Celeris AI vs Voyage AI: Token Pricing, Speed & Intelligence
Full comparison of Celeris AI and Voyage AI — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
Celeris AI
High-throughput frontier reasoning with Celeris-1
Celeris AI is a frontier AI lab focused on high-throughput reasoning models. Celeris-1 is their flagship model, combining strong benchmark performance on coding, math, and agentic tasks with competitive inference speed. The model supports a 256K-token context window, prompt caching, and function calling.
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
Celeris AI
Voyage AI
Key differentiators
Celeris-1 targets the gap between o3-class reasoning quality and GPT-4o-class speed, offering frontier-tier intelligence scores at throughput rates competitive with non-reasoning models.
Voyage 3.5 Lite offers top-tier retrieval quality at just $0.02/1M tokens — the most cost-effective high-quality embedding available.
Frequently asked questions
Celeris AI FAQs
What is Celeris AI?
Celeris AI is a frontier AI lab that develops high-throughput reasoning models. Their flagship Celeris-1 model targets the intersection of strong reasoning capability and fast inference.
How does Celeris-1 compare to o3 and Claude Opus?
Celeris-1 sits in the same intelligence score range as o3 and Claude Opus 5, with competitive throughput. It is priced similarly to Claude Opus 5 at $3/1M input and $15/1M output.
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
Celeris AI — High-throughput frontier reasoning with Celeris-1
Celeris AI is a frontier AI lab focused on high-throughput reasoning models. Celeris-1 is their flagship model, combining strong benchmark performance on coding, math, and agentic tasks with competitive inference speed. The model supports a 256K-token context window, prompt caching, and function calling.
Celeris-1 targets the gap between o3-class reasoning quality and GPT-4o-class speed, offering frontier-tier intelligence scores at throughput rates competitive with non-reasoning models.
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
Celeris AI
- ▸Strong reasoning and coding benchmarks
- ▸High throughput at frontier tier
- ▸Competitive prompt caching pricing
Voyage AI
- ▸Top MTEB leaderboard performance
- ▸Multimodal embedding support
- ▸Very competitive pricing
Provider category context
Celeris AI is a frontier lab, founded in 2025. Voyage AI is a inference api, founded in 2023. Celeris AI as a frontier lab trains and serves its own proprietary models. Voyage AI as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers but without access to proprietary frontier models.
How to choose between them
Choose Celeris AI if you need strong reasoning and coding benchmarks. Choose Voyage AI if you need top mteb leaderboard performance. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.