Top 5 Open Source AI Model API Providers

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# Introduction

Open weight models have changed the economics of AI. Today, developers can deploy powerful models like Kimmy, Deepsec, Quen, Minimax, and GPT-SOS locally, running them entirely on their own infrastructure and maintaining full control over their systems.

However, this freedom comes with a caveat Trade-off. Operating state-of-the-art open-weight models typically require a lot of hardware resources, often hundreds of gigabytes of GPU memory (500 GB), roughly 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 quickly rivals proprietary alternatives.

This raises a practical question: How do most teams actually access these open source models? Actually, there are two viable ways. You can too Rent a high-end GPU Access servers or these models Specialized API providers It gives you access to the model and charges you based on input and output tokens.

In this article, we review and compare the leading API providers for open weight models Cost, speed, delay, And Correctness. Our brief analysis combines benchmark data from synthetic analysis with performance data from live routing and open routers, offering a fundamental, real-world perspective on which providers deliver today’s best results.

# 1. Cerebra: Wafer-scale acceleration for open models

Cerebra Built around a wafer-scale architecture that replaces traditional multi-GPU clusters with a single, extremely large chip. By keeping compute and memory on the same wafer, Cerberus removes many of the bandwidth and communication bottlenecks that reduce large model detection on GPU-based systems.

This design enables exceptionally fast decompression for large open models such as the GPTOSS 120B. In real-world benchmarks, Cerebros offers near-instant response for long cues while maintaining very high throughput, making it one of the fastest platforms available for serving large language models at scale.

Performance snapshot for GPTOSS 120B model:

• Speed: About 2,988 tokens per second
• delay: Around 0.26 seconds for 500 token generation
• Price: About 0.45 USD per million tokens
• GPQA X16 Median: Around 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 extremely fast and scalable deployments without the complexity of managing large multi-GPU clusters.

# 2. Together. AI: High throughput and reliable scaling

Together together Provides the most reliable GPU-based deployment for large open-weight models such as the GPTOSS 120B. Built on a scalable GPU infrastructure, Together AI is used as the default provider for open models due to its consistent uptime, predictable performance, and competitive pricing in production workloads.

The platform is focused on balancing speed, cost and reliability rather than pushing extreme hardware specializations. This makes it a strong choice for teams that want reliable estimation at scale without being locked into premium or experimental infrastructure. AI is commonly used behind routing layers like OpenRouter, where it consistently performs well in availability and latency metrics.

Performance snapshot for GPTOSS 120B model:

• Speed: About 917 tokens per second
• delay: About 0. 0.78 seconds
• Price: About 0.26 USD per million tokens
• GPQA X16 Median: Around 78%, placing it in the top performance band

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

# 3. Fireworks AI: Lowest latency and logic first design

Fireworks AI Provides a highly optimized intelligence platform focused on low latency and robust reasoning performance for open-weight models. The company’s Inference Cloud is built to serve popular open models with better throughput and lower latency than 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 responses and seamless user experiences matter.

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

• Speed: About 74 747 tokens per second
• delay: Around 0.17 seconds (lowest among peers)
• Price: About 0.26 USD per million tokens
• GPQA X16 Median: About 78 78 to 79 percent (top band)

Best for: Interactive assistants and agent workflows where responsiveness and fast user experiences are critical.

# 4. Guruq: Custom hardware for real-time agents

Groq AI develops purpose-built hardware and software around its language processing unit (LPU) to speed up diagnosis. The LPU is specifically designed to run large-scale language models 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 biased 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: About 45 456 tokens per second
• delay: About 0.19 seconds
• Price: About 0.26 USD per million tokens
• GPQA X16 Median: Around 78%, placing it in the top performance band

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

# 5. Clarify: Enterprise Orchestration and Cost Efficiency

Clarify 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 fractionation, and efficient resource utilization.

This approach helps enterprises reduce redundancy costs while maintaining high throughput and low latency in production workloads. Clarify consistently appears in independent benchmarks as the most cost-effective and balanced provider of GPT-level diagnostics.

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

• Speed: About 313 tokens per second
• delay: About 0.27 seconds
• Price: About 0.16 USD per million tokens
• GPQA X16 Median: Around 78%, placing it in the top performance band

Best for: Enterprises need cost control for hybrid deployments, orchestration in the cloud and on-premise, and open models.

# Bonus: Dipenfra

Deep infra is a cost-effective 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. Deepenfra supports many popular models and provides OpenIAI-compliant API endpoints with both regular and streaming inference options.

Although Deep Infra’s pricing is among the lowest on the market and attractive for experience and budget-sensitive projects, routing networks such as OpenRouter report that it may exhibit poorer reliability or lower uptime for some model endpoints than other providers.

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

• Speed: About 79 79 to 258 tokens per second
• delay: About 0.23 to 1.27 seconds
• Price: About US$0.10 per million tokens
• GPQA X16 Median: Around 78%, placing it in the top performance band

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

# Summary table

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

Abid Ali Owan For centuries.@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 create an AI product using graph neural networks for students with mental illness.