KI-Agenten.shopWhich LLM for business? Models, costs and data controls
The right LLM completes your task with acceptable errors, effort and cost. Compare a staff chat app separately from an API model for your workflows and from self-hosted model weights. A large context window or familiar provider does not prove the best fit.
Five steps to choosing a model
Choose Claude, GPT, Gemini, Mistral or Llama for business using task requirements, API costs, data controls and a practical evaluation method.
- Define one task, its expected output and disqualifying failures. Separate document extraction, drafting and actions in business systems.
- Build a representative test set with normal, difficult and prohibited cases. Use sensitive data only after approval.
- Assess factual accuracy, supporting evidence, missing information and human rework using the same criteria for every candidate.
- Measure latency and total cost per accepted result: model calls, retries, tools, integration and review.
- Check permissions, data paths, licensing, operational ownership and a stop mechanism before production.
Compare deployment choices
The right LLM completes your task with acceptable errors, effort and cost. Compare a staff chat app separately from an API model for your workflows and from self-hosted model weights. A large context window or familiar provider does not prove the best fit.
| Provider | What to check |
|---|---|
| Claude | Choose chat and API separately; record the exact model and hosting platform. |
| OpenAI / GPT | Separate ChatGPT subscriptions from API billing; record the model identifier. |
| Gemini | Evaluate Developer API and cloud deployments separately, including required regions and features. |
| Mistral | Choose global or regional endpoints deliberately; storage and processing locations differ. |
| Llama | Review weights, license and operating environment; self-hosting still has costs. |
API price examples
Examples checked October 2, 2026, not a ranking or app subscription comparison. Standard uncached text input and text output rates; assess special tiers and extra features separately.
| Model | Input: USD / 1M tokens | Output: USD / 1M tokens |
|---|---|---|
| Claude Opus 5.5 | 4 | 20 |
| GPT-6.1 Sol | 2 | 10 |
| Gemini 3.5 Flash | 1.5 | 9 |
Cost example per request
Assume 2,000 input and 500 billed output tokens per request. Cost = input / 1,000,000 × input rate + output / 1,000,000 × output rate. This gives USD 0.018 for Opus 5.5, USD 0.009 for GPT-6.1 Sol and USD 0.0075 for Gemini 3.5 Flash. For 1,000 identical requests: USD 18 / 9 / 7.50 in model charges. This is arithmetic, not a quality comparison; additional reasoning tokens, tools, caching, retries and operations can change the total.
Check the actual data path
Assess storage, inference, metadata, training and retention separately. Record product, model, endpoint, region, enabled features and contractual commitments. An EU address or processing agreement alone does not describe the entire data path.
Mistral separates regional inference from ZDR, with limitations for stateful features. OpenAI requires additional approvals for non-US regions. Anthropic distinguishes inference and workspace geography; partner platforms have separate rules. Google global endpoints do not provide regional isolation. Follow the specific primary sources below.
Even self-hosting requires secure logs, backups, access controls and controlled connections to external tools. Review the applicable model license and processing context. No deployment choice automatically guarantees GDPR compliance.
One model or several?
Start with a suitable baseline model. Add another only for demonstrated value, such as fallback or a different task class. Routing and fallback also add testing, maintenance and data-governance work. Aggregate provider market share does not prove that every company uses multiple models or that routing lowers costs without affecting quality.
LLM selection questions
Which LLM is best for business?
Test your tasks against your acceptance criteria. This selection guide is not our own benchmark and does not award unverified winners.
Is a large context window more important than RAG?
A context limit describes input capacity, not guaranteed accuracy. Assess retrieval, freshness, access permissions and answer quality; compare long context and retrieval on the same task.
Does a chat subscription include API costs?
Budget app licenses and API usage separately unless the specific contract confirms included API usage. Compare total cost per accepted result.
Primary sources
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
OpenAI: GPT-6.1 Sol
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
Anthropic: API pricing
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
Google: Gemini API pricing
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
OpenAI: data controls
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
Anthropic: data residency
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
Anthropic: model-specific retention
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
Google: data residency
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
Mistral: regional inference
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
Mistral: zero data retention
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
Meta: Llama 4 license
Provider documentation checked September 15, 2026. Each statement applies to its described product and conditions; no original performance benchmark.
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