)

Clarifai

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Google Vertex AI

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Microsoft Azure

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Hyperbolic

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AWS

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Databricks

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Together AI

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Fireworks AI

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CompactifAI

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Nebius Base

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Best Providers Based on Price and Throughput

Selecting the right inference provider for GPT‑OSS‑120B requires evaluating time to first token, throughput, and cost based on your workload. Platforms like Clarifai offer high throughput, low latency, and competitive cost, making them well-suited for reasoning-heavy or interactive tasks. Other providers, such as CompactifAI, prioritize lower cost but come with reduced throughput, which may be more suitable for cost-sensitive or batch-oriented workloads. The optimal choice depends on which trade-offs matter most for your applications.

Best for Price

Best for Throughput

Performance and Flexibility

Along with price and throughput, flexibility is critical for real-world workloads. Teams often need control over scaling behavior, GPU utilization, and deployment environments to manage cost and efficiency.

Clarifai, for example, supports fractional GPU utilization, autoscaling, and local runners — features that can improve efficiency and reduce infrastructure overhead.

These capabilities extend beyond GPT‑OSS‑120B. With the Clarifai Reasoning Engine, custom or open-weight reasoning models can run with consistent performance and reliability. The engine also adapts to workload patterns over time, gradually improving speed for repetitive tasks without sacrificing accuracy.

Benchmark Summary

So far, we’ve compared providers based on throughput, latency, and cost using the Artificial Analysis Benchmark. To see how these trade-offs play out in practice, here’s a visual summary of the results across the different providers. These charts are directly from Artificial Analysis.

The first chart highlights output speed vs price, while the second chart compares latency vs output speed.

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Output Speed vs. Price

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Latency vs. Output Speed

Below is a detailed comparison table summarizing the key metrics for GPT-OSS-120B inference across providers.

Provider Throughput (tokens/s) Time to First Token (s) Blended Cost ($ / 1M tokens)
Clarifai 544 0.32 0.16
Google Vertex AI 392 0.40 0.26
Microsoft Azure 348 0.48 0.26
Hyperbolic 395 0.52 0.30
AWS 252 0.64 0.26
Databricks 195 0.36 0.26
Together AI 248 0.25 0.26
Fireworks AI 482 0.44 0.26
CompactifAI 186 0.29 0.10
Nebius Base 165 0.66 0.26

Conclusion

Choosing an inference provider for GPT‑OSS‑120B involves balancing throughput, latency, and cost. Each provider handles these trade-offs differently, and the best choice depends on the specific workload and performance requirements.

Providers with high throughput excel at reasoning-heavy or interactive tasks, while those with lower median throughput may be more suitable for batch or background processing where speed is less critical. Latency also plays a key role: low time-to-first-token improves responsiveness for real-time applications, whereas slightly higher latency may be acceptable for less time-sensitive tasks.

Cost considerations remain important. Some providers offer strong performance at low blended costs, while others trade efficiency for price. Benchmarks covering throughput, time to first token, and blended cost provide a clear basis for understanding these trade-offs.

Ultimately, the right provider depends on the engineering problem, workload characteristics, and which trade-offs matter most for the application.