Model Selection

Dynamic Model Routing

Simply select “auto” in the OpenAI model selection to activate CompanyGPT’s dynamic routing. The system analyzes your prompt and automatically chooses the most efficient OpenAI model: fast, smaller models for standard questions and high-end models for complex analyses. This saves time and token costs without any manual effort.

Rule of thumb:

Fast & inexpensive → Mini / Flash / Nano / Haiku
Standard & reliable → large all-round models
Complex & critical → most powerful models
EU / internal / data protection → STACKIT models

OpenAI

GPT 5.5

For: coding, professional knowledge work, and the most complex tasks
When: software development, strategic decisions, critical analyses, high-stakes outputs
Why: latest flagship (1M context) with the highest overall performance across all categories

GPT 5.4

For: coding and demanding analyses with very long context
When: large document volumes, extensive reports, long-context workflows
Why: more affordable than GPT 5.5 while still delivering strong quality (1M context)

GPT 5.4 Mini

For: high quality with high throughput, coding, and autonomous agents
When: batch processing, scaled content workflows, computer use, subagents
Why: strongest mini model (400K context) with support for computer use and subagents

GPT 5.3 Chat

For: dynamic real-time interaction
When: assistant dialogues, iterative coordination, quick follow-up questions in chat
Why: chat-optimized for smooth conversations and strong context retention

GPT 5.2 Codex

For: software development and technical implementation
When: writing code, refactoring, debugging, creating tests
Why: specialized for development tasks with high code quality

GPT 5.2

For: challenging analysis and structuring tasks
When: complex evaluations, difficult trade-offs, precise reasoning
Why: strong reasoning model for demanding general workloads

GPT 5.2 Chat

For: demanding dialogues with high reliability
When: consulting-like conversations, long interactions, precise follow-ups
Why: optimized for chat quality and more robust against hallucinations

GPT 5.1 Chat

For: interactive dialogues and agent workflows
When: support, fluent conversations, strict adherence to instructions
Why: chat-optimized for natural language and stable context management

GPT 5.1

For: versatile general-purpose tasks
When: structured texts, analyses, automations without a primary chat focus
Why: balanced quality, speed, and reliability for everyday use

GPT 5 Mini

For: demanding tasks with efficiency
When: coding, structured outputs, longer texts
Why: more modern and robust than the GPT-4 series

GPT 5 Nano

For: extremely simple, fast tasks
When: auto-completion, short answers, mass tasks
Why: extremely fast and inexpensive, but limited depth

O3 Mini

For: logic, mathematics, coding, and step-by-step reasoning
When: when clean derivation is more important than style or creativity
Why: strongly optimized for precise problem-solving and rational derivations

GPT 4.1

For: complex analyses, clean argumentation
When: strategy, concepts, in-depth explanations
Why: more precise and stable than GPT 4o

GPT 4.1 Mini

For: better quality while still maintaining high speed
When: when GPT 4o mini is too superficial
Why: a good middle ground between quality and performance

GPT Image 1.5

For: image generation
When: when images need to be generated
Why: OpenAI’s image generation model

Google

Gemini 2.5 Flash

For: maximum speed
When: quick follow-up questions, brainstorming, iterations
Why: very fast, good for overview and context

Gemini 2.5 Pro

For: deep thinking and large contexts
When: complex documents, comparisons, analyses
Why: Google’s strong model for high quality on complex tasks

Gemini Image Tools / Nano Banana

For: image analysis, image generation, image editing
When: text-to-image generation, image editing with prompts (image + text), and composition of multiple images
Why: Google’s image models integrated into CompanyGPT

Anthropic

Claude Opus 4.6

For: highest complexity and in-depth analyses
When: strategic planning, extremely long contexts (1M), most difficult logic tasks
Why: strongest Anthropic model available in CompanyGPT for agent teams and parallel workflows (highest costs)

Claude Sonnet 4.6

For: programming, complex text processing, and demanding all-round tasks (Recommended)
When: software development, code refactoring, deep text comprehension
Why: the sweet spot of the series. Opus-class performance at Sonnet pricing (1M context)

Claude Haiku 4.5

For: very fast processing with high logical precision
When: filtering large data volumes, UI-based chatbots, simple to medium tasks at scale
Why: very fast and cost-efficient (less reasoning than Sonnet/Opus)

STACKIT

These open-source models run in the STACKIT Cloud (EU/Germany) and are particularly suited for workloads with high requirements for data protection, data sovereignty, and internal compliance.

Mistral Nemo Instruct

For: fast, general instruct tasks
When: internal assistants, standard QA, summaries with EU hosting
Why: efficient all-rounder with a good balance of quality, latency, and cost

Qwen 3 VL Embedding 8B

For: multimodal embeddings (text/image) for search and retrieval
When: semantic search, RAG indexing, similarity search across mixed data
Why: specialized in vector representations rather than classic chat responses

Qwen 3 VL 235B

For: multimodal high-end analysis with image and text understanding
When: visual document analysis, complex image-text tasks, demanding inference
Why: very powerful VL model for deeper understanding of multimodal content

GPT OSS 20B

For: lightweight open-source text tasks
When: cost-sensitive internal workflows with controllable infrastructure
Why: compact OSS approach for solid quality with lower resource requirements

Llama 3.1 8B Instruct

For: fast inference for standard instruct use cases
When: chatbots, classification, extraction, and simple automations
Why: small model size with high efficiency and solid production maturity

Llama 3.3 70B Instruct

For: more demanding generation and reasoning in the EU stack
When: more complex enterprise questions, longer responses, better level of detail
Why: 70B class provides significantly more quality than small models while retaining OSS flexibility

GPT OSS 120B

For: highest OSS quality for complex text tasks
When: when internally hosted top performance is required instead of maximum speed
Why: large open-source model for strong analytical and linguistic results

E5 Mistral 7B Instruct

For: high-quality text embeddings for retrieval and ranking
When: vector search, document retrieval, relevance ranking in RAG pipelines
Why: proven embedding model for precise semantic search applications

Gemma 3 27B

For: versatile instruct tasks with good efficiency
When: internal assistants, knowledge work, structured text production
Why: strong mid-range between small fast and large expensive OSS models

Quick decision-making aid

“I just want a very good answer” → GPT 5.1 / GPT 5 Mini / Claude Sonnet 4.6
“It should be as fast and inexpensive as possible” → GPT 5 Nano / GPT 5.4 Mini / Gemini 2.5 Flash / Claude Haiku 4.5
“I want to program / write code” → GPT 5.2 Codex / Claude Sonnet 4.6
“It’s complicated or extremely important” → GPT 5.5 / GPT 5.4 / Claude Opus 4.6 / Gemini 2.5 Pro
“Data protection (EU/Germany) is mandatory” → STACKIT models (e.g., Llama 3.3 70B Instruct)
“I work in chat (dialogue)” → GPT 5.3 Chat / GPT 5.2 Chat / GPT 5.1 Chat
“I need images” → GPT Image 1.5 / Gemini Image Tools / Nano Banana