I was debugging a customer support chatbot last month when it happened again. The bot gave a completely wrong answer about a refund policy because it could only see the first chunk of a 20-page document. The context window was too damn small.
That frustration just became obsolete.
Cohere dropped Rerank 4 on December 11th with a 32K token context window – four times larger than Rerank 3.5's measly 8K limit. This isn't just an incremental bump. This is the difference between your AI agent seeing a paragraph versus reading the entire manual.
Why This Actually Matters (Beyond the Marketing Hype)
Most reranking models suffer from what I call "keyhole vision" – they peek through tiny windows and miss crucial context. Rerank 4 throws open the curtains.
Here's what changed:
- 32K tokens means processing documents that would previously get chopped into fragments
- Two variants: Fast (same latency as 3.5 but higher accuracy) and Pro (maximum accuracy for complex reasoning)
- Cross-encoder architecture that processes queries and documents together instead of separately
- 100+ languages with top performance in 10 major commercial languages
But the real kicker? It excels on semi-structured data like code, tables, and JSON. Finally, someone who gets that enterprise data isn't just clean paragraphs.
<> "Rerank 4.0 dramatically improves search quality, reduces hallucinations, and strengthens AI agent reasoning with just a few lines of code," according to Microsoft Azure Foundry./>
That's not just vendor speak. The technical implications are massive.
The Developer Reality Check
Integrating this thing is surprisingly straightforward. The API supports parameters like top_n for limiting results and max_tokens_per_doc (default 4096, up to 40,000). You can truncate long documents intelligently instead of just cutting them off mid-sentence like a barbaric substring operation.
The real win? Fewer, higher-quality documents get passed to your LLMs. This means:
- Lower token costs (up to 25% reduction according to research)
- Less "trace bloat" in agentic pipelines
- Reduced hallucinations from irrelevant context
Cohere also made it deployable in VPCs or on-premises. Smart move – enterprises are paranoid about their data, and rightfully so.
The Enterprise Sweet Spot
This targets exactly where enterprise search has been failing: finance, healthcare, energy, government, manufacturing. Industries where accuracy isn't negotiable and context windows matter.
Think about it:
- Medical records spanning multiple visits
- Financial regulations with cross-references
- Manufacturing specs with nested dependencies
- Legal documents with endless clauses
Rerank 3.5 would choke on these. Rerank 4 eats them for breakfast.
The availability on Microsoft Azure Foundry, AWS Marketplace, and Oracle Cloud Infrastructure removes integration friction. No more "let me check with our infrastructure team" delays.
What Nobody's Talking About
Here's the thing everyone's missing: this is Cohere's first self-learning reranker. Users can customize it for domain-specific tuning.
That's huge.
Generic models are fine for generic problems. But enterprise search isn't generic. Your company's jargon, document structure, and user patterns are unique. A reranker that learns your specific context? That's a competitive moat.
The multilingual support (100+ languages) also matters more than people realize. Global enterprises don't operate in English-only bubbles. Cross-lingual search in complex datasets has been a nightmare – until now.
My Bet
Rerank 4 becomes the default choice for enterprise RAG systems within six months. The combination of massive context windows, domain customization, and proven accuracy in structured data creates too much value to ignore. Companies still using 8K context windows will feel like they're browsing the web on dial-up – technically functional, but painfully limited.
