Model Selection: Which Model for Which Use Case?
The noris “LLM as a Service” platform offers several models of varying size and strength. The right choice depends on the use case: small models excel at speed and low cost, large ones at reasoning quality and context length. This decision matrix helps you get started quickly.
Decision Matrix
Section titled “Decision Matrix”| Use Case | Recommended Model | Why |
|---|---|---|
| Simple chat, classification | Gemma 4 31B | Fast, cost-effective, multimodal |
| Complex reasoning, math | GPT-OSS 120B | Reasoning levels, chain-of-thought |
| Coding, repository-level | Qwen 3.6 27B | SWE-bench 77.2%, agentic coding |
| Long documents (1M+) | GLM 5.2 | 1M context, IndexShare |
| RAG embedding | Harrier OSS 0.6B | Compact, fast, RAG-optimized |
| RAG reranking | BGE Reranker v2 M3 | Multilingual, precise |
| Multilingual RAG reranking | Jina Reranker v2 | 0.3B, high throughput |
Additional Factors
Section titled “Additional Factors”Beyond the primary use case, further criteria influence the choice:
- Context Length: How many tokens need to fit in a prompt? For long documents or entire repositories, prefer GLM 5.2 with up to 1M tokens of context. Smaller models are often sufficient if you’re already feeding short candidates via RAG.
- Cost: Larger models consume more tokens per request and cost more AI-Punkte. For bulk classification tasks, a compact model like Gemma 4 31B pays off.
- Latency: Larger models respond more slowly, especially with long contexts. For interactive chat applications with strict response-time requirements, prefer smaller models or enable Streaming.
- Tier: Prefer LTS versions for production environments. Experimental or short-cycle releases are suitable for testing and evaluation but shouldn’t be integrated directly into business-critical pipelines.
A complete overview of all available models, including context lengths and status, is available in the Models overview.