LLM Providers
POCR.AI orchestrates prompts across six leading LLM providers - OpenAI, Anthropic, Google, Mistral, DeepSeek, and Cohere. Five power your workflows; Cohere acts as the meta-agent that produces structured outputs.
OpenAI's frontier reasoning and coding models, with smaller variants optimised for latency and cost. Strong general-purpose performance and tool-use capabilities.
Opus delivers top-tier reasoning and coding intelligence, Sonnet balances speed and quality, Haiku is the fastest with near-frontier intelligence.
Google's multimodal reasoning models with very long context, native image and video understanding, and adaptive thinking modes.
Unified MoE models combining reasoning, multimodal, and agentic coding in efficient open-weight packages — strong cost/quality trade-off.
Large MoE models that rival frontier labs on reasoning and coding benchmarks at a fraction of the cost. Excellent value for high-volume deployments.
Enterprise-focused models optimised for RAG, tool use, and multilingual deployment. Used exclusively as the POCR meta-agent — not selectable as a primary model.
Cohere Command-A acts as the POCR meta-agent — it analyses all model outputs and generates the structured result formats (Rank, Reasons, Compare, Checkbox). It is not available as a primary workflow model.
Meta-Agent Only| Capability | OpenAI | Anthropic | Gemini | Mistral | DeepSeek | Cohere |
|---|---|---|---|---|---|---|
| Knowledge | ★★★ | ★★★ | ★★★ | ★★½ | ★★½ | ★★½ |
| Reasoning | ★★★ | ★★★ | ★★★ | ★★★ | ★★★ | ★★☆ |
| Coding | ★★★ | ★★★ | ★★★ | ★★★ | ★★★ | ★★☆ |
| Writing | ★★★ | ★★★ | ★★½ | ★★½ | ★½☆ | ★★☆ |
| Logic | ★★★ | ★★½ | ★★★ | ★★½ | ★★★ | ★★☆ |
| Multilingual | ★★★ | ★★½ | ★★★ | ★★½ | ★★☆ | ★★★ |
| Alignment | ★★★ | ★★★ | ★★★ | ★★½ | ★½☆ | ★★½ |
| Best For | General purpose, agentic | Safety-first, coding | Multimodal, research | Efficient deploy, code | Cost-efficient reasoning | Enterprise RAG, search |
Match workflows to the specific types of problems you're solving. Not all tasks benefit from the same workflow pattern — sequential refinement excels at iterative quality, while majority vote suits independent perspective gathering.
Choose the right lens for each run. The Technical lens excels with DeepSeek and Mistral for cost-efficient analytical work; the Strategic lens pairs well with OpenAI and Anthropic for governance and risk analysis.
Optimise for performance, cost, or data compliance needs. Enterprise customers can opt for provisioned hardware or a local-cloud deployment for sensitive workloads.
Establish clear metrics to evaluate workflow effectiveness. Use the Rank and Reasons output formats to compare model quality across runs and identify which combinations produce the strongest outputs.
Mix high-capability models with cost-efficient alternatives across workflow stages. Sequential Refinement allows you to draft cheaply and refine expensively only where it matters.
Combine multiple workflow patterns for complex tasks requiring different processing stages. A Divide & Conquer decomposition can feed individual Sequential Refinement chains before a final Majority Vote synthesis.