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AI Open Source Models Comparison

A practical comparison of leading open source language models: Llama, Mistral, Qwen, Gemma, and Phi families, with guidance on licenses, sizes, and where each fits.

·5 min read · By Codeloom
Beginner 10 min read

What you'll learn

  • ✓Which model families dominate the open source landscape
  • ✓How licenses and weights affect commercial use
  • ✓The strengths and weaknesses of each family
  • ✓How to think about size, quantization, and serving
  • ✓Practical tips for picking a model for your use case

Prerequisites

  • •Basic LLM familiarity

The open source LLM scene moves fast. New models drop weekly, benchmarks shuffle, and yesterday’s leader fades. Still, the landscape has clear shape if you step back. This post compares the major families and helps you pick one.

What and Why

By open source models I mean LLMs whose weights are downloadable and runnable on your own hardware. Some are truly permissive; others have license clauses worth reading before you build a product on them. Open weights matter because they let you fine-tune, quantize, audit, and serve without depending on a vendor API.

Pick well and you cut costs, gain control, and avoid lock-in. Pick poorly and you spend months on a model that lags behind cheaper APIs or carries license terms you cannot meet.

Mental Model

Group the open ecosystem by lineage. Meta’s Llama family is the most influential, with strong general capability and a community license that allows most commercial use below a usage threshold. Mistral ships compact, efficient models under permissive Apache licenses, with a separate set of commercial offerings. Qwen, from Alibaba, has aggressive multilingual coverage and many sizes. Google’s Gemma models are small to medium, Apache-style licensed, and well-tuned for instruction following. Microsoft’s Phi line focuses on small models trained on synthetic textbook-quality data.

Within each family you usually find a base model, an instruction-tuned variant, and sometimes domain-specific releases for code, math, or vision.

Hands-on Example

Suppose you are building a customer support assistant that needs to run on a single 24 GB GPU. You want decent reasoning, good instruction following, and a clean license.

A Mistral 7B or Llama 3 8B Instruct fits comfortably in 16-bit, or in 4-bit quantization with room to spare. You serve it with vLLM, get a few thousand tokens per second on a modern GPU, and pay nothing per request.

For a smaller edge deployment you might pick Phi or Gemma 2B. For multilingual support, Qwen often wins. For a larger reasoning-heavy task, Llama 3 70B or Qwen 72B step up but need multiple GPUs or aggressive quantization.

use case
 |
 +-- general assistant, single GPU
 |     -> Mistral 7B / Llama 3 8B Instruct
 |
 +-- multilingual support
 |     -> Qwen 7B / 14B
 |
 +-- edge / mobile
 |     -> Phi-3 mini / Gemma 2B
 |
 +-- heavy reasoning, multi-GPU
 |     -> Llama 3 70B / Qwen 72B / Mixtral 8x22B
 |
 +-- code generation
       -> DeepSeek Coder / Code Llama / Qwen Coder
Picking an open source model

A quick load with Hugging Face Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

name = "mistralai/Mistral-7B-Instruct-v0.3"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForCausalLM.from_pretrained(name, device_map="auto")

prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Summarize: open models are great because..."}],
    return_tensors="pt",
).to(model.device)

out = model.generate(prompt, max_new_tokens=200)
print(tok.decode(out[0], skip_special_tokens=True))

Trade-offs

Llama has the deepest ecosystem of fine-tunes, tools, and documentation, but its license has a 700-million-monthly-user clause and a few use-case restrictions you must read.

Mistral and Mixtral models are Apache 2.0 and refreshingly unrestricted. Mixtral’s sparse mixture of experts gives strong quality per active parameter, but total memory is higher than the active size suggests.

Qwen models are strong on benchmarks and multilingual tasks but live under a tencent-style license that is mostly permissive with a few clauses worth checking.

Gemma is well-documented and Apache-style licensed with a usage policy you must accept. It is excellent at small sizes; the largest variants lag a bit behind Llama at the same scale.

Phi punches above its weight on reasoning benchmarks but can feel narrow on open-ended creative tasks. The synthetic training data shows.

Quantization changes the picture. A 4-bit 70B model fits on a single 48 GB GPU and outperforms a 16-bit 13B on most tasks. Do not pick a model without considering the quantization tier you will actually serve.

Practical Tips

Read the license before you commit. Open weights does not always mean open use. Save yourself a legal review months later.

Benchmark on your own task. Public leaderboards are useful signposts but rarely match production reality. Build a small eval set of 100 to 500 prompts and run candidates against it.

Test the instruction format. Each family uses different chat templates. Mismatched templates can cut quality by 20 points without any obvious error.

Plan for serving cost, not just model cost. A model that needs four GPUs is much more expensive to run than one that fits on one, even if the weights are free.

Track new releases monthly, not weekly. The pace is exhausting but most releases are incremental. Major jumps happen a few times a year.

Wrap-up

The open source LLM ecosystem is rich enough that almost any reasonable use case has a strong fit. Pick by license, size, and benchmark on your own task. The differences between families matter less than the discipline of evaluating against the problem you actually need to solve. Once you find a fit, the cost savings and control over the model can be transformative.

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