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# Introduction
Open‑weight fashions have remodeled the economics of AI. At present, builders can deploy highly effective fashions comparable to Kimi, DeepSeek, Qwen, MiniMax, and GPT‑OSS domestically, working them completely on their very own infrastructure and retaining full management over their methods.
Nevertheless, this freedom comes with a major commerce‑off. Working state‑of‑the‑artwork open‑weight fashions usually requires monumental {hardware} sources, usually tons of of gigabytes of GPU reminiscence (round 500 GB), virtually the identical quantity of system RAM, and high‑of‑the‑line CPUs. These fashions are undeniably massive, however additionally they ship efficiency and output high quality that more and more rival proprietary alternate options.
This raises a sensible query: how do most groups truly entry these open‑supply fashions? In actuality, there are two viable paths. You may both lease excessive‑finish GPU servers or entry these fashions via specialised API suppliers that provide you with entry to the fashions and cost you primarily based on enter and output tokens.
On this article, we consider the main API suppliers for open‑weight fashions, evaluating them throughout value, velocity, latency, and accuracy. Our quick evaluation combines benchmark knowledge from Synthetic Evaluation with dwell routing and efficiency knowledge from OpenRouter, providing a grounded, actual‑world perspective on which suppliers ship one of the best outcomes right this moment.
# 1. Cerebras: Wafer Scale Pace for Open Fashions
Cerebras is constructed round a wafer scale structure that replaces conventional multi GPU clusters with a single, extraordinarily massive chip. By protecting computation and reminiscence on the identical wafer, Cerebras removes most of the bandwidth and communication bottlenecks that decelerate massive mannequin inference on GPU primarily based methods.
This design permits exceptionally quick inference for big open fashions comparable to GPT OSS 120B. In actual world benchmarks, Cerebras delivers close to prompt responses for lengthy prompts whereas sustaining very excessive throughput, making it one of many quickest platforms accessible for serving massive language fashions at scale.
Efficiency snapshot for the GPT OSS 120B mannequin:
- Pace: roughly 2,988 tokens per second
- Latency: round 0.26 seconds for a 500 token technology
- Value: roughly 0.45 US {dollars} per million tokens
- GPQA x16 median: roughly 78 to 79 p.c, putting it within the high efficiency band
Greatest for: Excessive site visitors SaaS platforms, agentic AI pipelines, and reasoning heavy purposes that require extremely quick inference and scalable deployment with out the complexity of managing massive multi GPU clusters.
# 2. Collectively.ai: Excessive Throughput and Dependable Scaling
Collectively AI offers some of the dependable GPU primarily based deployments for big open weight fashions comparable to GPT OSS 120B. Constructed on a scalable GPU infrastructure, Collectively AI is broadly used as a default supplier for open fashions on account of its constant uptime, predictable efficiency, and aggressive pricing throughout manufacturing workloads.
The platform focuses on balancing velocity, value, and reliability somewhat than pushing excessive {hardware} specialization. This makes it a powerful alternative for groups that need reliable inference at scale with out locking into premium or experimental infrastructure. Collectively AI is usually used behind routing layers comparable to OpenRouter, the place it constantly performs effectively throughout availability and latency metrics.
Efficiency snapshot for the GPT OSS 120B mannequin:
- Pace: roughly 917 tokens per second
- Latency: round 0.78 seconds
- Value: roughly 0.26 US {dollars} per million tokens
- GPQA x16 median: roughly 78 p.c, putting it within the high efficiency band
Greatest for: Manufacturing purposes that want robust and constant throughput, dependable scaling, and value effectivity with out paying for specialised {hardware} platforms.
# 3. Fireworks AI: Lowest Latency and Reasoning-First Design
Fireworks AI offers a extremely optimized inference platform targeted on low latency and robust reasoning efficiency for open-weight fashions. The corporate’s inference cloud is constructed to serve in style open fashions with enhanced throughput and diminished latency in comparison with many normal GPU stacks, utilizing infrastructure and software program optimizations that speed up execution throughout workloads.
The platform emphasizes velocity and responsiveness with a developer-friendly API, making it appropriate for interactive purposes the place fast solutions and easy person experiences matter.
Efficiency snapshot for the GPT-OSS-120B mannequin:
- Pace: roughly 747 tokens per second
- Latency: round 0.17 seconds (lowest amongst friends)
- Value: roughly 0.26 US {dollars} per million tokens
- GPQA x16 median: roughly 78 to 79 p.c (high band)
Greatest for: Interactive assistants and agentic workflows the place responsiveness and snappy person experiences are essential.
