Top 5 Open-Source AI Model API Providers

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# Introduction

 
Open‑weight models have transformed the economics of AI. Today, developers can deploy powerful models such as Kimi, DeepSeek, Qwen, MiniMax, and GPT‑OSS locally, running them entirely on their own infrastructure and retaining full control over their systems.

However, this freedom comes with a significant trade‑off. Operating state‑of‑the‑art open‑weight models typically requires enormous hardware resources, often hundreds of gigabytes of GPU memory (around 500 GB), almost the same amount of system RAM, and top‑of‑the‑line CPUs. These models are undeniably large, but they also deliver performance and output quality that increasingly rival proprietary alternatives.

This raises a practical question: how do most teams actually access these open‑source models? In reality, there are two viable paths. You can either rent high‑end GPU servers or access these models through specialized API providers that give you access to the models and charge you based on input and output tokens.

In this article, we evaluate the leading API providers for open‑weight models, comparing them across price, speed, latency, and accuracy. Our short analysis combines benchmark data from Artificial Analysis with live routing and performance data from OpenRouter, offering a grounded, real‑world perspective on which providers deliver the best results today.

# 1. Cerebras: Wafer Scale Speed for Open Models

 
Cerebras is built around a wafer scale architecture that replaces traditional multi GPU clusters with a single, extremely large chip. By keeping computation and memory on the same wafer, Cerebras removes many of the bandwidth and communication bottlenecks that slow down large model inference on GPU based systems.

This design enables exceptionally fast inference for large open models such as GPT OSS 120B. In real world benchmarks, Cerebras delivers near instant responses for long prompts while sustaining very high throughput, making it one of the fastest platforms available for serving large language models at scale.

Performance snapshot for the GPT OSS 120B model:

• Speed: approximately 2,988 tokens per second
• Latency: around 0.26 seconds for a 500 token generation
• Price: approximately 0.45 US dollars per million tokens
• GPQA x16 median: roughly 78 to 79 percent, placing it in the top performance band

Best for: High traffic SaaS platforms, agentic AI pipelines, and reasoning heavy applications that require ultra fast inference and scalable deployment without the complexity of managing large multi GPU clusters.

# 2. Together.ai: High Throughput and Reliable Scaling

 
Together AI provides one of the most reliable GPU based deployments for large open weight models such as GPT OSS 120B. Built on a scalable GPU infrastructure, Together AI is widely used as a default provider for open models due to its consistent uptime, predictable performance, and competitive pricing across production workloads.

The platform focuses on balancing speed, cost, and reliability rather than pushing extreme hardware specialization. This makes it a strong choice for teams that want dependable inference at scale without locking into premium or experimental infrastructure. Together AI is commonly used behind routing layers such as OpenRouter, where it consistently performs well across availability and latency metrics.

Performance snapshot for the GPT OSS 120B model:

• Speed: approximately 917 tokens per second
• Latency: around 0.78 seconds
• Price: approximately 0.26 US dollars per million tokens
• GPQA x16 median: roughly 78 percent, placing it in the top performance band

Best for: Production applications that need strong and consistent throughput, reliable scaling, and cost efficiency without paying for specialized hardware platforms.

# 3. Fireworks AI: Lowest Latency and Reasoning-First Design

 
Fireworks AI provides a highly optimized inference platform focused on low latency and strong reasoning performance for open-weight models. The company’s inference cloud is built to serve popular open models with enhanced throughput and reduced latency compared to many standard GPU stacks, using infrastructure and software optimizations that accelerate execution across workloads. 

The platform emphasizes speed and responsiveness with a developer-friendly API, making it suitable for interactive applications where quick answers and smooth user experiences matter.

Performance snapshot for the GPT-OSS-120B model:

• Speed: approximately 747 tokens per second
• Latency: around 0.17 seconds (lowest among peers)
• Price: approximately 0.26 US dollars per million tokens
• GPQA x16 median: roughly 78 to 79 percent (top band)

Best for: Interactive assistants and agentic workflows where responsiveness and snappy user experiences are critical.

# 4. Groq: Custom Hardware for Real-Time Agents

 
Groq builds purpose-built hardware and software around its Language Processing Unit (LPU) to accelerate AI inference. The LPU is designed specifically for running large language models at scale with predictable performance and very low latency, making it ideal for real-time applications. 

Groq’s architecture achieves this by integrating high speed on-chip memory and deterministic execution that reduces the bottlenecks found in traditional GPU inference stacks. This approach has enabled Groq to appear at the top of independent benchmark lists for throughput and latency on generative AI workloads.

Performance snapshot for the GPT-OSS-120B model:

• Speed: approximately 456 tokens per second
• Latency: around 0.19 seconds
• Price: approximately 0.26 US dollars per million tokens
• GPQA x16 median: roughly 78 percent, placing it in the top performance band

Best for: Ultra-low-latency streaming, real-time copilots, and high-frequency agent calls where every millisecond of response time counts.

# 5. Clarifai: Enterprise Orchestration and Cost Efficiency

 
Clarifai offers a hybrid cloud AI orchestration platform that lets you deploy open weight models on public cloud, private cloud, or on-premise infrastructure with a unified control plane. 

Its compute orchestration layer balances performance, scaling, and cost through techniques such as autoscaling, GPU fractioning, and efficient resource utilization. 

This approach helps enterprises reduce inference costs while maintaining high throughput and low latency across production workloads. Clarifai consistently appears in independent benchmarks as one of the most cost-efficient and balanced providers for GPT-level inference.

Performance snapshot for the GPT-OSS-120B model:

• Speed: approximately 313 tokens per second
• Latency: around 0.27 seconds
• Price: approximately 0.16 US dollars per million tokens
• GPQA x16 median: roughly 78 percent, placing it in the top performance band

Best for: Enterprises needing hybrid deployment, orchestration across cloud and on-premise, and cost-controlled scaling for open models.

# Bonus: DeepInfra

 
DeepInfra is a cost-efficient AI inference platform that offers a simple and scalable API for deploying large language models and other machine learning workloads. The service handles infrastructure, scaling, and monitoring so developers can focus on building applications without managing hardware. DeepInfra supports many popular models and provides OpenAI-compatible API endpoints with both regular and streaming inference options.

While DeepInfra’s pricing is among the lowest in the market and attractive for experimentation and budget-sensitive projects, routing networks such as OpenRouter report that it can show weaker reliability or lower uptime for certain model endpoints compared to other providers.

Performance snapshot for the GPT-OSS-120B model:

• Speed: approximately 79 to 258 tokens per second
• Latency: approximately 0.23 to 1.27 seconds
• Price: approximately 0.10 US dollars per million tokens
• GPQA x16 median: roughly 78 percent, placing it in the top performance band

Best for: Batch inference or non-critical workloads paired with fallback providers where cost efficiency is more important than peak reliability.

# Summary Table

 
This table compares the leading open-source model API providers across speed, latency, cost, reliability, and ideal use cases to help you choose the right platform for your workload.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.