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Cohere vs Subquadratic

Two fact sheets from our own research, side by side — pricing, platforms, API access and status re-checked by us rather than quoted from the vendors. Only Cohere has a free tier. Both ship a public API.

CohereEnterprise LLM family (Command, Embed, Rerank) sold API-first with VPC, on-prem and dedicated hosting optionsSubquadraticLong-context LLM (SubQ) built to reason over million-token documents without chunking, in early access.

Side by side

FactCohereSubquadratic
CategoryFoundation Models & LLM APIsFoundation Models & LLM APIs
Pricing modelFreemiumContact
Public APIYesYes
PlatformsWeb, APIAPI
CompanyCohere Inc.Subquadratic Inc.
Launched20192026
Last verifiedSep 6, 2026Sep 1, 2026

What our research says

Cohere

Cohere is the pragmatic choice when retrieval quality and data sovereignty drive the decision — Embed and Rerank remain reference points, and few rivals match its VPC and on-prem menu. The catch is that its generative models trail the frontier labs on raw capability, and the newest platform products push you into sales conversations rather than self-serve.

Pros

  • Embed and Rerank are widely used building blocks for enterprise search
  • Deployment menu spans SaaS API, VPC, on-prem and managed dedicated instances
  • Clear per-token public pricing for API models
  • Free rate-limited keys make evaluation easy

Cons

  • Generation quality sits behind top-tier frontier models
  • Flagship platforms (North, Compass) have no public pricing
  • Trial keys prohibit commercial use

Subquadratic

Subquadratic is betting that a purpose-built sparse-attention architecture beats chunking-and-retrieval for genuinely huge documents, backed by real seed funding and a team with frontier-lab pedigree. Every performance and efficiency number available right now comes from the company itself, and the product isn't broadly available yet, so it can't be evaluated hands-on.

Pros

  • Raised $29M in seed funding, per independent press coverage, backing continued development
  • Claims a large compute-efficiency advantage at multi-million-token context via a proprietary attention method
  • Founding team has direct frontier-lab and relevant enterprise AI experience
  • Targets a genuine gap: true whole-document reasoning versus chunk-and-retrieve workarounds

Cons

  • Not generally available; access is gated behind an application
  • No published pricing
  • All benchmark numbers currently come from the company itself, not independent evaluation

Which one fits

Choose Cohere if you need

  • Enterprises in finance, healthcare and government needing private or sovereign deployment
  • RAG pipelines that lean on high-quality embeddings and reranking
  • Multilingual products using Command's 49-language coverage

Choose Subquadratic if you need

  • Teams that specifically need whole-document reasoning over multi-million-token inputs (huge codebases, contract archives)
  • Organizations willing to work with an early-access API rather than a GA product
  • Technical evaluators comfortable validating vendor-supplied benchmarks themselves

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