MODELS

General Purpose Models

OpenAI

Flagship model for complex reasoning and coding. Smaller variants like mini and nano optimise for latency and cost.

GPT-5.4 GPT-5.4 Mini GPT-5.4 Nano

Anthropic

Opus is the most intelligent model for agents and coding. Sonnet balances speed and intelligence. Haiku is fastest with near-frontier intelligence.

Claude Opus 4.6 Claude Sonnet 4.6 Claude Haiku 4.5

Gemini

Google's multimodal reasoning models with 1M context, native image and video understanding, and adaptive thinking.

Gemini 2.5 Pro Gemini 3 Flash Gemini 2.5 Flash

Mistral

Unified MoE models combining reasoning, multimodal, and agentic coding in efficient open-source packages.

Mistral Small 4 Mistral Large 3

Cohere

Enterprise-focused models optimised for RAG, tool use, and multilingual deployment with data sovereignty.

Command A Command A Reasoning

DeepSeek

671B MoE models rivalling GPT-5 on reasoning benchmarks at a fraction of the cost.

DeepSeek V3.2 DeepSeek R2

Qwen

Alibaba's hybrid-architecture models with 262K context, 201 languages, and native multimodal agents.

Qwen 3.5 Qwen 3.5 Flash

Falcon

TII's hybrid Mamba-Transformer models excelling in Arabic AI, efficient reasoning, and edge deployment.

Falcon H1 34B Falcon H1R 7B

LLM Capabilities Comparison

The following table provides a detailed comparison of capabilities and use cases for each LLM in our platform:

Capability OpenAI Anthropic Gemini Mistral Cohere DeepSeek Qwen Falcon
Knowledge ★★★ ★★★ ★★★ ★★½ ★★½ ★★½ ★★½ ★½☆
Reasoning ★★★ ★★★ ★★★ ★★★ ★★☆ ★★★ ★★½ ★★☆
Coding ★★★ ★★★ ★★★ ★★★ ★★☆ ★★★ ★★½ ★★☆
Writing ★★★ ★★★ ★★½ ★★½ ★★☆ ★½☆ ★★☆ ★½☆
Logic ★★★ ★★½ ★★★ ★★½ ★★☆ ★★★ ★★½ ★½☆
Multilingual ★★★ ★★½ ★★★ ★★½ ★★★ ★★☆ ★★★ ★★½
Context 1M 1M 1M 256K 256K 164K 262K 256K
Alignment ★★★ ★★★ ★★★ ★★½ ★★½ ★½☆ ★★☆ ★½☆
Best For General purpose, agentic tasks Safety-first, coding agents Multimodal, research Efficient deployment, code Enterprise RAG, search Cost-efficient coding Multilingual, Asian langs Arabic AI, edge deploy

IMPLEMENTATION CONSIDERATIONS

When implementing AI in your organization, consider these key factors to maximize effectiveness:

TASK-WORKFLOW ALIGNMENT

Match workflows to the specific types of problems you're solving. Not all tasks benefit from the same workflow pattern.

PERSONA SELECTION

Choose the right expertise profiles for each stage of your workflow to ensure diverse and relevant perspectives.

CLOUD V ON-PREM

Whether to optimise performance - increase speed or reduce cost - or meet data compliance needs, choose the right setup for your needs.

DECISION WEIGHTING

Consider assigning different weights to various personas or stages based on relevance to the specific task.

PERFORMANCE MEASUREMENT

Establish clear metrics to evaluate workflow effectiveness and identify opportunities for optimization.

HYBRID APPROACHES

Consider combining multiple workflow patterns for complex tasks that require different processing stages.

READY TO OPTIMIZE YOUR AI WORKFLOWS?

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