Cerebras vs Voyage AI: Token Pricing, Speed & Intelligence
Full comparison of Cerebras and Voyage AI — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
Cerebras
Wafer-scale AI chips — 4,500 tokens/sec, the fastest inference on earth
Cerebras uses wafer-scale silicon (the CS-3 chip covers an entire silicon wafer) to deliver extraordinary inference throughput. Llama 3.1 8B runs at 4,500+ tokens/second — roughly 10× faster than GPU-based providers. This makes Cerebras uniquely suited for real-time applications, voice AI, and interactive coding assistants.
Voyage AI
State-of-the-art embedding and reranking models
Voyage AI specialises in embedding and reranking models for retrieval-augmented generation (RAG) and semantic search. Voyage 3.5 and its variants consistently top the MTEB leaderboard for retrieval quality.
Key metrics
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Live token pricing
Strengths & weaknesses
Cerebras
Voyage AI
Key differentiators
Frequently asked questions
Cerebras FAQs
How fast is Cerebras inference?
Cerebras delivers 4,500+ tokens/second on Llama 3.1 8B — roughly 10× faster than GPU-based providers like Groq (1,200 t/s) or Together AI (350 t/s). This makes it the fastest inference option available.
What is a Cerebras wafer-scale chip?
The Cerebras CS-3 chip is fabricated on a single silicon wafer rather than individual dies. This gives it 900,000 AI cores and 44GB of on-chip SRAM, eliminating the memory bandwidth bottleneck that limits GPU inference speed.
What models does Cerebras support?
Cerebras currently supports Llama 3.1 8B and 70B, and Llama 3.3 70B. The model selection is intentionally limited — Cerebras focuses on delivering extreme speed on a curated set of models rather than broad catalog coverage.
Voyage AI FAQs
What is Voyage AI used for?
Voyage AI provides embedding and reranking models for RAG pipelines, semantic search, and document retrieval. It does not offer chat or text generation models.
How does Voyage AI compare to OpenAI embeddings?
Voyage 3.5 consistently outperforms OpenAI text-embedding-3-large on MTEB benchmarks while being significantly cheaper. It is the preferred choice for production RAG systems.
Provider resources
Cerebras — Wafer-scale AI chips — 4,500 tokens/sec, the fastest inference on earth
Cerebras uses wafer-scale silicon (the CS-3 chip covers an entire silicon wafer) to deliver extraordinary inference throughput. Llama 3.1 8B runs at 4,500+ tokens/second — roughly 10× faster than GPU-based providers. This makes Cerebras uniquely suited for real-time applications, voice AI, and interactive coding assistants.
Cerebras delivers 4,500+ tokens/sec on Llama 3.1 8B — 10× faster than any GPU provider, enabling genuinely real-time AI applications.
Voyage AI — State-of-the-art embedding and reranking models
Voyage AI specialises in embedding and reranking models for retrieval-augmented generation (RAG) and semantic search. Voyage 3.5 and its variants consistently top the MTEB leaderboard for retrieval quality.
Voyage 3.5 Lite offers top-tier retrieval quality at just $0.02/1M tokens — the most cost-effective high-quality embedding available.
Key strengths compared
Cerebras
- ▸4,500+ tokens/sec on Llama 3.1 8B — fastest inference available
- ▸Sub-50ms time-to-first-token for real-time applications
- ▸Wafer-scale chip architecture eliminates GPU memory bottlenecks
Voyage AI
- ▸Top MTEB leaderboard performance
- ▸Multimodal embedding support
- ▸Very competitive pricing
Provider category context
Cerebras is a inference api, founded in 2016. Voyage AI is a inference api, founded in 2023. Both are inference api providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both Cerebras and Voyage AI host open-weight models. The key differentiators are latency, throughput, and which specific model versions each provider offers. Check the speed metrics above — inference API providers often differ significantly on tokens-per-second for the same model. Pricing is typically competitive between them; availability of specific model versions (e.g., Llama 3.1 405B, DeepSeek V3) may be the deciding factor.