# 4. Groq: Customized {Hardware} for Actual-Time Brokers
Groq builds purpose-built {hardware} and software program round its Language Processing Unit (LPU) to speed up AI inference. The LPU is designed particularly for working massive language fashions at scale with predictable efficiency and really low latency, making it preferrred for real-time purposes.
Groq’s structure achieves this by integrating excessive velocity on-chip reminiscence and deterministic execution that reduces the bottlenecks present in conventional GPU inference stacks. This strategy has enabled Groq to seem on the high of impartial benchmark lists for throughput and latency on generative AI workloads.
Efficiency snapshot for the GPT-OSS-120B mannequin:
- Pace: roughly 456 tokens per second
- Latency: round 0.19 seconds
- Value: roughly 0.26 US {dollars} per million tokens
- GPQA x16 median: roughly 78 p.c, putting it within the high efficiency band
Greatest for: Extremely-low-latency streaming, real-time copilots, and high-frequency agent calls the place each millisecond of response time counts.
# 5. Clarifai: Enterprise Orchestration and Price Effectivity
Clarifai gives a hybrid cloud AI orchestration platform that permits you to deploy open weight fashions on public cloud, non-public cloud, or on-premise infrastructure with a unified management aircraft.
Its compute orchestration layer balances efficiency, scaling, and value via methods comparable to autoscaling, GPU fractioning, and environment friendly useful resource utilization.
This strategy helps enterprises scale back inference prices whereas sustaining excessive throughput and low latency throughout manufacturing workloads. Clarifai constantly seems in impartial benchmarks as some of the cost-efficient and balanced suppliers for GPT-level inference.
Efficiency snapshot for the GPT-OSS-120B mannequin:
- Pace: roughly 313 tokens per second
- Latency: round 0.27 seconds
- Value: roughly 0.16 US {dollars} per million tokens
- GPQA x16 median: roughly 78 p.c, putting it within the high efficiency band
Greatest for: Enterprises needing hybrid deployment, orchestration throughout cloud and on-premise, and cost-controlled scaling for open fashions.
# Bonus: DeepInfra
DeepInfra is a cost-efficient AI inference platform that gives a easy and scalable API for deploying massive language fashions and different machine studying workloads. The service handles infrastructure, scaling, and monitoring so builders can deal with constructing purposes with out managing {hardware}. DeepInfra helps many in style fashions and offers OpenAI-compatible API endpoints with each common and streaming inference choices.
Whereas DeepInfra’s pricing is among the many lowest available in the market and engaging for experimentation and budget-sensitive tasks, routing networks comparable to OpenRouter report that it will possibly present weaker reliability or decrease uptime for sure mannequin endpoints in comparison with different suppliers.
Efficiency snapshot for the GPT-OSS-120B mannequin:
- Pace: roughly 79 to 258 tokens per second
- Latency: roughly 0.23 to 1.27 seconds
- Value: roughly 0.10 US {dollars} per million tokens
- GPQA x16 median: roughly 78 p.c, putting it within the high efficiency band
Greatest for: Batch inference or non-critical workloads paired with fallback suppliers the place value effectivity is extra necessary than peak reliability.
# Abstract Desk
This desk compares the main open-source mannequin API suppliers throughout velocity, latency, value, reliability, and preferrred use instances that can assist you select the correct platform in your workload.
| Supplier | Pace (tokens/sec) | Latency (seconds) | Value (USD per M tokens) | GPQA x16 Median | Noticed Reliability | Ideally suited For |
|---|---|---|---|---|---|---|
| Cerebras | 2,988 | 0.26 | 0.45 | ≈ 78% | Very excessive (usually above 95%) | Throughput-heavy brokers and large-scale pipelines |
| Collectively.ai | 917 | 0.78 | 0.26 | ≈ 78% | Very excessive (usually above 95%) | Balanced manufacturing purposes |
| Fireworks AI | 747 | 0.17 | 0.26 | ≈ 79% | Very excessive (usually above 95%) | Interactive chat interfaces and streaming UIs |
| Groq | 456 | 0.19 | 0.26 | ≈ 78% | Very excessive (usually above 95%) | Actual-time copilots and low-latency brokers |
| Clarifai | 313 | 0.27 | 0.16 | ≈ 78% | Very excessive (usually above 95%) | Hybrid and enterprise deployment stacks |
| DeepInfra (Bonus) | 79 to 258 | 0.23 to 1.27 | 0.10 | ≈ 78% | Reasonable (round 68 to 70%) | Low-cost batch jobs and non-critical workloads |
Abid Ali Awan (@1abidaliawan) is an authorized knowledge scientist skilled who loves constructing machine studying fashions. At the moment, he’s specializing in content material creation and writing technical blogs on machine studying and knowledge science applied sciences. Abid holds a Grasp’s diploma in know-how administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college students combating psychological sickness.
