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INTELLIGENCE ALLIANCE DIRECTORY
Frontier LLMs + Jev Pairing Matrix
Modern AI agents require both fast, intuitive decision making and deliberate analytical reasoning. Explore our dedicated integration guides to see how TypeSafe Jev pairs with your existing model provider.
OpenAI40x - 190x Faster
Jev + OpenAI GPT-4o / GPT-5
Dual-Brain Agent Routing and 97% Cost Reduction on High-Frequency Decisions
P50 Decision Latency:114ms
Cost Reduction:97.2%
Golden Agreement:98.9%
Dual-System Agent Cost ReductionThe dual-system architecture delegates high-frequency routing and classification decisions to Jev while reserving GPT for complex reasoning tasks, targeting significant cost reduction in production agent pipelines.
Updated: 2026-09-20Read Full Guide →
Anthropic25x - 80x Faster
Jev + Anthropic Claude 3.5 Sonnet / Opus
Zero-Hallucination Tool Selection, Terminal Agent Shielding, and Cost-Effective Evaluation
P50 Decision Latency:128ms
Cost Reduction:98.8%
Golden Agreement:99.4%
Structured Evaluation PipelineUsing Jev as an inline validator and evaluation judge for programmatic pass/fail assertions can dramatically reduce the cost of running large evaluation suites compared to invoking frontier LLMs for each assertion.
Updated: 2026-09-20Read Full Guide →
DeepSeek18x - 50x Faster
Jev + DeepSeek V3 / R1
High-Throughput Content Triage at 780 Decisions Per Second with Zero Token Waste
P50 Decision Latency:142ms
Cost Reduction:89.4%
Golden Agreement:97.8%
High-Throughput Content Triage PipelineIn high-volume content classification pipelines, Jev processes structured decisions in parallel without autoregressive token generation, allowing DeepSeek to be reserved for high-value synthesis tasks on filtered candidates.
Updated: 2026-09-20Read Full Guide →
Google30x - 120x Faster
Jev + Google Gemini 1.5 / 2.0
Multimodal State Embedding, Live Search Grounding, and Real-Time Web Routing
P50 Decision Latency:110ms
Cost Reduction:95.1%
Golden Agreement:98.2%
Multimodal Decision RoutingEngineers pair Jev as a real-time router with Gemini multimodal capabilities. Jev evaluates whether user queries require internal database queries, live web crawling, or direct cached answers, reducing unnecessary model invocations.
Updated: 2026-09-20Read Full Guide →
xAI35x - 140x Faster
Jev + xAI Grok 2 / Grok 3
Real-Time Social Stream Triage and High-Velocity Sentiment Filtering
P50 Decision Latency:98ms
Cost Reduction:96.5%
Golden Agreement:97.4%
Social Stream Moderation FirewallUsing Jev as an upfront moderation firewall in high-velocity social streams allows developers to filter large volumes of content at high speed, invoking Grok only on posts requiring creative or context-rich replies.
Updated: 2026-09-20Read Full Guide →
Open Source / Kimi20x - 60x Faster
Jev + Moonshot Kimi & Open Weights (Qwen / Llama)
Edge Deployment, Local GGUF Decision Engines, and Hybrid Privacy Architectures
P50 Decision Latency:154ms (Local)
Cost Reduction:100% (Local)
Golden Agreement:89.1%
Local Decision Engine via llama-serverCommunity implementations of Jev-compatible APIs within llama-server enable running structured classifications with probabilities on local consumer hardware without internet connectivity, suitable for robotics and air-gapped environments.
Updated: 2026-09-20Read Full Guide →
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COMPARATIVE_BENCHMARK_MATRIX // JEV_VS_MODELS| LLM Provider | Role Split (System 1 / 2) | P50 Latency | Cost Cut | Primary Use Case | Documentation |
|---|---|---|---|---|---|
| OpenAI | Jev (Router) + OpenAI (Reasoning) | 114ms | 97.2% | Dual-System Agent Cost Reduction | View Guide → |
| Anthropic | Jev (Router) + Anthropic (Reasoning) | 128ms | 98.8% | Structured Evaluation Pipeline | View Guide → |
| DeepSeek | Jev (Router) + DeepSeek (Reasoning) | 142ms | 89.4% | High-Throughput Content Triage Pipeline | View Guide → |
| Jev (Router) + Google (Reasoning) | 110ms | 95.1% | Multimodal Decision Routing | View Guide → | |
| xAI | Jev (Router) + xAI (Reasoning) | 98ms | 96.5% | Social Stream Moderation Firewall | View Guide → |
| Open Source / Kimi | Jev (Router) + Open Source / Kimi (Reasoning) | 154ms (Local) | 100% (Local) | Local Decision Engine via llama-server | View Guide → |