Mistral 7B: The Quality-to-Size Champion
Best for: The default choice at the 8GB RAM tier. Strong all-around performance.
Mistral 7B has been the benchmark for quality-per-parameter since its release. In benchmarks against other 7B models, it consistently outperforms Llama 2 7B on every task (Mistral AI technical report, 2023). In the 2025-2026 generation of 7B models, Qwen 2.5 7B has pulled ahead on coding, but Mistral remains excellent for general use.
Scores (7B tier):
- MMLU: ~64% (Open LLM Leaderboard, Hugging Face, 2024)
- HumanEval coding: ~32% pass@1
- MT-Bench (instruction following): 7.6/10
What it does well: General chat, text summarization, instruction following, light coding tasks. Very fast on 8GB machines.
Where it falls short: Complex reasoning chains, mathematics, and coding tasks that require deep context.
Hardware: 4.1GB storage, ~6-7GB RAM when running. The default ollama run mistral pulls this model.
Llama 3.3 8B: Better Than Mistral 7B for Most Tasks
Best for: 8-16GB RAM machines where you want the best 7-8B class performance.
Llama 3.3 represents Meta's fourth generation of open source Llama models, and the quality improvement from Llama 2 to 3.3 is substantial. Llama 3.3 8B outperforms Llama 2 13B on most benchmarks despite being smaller.
Scores:
- MMLU: ~73% (Meta AI, Llama 3 model card, 2024)
- HumanEval: ~62% pass@1
- GSM8K (math): ~78%
What it does well: Instruction following is noticeably better than Mistral 7B. Coding quality is meaningfully higher. The model has a 128k token context window, which is large for its size class.
Where it falls short: Complex multi-step reasoning and tasks that genuinely benefit from more parameters. At 8B, you are still limited in how much context and reasoning depth the model can handle.
Hardware: 4.7GB storage, ~7-8GB RAM when running.
Gemma 2 9B: Strong Reasoning at the 9B Tier
Best for: 10-12GB RAM machines looking for strong analysis and reasoning performance.
Google's Gemma 2 9B consistently outperforms comparable-size models on reasoning benchmarks. It was trained with knowledge distillation from larger models, which contributes to its above-average performance for its size class.
Scores:
- MMLU: ~71.3% (Google Gemma 2 technical report, 2024)
- MT-Bench: 8.2/10 (notably high for a 9B model)
What it does well: Analytical tasks, structured reasoning, following complex instructions. The 8.2/10 MT-Bench score is exceptional for a 9B model and reflects genuinely strong instruction-following capability.
Where it falls short: Coding is not its strongest suit compared to Qwen 2.5 7B. Also, Google's usage license has some restrictions (commercial use above certain thresholds requires approval).
Hardware: 5.4GB storage, ~10-11GB RAM when running.
Best for: Developers who primarily use local LLMs for coding assistance.
Qwen 2.5, released by Alibaba Cloud's Qwen team, is the best open source model for coding at the 7B parameter scale. EvalPlus benchmark (a harder version of HumanEval with more edge cases) shows Qwen 2.5 7B at approximately 52% pass@1 (EvalPlus leaderboard, December 2024). For context, Mistral 7B scores around 31% on the same benchmark.
Scores:
- HumanEval: ~71% pass@1
- EvalPlus: ~52% pass@1
- MMLU: ~74.2%
What it does well: Code generation, code explanation, debugging assistance. The training data skew toward code quality is apparent in practice.
Where it falls short: Long-form writing and nuanced text generation are not as strong as Mistral 7B. Also, instruction-following in edge cases can be weaker.
Hardware: 4.4GB storage, ~7GB RAM when running.
Qwen 2.5 72B: Best Open Source Coding Model If You Have the Hardware
Best for: Developers with 64GB RAM who want the closest open source experience to GPT-4o for coding.
Qwen 2.5 72B is currently the strongest open source model for coding tasks. EvalPlus benchmark scores reach approximately 70% pass@1, which is competitive with GPT-4o-mini and noticeably above other 70B open source models (EvalPlus leaderboard, December 2024).
Scores:
- HumanEval: ~87% pass@1
- EvalPlus: ~70% pass@1
- MMLU: ~86.1%
What it does well: Complex coding tasks, multi-file refactoring, understanding large codebases, technical writing. At 72B, the quality jump from 7B models is significant and obvious.
Where it falls short: Requires 64GB RAM, so it is unavailable to most laptop users. Inference speed on CPU-only machines (even with 64GB) is slow, around 3-6 tokens per second. A Mac with M2 Max (64GB) runs it at 8-12 tokens per second, which is usable.
Hardware: ~43GB storage, 64GB RAM required.
Deepseek-R1 Distill: Reasoning Model for Local Use
Best for: Tasks that benefit from structured reasoning: math, logic puzzles, complex analysis, step-by-step problem solving.
Deepseek-R1 is a reasoning-focused model trained with reinforcement learning. The "distill" variants are smaller versions distilled from the full R1 model that can run locally. Deepseek-R1 Distill 8B is available via Ollama and fits in 8GB RAM.
What it does well: Tasks where thinking through a problem step by step matters. For complex debugging where you want the model to reason about the code rather than just pattern-match, R1 distill is often better than Llama 3.3 8B on the same hardware.
Where it falls short: Reasoning models are slower because they generate more tokens (the "chain of thought"). A query that takes 5 seconds on Mistral 7B may take 15-20 seconds on Deepseek-R1 8B because it thinks through the problem before answering.
Hardware: 4.9GB storage, ~8GB RAM when running (8B variant).
Llama 3.3 70B: Best All-Around Open Source at 70B
Best for: Teams with server infrastructure who want the best general-purpose open source model.
Llama 3.3 70B is Meta's top-tier open source model and is competitive with GPT-4o-mini on most general tasks.
Scores:
- MMLU: ~86% (Meta AI, Llama 3 model card, 2024)
- HumanEval: ~82% pass@1
- GSM8K: ~95%
For most applications, the quality difference between Llama 3.3 70B and GPT-4o is smaller than the difference between a 7B and a 70B model. If you have the hardware to run it, it is a genuine alternative to cloud APIs.
Hardware: ~40GB storage, 64GB RAM required.
Summary: Hardware to Model Map
| Your Hardware | Recommended Model | Notes |
|---|
| 4GB RAM | Phi-3 Mini 3.8B | Only viable option |
| 8GB RAM | Llama 3.3 8B or Qwen 2.5 7B | Llama for general use, Qwen for coding |
| 16GB RAM | Llama 3.3 8B (faster) or Gemma 2 9B | 13B quantized models also viable |
| 32GB RAM | Llama 3.3 8B runs very fast; try 13B Q8 | Meaningful quality step up |
| 64GB RAM | Llama 3.3 70B or Qwen 2.5 72B | Near-GPT-4o-mini quality |
| NVIDIA 8GB VRAM | Mistral 7B or Llama 3.3 8B (GPU-accelerated) | Much faster than CPU |
| NVIDIA 24GB VRAM | 13B or 34B models | Strong quality |
Keep Reading
- Ollama Complete Guide 2026 - How to install Ollama and run these models in 5 minutes
- Building a RAG System With Open Source Tools - Use local models in a document retrieval system
- Open Source Alternatives to GitHub Copilot - Use these models for free AI coding assistance
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