Text Models Compared

What This Article Covers

• A practical comparison of the leading local text models.
• Strengths and weaknesses of Llama, Mistral, Qwen, Gemma, and DeepSeek.
• Which model fits which use case.
• Hardware requirements and inference speed.
• How to choose the right model for your needs.

Introduction: Text Models Explained

Text models are the workhorses of local AI. They generate text, answer questions, summarize, and translate. But they’re not all the same: some excel at German, others at code, and some at reasoning. This comparison helps you pick the right one.

Why Compare Text Models?

Dozens of text models exist. Without a clear comparison, you might pick one that’s slow, poor at German, or too large for your hardware. This article shows which model works best for each task.

Text Models at a Glance

The top models for local deployment: Llama 3.1/3.2 (Meta), Mistral (Mistral AI), Qwen 2.5 (Alibaba), Gemma 2 (Google), and DeepSeek. All run locally via Ollama. They differ in language quality, speed, tool calling, context length, and licensing.

The key principle: not the “best” model overall, but the best for your specific task.

Who Should Read This?

• Beginners choosing their first model.
• Intermediate users optimizing for specific tasks.
• Developers who need tool calling.
• Multilingual users working with German.

Key Concepts

• Ollama - Model server. Use it to run models.
• Quantization - Model compression. Use it to reduce VRAM.
• Context Length - Maximum text length. Use it for long documents.
• Function Calling - Tool use. Use it for agents.
• Instruct - Instruction-tuned. Use it for chat and assistants.
• Base - Untuned. Use it for fine-tuning.

The Big Five Compared

Model	Creator	Sizes	Strength	Weakness	Best For
Llama 3.1/3.2	Meta	1B-405B	Versatile, excellent tool calling	German is good, not top-tier	General purpose, agents
Mistral	Mistral AI	7B-123B	Efficient, strong German	Limited tool calling	Chat, efficient workloads
Qwen 2.5	Alibaba	0.5B-72B	Multilingual, great tool calling	Less well-known	Multilingual, coding
Gemma 2	Google	2B-27B	Efficient, safe	Smaller model range	Simple tasks, edge devices
DeepSeek	DeepSeek	1.5B-236B	Reasoning, coding	Complex licensing	Math, programming

Detailed Breakdown

1. Llama 3.1 / 3.2 (Meta)

Strengths:

• Best all-around model for local deployment
• Excellent tool calling (critical for agents)
• Large community with many fine-tuned variants
• Llama 3.2: Compact models (1B, 3B) for edge devices

Weaknesses:

• German quality is good but not exceptional
• Larger versions demand significant VRAM

Recommendation: Use llama3.1:8b for most applications.

2. Mistral (Mistral AI)

Strengths:

• Very efficient (fast with minimal VRAM)
• Strong German output
• Mistral Nemo: robust 12B model

Weaknesses:

• Tool calling less mature than Llama
• Fewer model options

Recommendation: Use mistral-nemo for chat and German text.

3. Qwen 2.5 (Alibaba)

Strengths:

• Excellent multilingual support including German
• Strong tool calling capabilities
• Qwen 2.5-Coder: top-tier for programming

Weaknesses:

• Less well-known, smaller community
• Alibaba model (potential privacy concerns)

Recommendation: Use qwen2.5:14b for multilingual applications.

4. Gemma 2 (Google)

Strengths:

• Very efficient (small, fast)
• Good safety properties (resistant to jailbreaks)
• 2B model suitable for edge and IoT

Weaknesses:

• Smaller models have fewer capabilities
• Fewer fine-tuned variants available

Recommendation: Use gemma2:2b for simple tasks, gemma2:9b for more complex work.

5. DeepSeek (DeepSeek)

Strengths:

• Excellent for reasoning and math
• DeepSeek-Coder: exceptional for programming
• DeepSeek-R1: reasoning comparable to o1

Weaknesses:

• Complex licensing model
• Chinese model (privacy considerations apply)

Recommendation: Use deepseek-r1 for reasoning, deepseek-coder for code.

Model Size Comparison

Size	VRAM (Q4)	Speed	Quality	Use Case
1-3B	2-3 GB	Very fast	Basic	Edge, IoT, rapid testing
7-9B	5-6 GB	Fast	Good	Standard applications
12-14B	8-10 GB	Moderate	Very good	Quality-focused work
30-70B	20-40 GB	Slow	Excellent	Professional workloads
100B+	60+ GB	Very slow	Maximum	Server-only deployments

German Language Quality

Model	German	Notes
Mistral	⭐⭐⭐⭐	Excellent, European focus
Qwen 2.5	⭐⭐⭐⭐	Excellent, strong multilingual
Llama 3.1	⭐⭐⭐	Good, not exceptional
Gemma 2	⭐⭐⭐	Adequate
DeepSeek	⭐⭐	Leans toward English/Chinese

Tool Calling Capability

Model	Tool Calling	Notes
Llama 3.1	⭐⭐⭐⭐⭐	Best-in-class tool calling
Qwen 2.5	⭐⭐⭐⭐	Very strong
Mistral	⭐⭐⭐	Good
Gemma 2	⭐⭐	Limited
DeepSeek	⭐⭐⭐	Good for reasoning

Model Selection by Use Case

Use Case	Recommended	Alternative
General-purpose chat	llama3.1:8b	mistral-nemo
German text	mistral-nemo	qwen2.5:14b
Agents/tools	llama3.1:8b	qwen2.5:14b
Multilingual	qwen2.5:14b	mistral-nemo
Reasoning	deepseek-r1	qwen2.5:32b
Edge/IoT	gemma2:2b	llama3.2:1b
Code generation	deepseek-coder	qwen2.5-coder

Security and Legal Considerations

• Check licensing: Not all models permit commercial use.
• Data privacy: Local models keep data on-device. Verify cloud alternatives.
• Jailbreaks: Larger models can be more vulnerable. See Prompt Injection.
• Bias: All models exhibit bias. Test thoroughly for critical applications.

Common Pitfalls

• Oversized models: Running a 70B model on 8GB VRAM causes out-of-memory errors. Quantize or downsize.
• Wrong quantization: Q4 balances quality and size well. Q2 loses significant quality. See Quantization.
• Base instead of Instruct: Base models don’t follow instructions. Always use Instruct for chat.
• Missing tool calling: Not every model supports tool calling. For agents, choose llama3.1 or qwen2.5.

Further Reading

• Ollama - Run models locally.
• Model Gallery - All available Ollama models.
• Model Recommendations - Curated suggestions.
• Quantization - Model compression explained.
• VRAM Calculator - Estimate memory requirements.
• Coding Models - Specialized for code.
• Reasoning Models - For complex reasoning.

Key Takeaways:

• llama3.1:8b is the best all-around model for local AI.
• mistral-nemo offers the best German support.
• qwen2.5:14b excels at multilingual work and tool calling.
• deepseek-r1 leads for reasoning tasks.
• gemma2:2b is best for edge deployments.
• For agents, always pick models with tool-calling support.

FAQ

Sources and Further Resources

• Llama - Meta models.
• Mistral - Mistral models.
• Qwen - Alibaba models.
• Gemma - Google models.
• Ollama Library - Available models.