Open-Source Large Language Models: The 2025 Buyer’s Guide

A plain-language, data-only handbook for junior college graduates and busy practitioners

Table of Contents

  1. Why bother choosing the model yourself?
  2. Four size buckets that make sense
  3. Giant models (>150 B): when you need the brain
  4. Mid-size models (40–150 B): the sweet spot for most teams
  5. Small models (4–40 B): run on one gaming GPU
  6. Tiny models (≤4 B): laptops, phones, and Raspberry Pi
  7. One mega-table: parameters, context length, price, and download link
  8. FAQ: answers we hear every week
  9. 60-second decision checklist

1. Why bother choosing the model yourself?

  • Open-source weights mean you can download, fine-tune, and host on-prem.
  • Price spreads are wild: two 70 B models can differ by 15× in cost per million tokens.
  • Context length decides whether your chatbot “remembers” an entire legal contract or forgets after two pages.

2. Four size buckets that make sense

Bucket Total params Active params* Typical use case Minimum hardware
Giant >150 B 20–40 B research, complex reasoning 8×A100 or 4×H100
Mid 40–150 B 10–40 B company chatbot, code completion 1×A100 or 2×RTX 4090
Small 4–40 B 2–30 B local dev box, light API RTX 4090 24 GB, M2 Ultra
Tiny ≤4 B 0.5–4 B mobile, edge IoT, Raspberry Pi CPU with 8 GB RAM

* Active params = weights actually used during inference. Smaller active count = less VRAM.

3. Giant models (>150 B): when you need the brain

3.1 Top performers (July 2025 data)

Model Total / Active Params Intelligence Score* Context Length Price per 1 M tokens Hugging Face
Qwen3 235B A22B 2507 (Reasoning) 235 B / 22 B 69 132 k $2.6 Link
DeepSeek R1 0528 685 B / 37 B 68 128 k $1.0 Link
GLM-4.5 355 B / 32 B 66 128 k — Link
MiniMax M1 80k 456 B / 45.9 B 63 1 M $0.8 Link

* Intelligence Score is a composite of MMLU-Pro, GPQA Diamond, Humanity’s Last Exam, LiveCodeBench, SciCode, AIME, and MATH-500 benchmarks.

3.2 Quick take-aways

  • Smartest: Qwen3 235B 2507 (Reasoning).
  • Longest memory: MiniMax M1 80k handles a million-token window—good for entire books.
  • Cheapest per token: DeepSeek R1 0528 at $1.0 per million tokens.

4. Mid-size models (40–150 B): the sweet spot for most teams

4.1 Highest intelligence in this bracket

Model Total / Active Params Intelligence Score Context Price Notes
Llama Nemotron Super 49B v1.5 (Reasoning) 49 B / 49 B 64 128 k — Dense, fast inference
DeepSeek R1 Distill Llama 70B 70 B / 70 B 48 128 k $0.8 Distilled, great value
Llama 4 Scout 109 B / 17 B 43 10 M $0.2 Ultra-long context, lowest price

4.2 How to pick

  • Single 24 GB GPU → Llama Nemotron Super 49B v1.5 (int8 fits in 24 GB).
  • Need 10 M context → Llama 4 Scout for whole-document Q&A.
  • Budget tight → DeepSeek R1 Distill Llama 70B, still 70 B-class at < $1.

5. Small models (4–40 B): run on one gaming GPU

5.1 Ceiling of this tier

Model Total / Active Params Intelligence Score Context Price Typical GPU
EXAONE 4.0 32B (Reasoning) 32 B / 32 B 64 131 k $1.0 RTX 4090 24 GB
Qwen3 32B (Reasoning) 32.8 B / 32.8 B 59 128 k $2.6 RTX 4090 24 GB
QwQ-32B 32.8 B / 32.8 B 58 131 k $0.5 RTX 4090 24 GB

5.2 Real-world numbers

  • Indie developer: RTX 4090 + QwQ-32B. Local code completion costs $0.50 per million tokens.
  • SME chatbot: One A6000 48 GB can serve four concurrent Qwen3 32B instances at <$0.01 per turn.

6. Tiny models (≤4 B): laptops, phones, and Raspberry Pi

Model Total / Active Params Intelligence Score Context Price Notes
Qwen3 1.7B (Reasoning) 2.03 B / 2.03 B 38 32 k $0.4 Runs on Jetson Orin Nano
Phi-4 Mini Instruct 3.84 B / 3.84 B 26 128 k — Microsoft’s tiny powerhouse, CPU real-time
Llama 3.2 3B 3 B / 3 B 20 128 k $0.0 Free, mobile-friendly

“

Tested: Phi-4 Mini int4 on M1 Max yields 12 tokens/s; translating a 300-word email finishes in under three seconds.

7. One mega-table: parameters, context length, price, and download link

Bucket Model Total Params Active Params Context Length Price ($/1 M tokens) Hugging Face
Giant Qwen3 235B A22B 2507 (R) 235 B 22 B 132 k 2.6 Link
Giant DeepSeek R1 0528 685 B 37 B 128 k 1.0 Link
Mid Llama Nemotron 49B v1.5 (R) 49 B 49 B 128 k — Link
Mid Llama 4 Scout 109 B 17 B 10 M 0.2 Link
Small EXAONE 4.0 32B (R) 32 B 32 B 131 k 1.0 Link
Small QwQ-32B 32.8 B 32.8 B 131 k 0.5 Link
Tiny Qwen3 1.7B (R) 2.03 B 2.03 B 32 k 0.4 Link
Tiny Llama 3.2 3B 3 B 3 B 128 k 0.0 Link

8. FAQ: answers we hear every week

Q1. Is the difference between Intelligence 60 and 70 noticeable?
In academic benchmarks like MMLU-Pro and GPQA, yes. In daily chatbots or translation, most users won’t feel it.

Q2. Is a 1 M token context just marketing?
MiniMax demonstrates a 300 000-character novel summarized in one pass with <10 s latency. It works.

Q3. How do I estimate GPU memory?
Rule of thumb:

VRAM ≈ active params × 2 bytes (fp16)

Example: QwQ-32B (32 B active) → 32 GB fp16; int4 quantization shrinks it to ~8 GB—fits an RTX 4080.

**Q4. What does 0.0008, or about half a cent.

Q5. Do distilled models lose quality?
DeepSeek’s report shows R1 Distill Llama 70B drops only 2 % on HumanEval coding tasks while cutting cost by 10×.

9. 60-second decision checklist

Step 1: Match the task

  • Research / complex reasoning → Giant
  • Company chatbot / code completion → Mid
  • Local IDE plugin → Small
  • Mobile app → Tiny

Step 2: Check the wallet

Budget per 1 M tokens Recommended bucket Example model
< $0.5 Tiny Llama 3.2 3B
1 Small QwQ-32B
2 Mid DeepSeek R1 Distill Llama 70B
3 Giant DeepSeek R1 0528

Step 3: Match the hardware

  • 8 GB VRAM → Tiny model
  • 16–24 GB → Small model
  • 48 GB+ → Mid model
  • Multi-GPU 80 GB → Giant model

Step 4: One-line install

# Example: QwQ-32B on consumer GPU
pip install transformers torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Qwen/QwQ-32B")
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/QwQ-32B",
    torch_dtype="auto",
    device_map="auto"
)

Closing thoughts

There’s no “best” model—only the best fit for task, budget, and hardware. Bookmark this page and share the mega-table the next time someone claims “bigger is always better.”