Open source reasoning models comparison
Llama 3, Mistral Large, and GPT4All offer strong capabilities for complex reasoning tasks. Llama 3 leads in context size and accuracy, while Mistral Large balances speed and cost effectively.VERDICT
Llama 3 for the best open source reasoning accuracy and large context handling; choose Mistral Large for faster inference with competitive reasoning at lower cost.| Model | Context window | Speed | Cost/1M tokens | Best for | Free tier |
|---|---|---|---|---|---|
| Llama 3.3-70b | 32k tokens | Moderate | Free (open source) | Deep reasoning, large context | Yes |
| Mistral Large Latest | 8k tokens | Fast | Free (open source) | Efficient reasoning, lower latency | Yes |
| GPT4All-J | 4k tokens | Fast | Free (open source) | Lightweight reasoning, local use | Yes |
| Vicuna 13B | 8k tokens | Moderate | Free (open source) | Conversational reasoning | Yes |
Key differences
Llama 3 offers the largest context window (up to 32k tokens) and excels in complex multi-step reasoning tasks due to its size and training. Mistral Large is optimized for speed and efficiency, making it suitable for latency-sensitive applications with solid reasoning. GPT4All is lightweight and designed for local deployment but has a smaller context window and less reasoning depth.
Side-by-side example: multi-step reasoning with Llama 3
This example shows how to prompt Llama 3.3-70b for a multi-step reasoning task using the vLLM Python library.
from vllm import LLM, SamplingParams
llm = LLM(model="meta-llama/Llama-3.3-70b")
prompt = "You are a reasoning assistant. Explain step-by-step how to solve: If a train travels 60 miles in 1.5 hours, what is its average speed?"
outputs = llm.generate([prompt], SamplingParams(temperature=0))
print(outputs[0].outputs[0].text)Step 1: Identify the distance traveled: 60 miles. Step 2: Identify the time taken: 1.5 hours. Step 3: Calculate average speed = distance / time = 60 / 1.5 = 40 miles per hour. Answer: The average speed is 40 miles per hour.
Equivalent example: reasoning with Mistral Large
Using Mistral Large for the same reasoning task with the OpenAI-compatible SDK.
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
response = client.chat.completions.create(
model="mistral-large-latest",
messages=[{"role": "user", "content": "Explain step-by-step how to solve: If a train travels 60 miles in 1.5 hours, what is its average speed?"}]
)
print(response.choices[0].message.content)Step 1: Determine the distance traveled, which is 60 miles. Step 2: Determine the time taken, which is 1.5 hours. Step 3: Calculate average speed by dividing distance by time: 60 ÷ 1.5 = 40 miles per hour. Therefore, the average speed of the train is 40 miles per hour.
When to use each
Llama 3 is best for applications requiring deep, multi-step reasoning and large context windows, such as research assistants or complex document analysis. Mistral Large suits real-time applications needing faster responses with good reasoning, like chatbots or interactive agents. GPT4All fits offline or privacy-sensitive use cases with modest reasoning needs.
| Model | Best use case | Context window | Latency | Deployment |
|---|---|---|---|---|
| Llama 3.3-70b | Complex reasoning, large documents | 32k tokens | Moderate | Cloud or powerful local GPU |
| Mistral Large Latest | Fast interactive reasoning | 8k tokens | Low | Cloud or edge devices |
| GPT4All-J | Local, privacy-focused | 4k tokens | Low | Local CPU/GPU |
| Vicuna 13B | Conversational agents | 8k tokens | Moderate | Cloud or local |
Pricing and access
All models listed are fully open source and free to use, but hardware costs vary. Llama 3 requires high-end GPUs for best performance, while Mistral Large and GPT4All can run on more modest hardware. Cloud providers may offer hosted versions with usage-based pricing.
| Option | Free | Paid | API access |
|---|---|---|---|
| Llama 3.3-70b | Yes (open source) | No direct cost, hardware required | Available via vLLM and third-party APIs |
| Mistral Large Latest | Yes (open source) | No direct cost, hardware required | Available via OpenAI-compatible APIs |
| GPT4All-J | Yes (open source) | No direct cost, hardware required | Local only, no official API |
| Vicuna 13B | Yes (open source) | No direct cost, hardware required | Community APIs available |
Key Takeaways
- Use
Llama 3for the most accurate and large-context open source reasoning tasks. -
Mistral Largeoffers a strong speed-to-accuracy ratio for latency-sensitive reasoning. -
GPT4Allis ideal for local, privacy-focused deployments with lighter reasoning needs.