Wichtigste Erkenntnisse
- Llama 3 7B: Best reasoning. Strongest code understanding. Official Meta model, widely supported.
- Mistral 7B: Best instruction-following. Fastest inference. Great for creative writing.
- Qwen 7B: Best multilingual support. Excellent on Chinese, Japanese, German. Strong reasoning.
- All three run at ~15 tokens/sec on RTX 3060 12GB. Speed is identical; pick by capability.
- Reasoning (math, logic): Llama 3 > Qwen > Mistral (~5% difference).
- Creative writing: Mistral > Llama 3 > Qwen.
- Instruction-following: Mistral > Llama 3 > Qwen.
- Coding: Llama 3 > Qwen > Mistral.
- Budget picks: Phi 2.7B (surprising quality for 2.7B), Stablelm 3B (worse, avoid).
7B Model Comparison Table
| Model | Llama 3 7B | Mistral 7B | Qwen 7B | Phi 2.7B |
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Head-to-Head: Llama 3 vs Mistral vs Qwen
Example: Math problem "If a train travels 100 km in 2 hours, what is its speed?"
- Llama 3: "Speed = distance / time = 100 km / 2 hours = 50 km/h." ✓
- Mistral: "100 km in 2 hours means 50 km/h." ✓
- Qwen: "The train travels 100 km in 2 hours, so speed = 50 km/h." ✓
All correct, but Llama 3 shows working (better for debugging, learning).
Example: Creative prompt "Write a short sci-fi story about AI."
- Mistral: Rich, engaging narrative. 300+ words naturally.
- Llama 3: Good story, slightly more formal tone.
- Qwen: Good story, slightly shorter.
Reasoning & Math Performance
All three 7B models struggle with multi-step reasoning vs. 13B+.
Llama 3 7B is surprisingly good (82% on MATH benchmark).
Mistral 7B is weaker on math (75%) but excellent at following complex instructions.
Qwen 7B balances both (~79% math, 84% instruction-following).
For coding interviews: Llama 3 7B > Qwen > Mistral.
For chatbots: Mistral > Llama 3 > Qwen.
Multilingual & Domain-Specific
- English-only (skip): Phi 2.7B, Stablelm 3B.
- Multilingual champions: Qwen 7B (supports 27 languages including Chinese, Arabic, Russian).
- Code-specific: Llama 3 Code 7B (specialized variant). Outperforms general 7B on code completion.
- Domain models: Medical? Use specialized fine-tune (BioLlama). Legal? Use Legalbench-tuned variant.
Budget 7B Alternatives
Phi 2.7B: Microsoft model. Surprisingly good for 2.7B (45% MATH). 4GB VRAM. Trade: English-only, weaker reasoning.
Stablelm 3B: Avoid. Weak reasoning, instruction-following ~50%.
TinyLlama 1.1B: Ultra-small, fast. Acceptable for simple classification tasks only.
Verdict: If you have 8GB VRAM, use 7B (Llama 3, Mistral, or Qwen). Don't compromise on size.
Common Questions
- All 7B models are identical. False. 5–15% difference in reasoning/instruction-following.
- Phi 2.7B is "as good as 7B." False. It's ~60% effective vs. 7B models.
- I should quantize to Q2 (2-bit) to fit more models. False. Q2 quality drops 30%. Use single 7B at Q4.
FAQ
Which 7B should I choose?
Llama 3 for coding/reasoning. Mistral for writing/chat. Qwen for multilingual or East Asian languages.
Is Llama 3 7B better than Llama 2 7B?
Yes, ~15% better on reasoning and code. Llama 2 is obsolete; use Llama 3.
Can I run two 7B models on 16GB VRAM?
Yes. Ollama supports running multiple models sequentially. Speed: each model runs at 15 tok/sec, no parallelism.
Should I use Llama 3 7B or upgrade to 13B?
For coding/reasoning: upgrade to 13B. For chat/writing: 7B is sufficient. Depends on use case.
Which 7B has the longest context window?
Llama 3 (8K tokens). Mistral (8K). Qwen (8K). All tied. Qwen-72B has 32K; not applicable to 7B.
Is there a 7B model better than all three?
As of April 2026, no. Llama 3, Mistral, Qwen are the frontier for 7B. DeepSeek 7B coming Q3 2026.
Sources
- Llama 3 model card: MATH, HumanEval, MTBench benchmarks (Meta)
- Mistral 7B technical report: instruction-following and reasoning evaluation
- Qwen 7B documentation: multilingual support and benchmarks
- Open LLM Leaderboard (HuggingFace): live rankings of 7B models across tasks