โ† Back to Model Hub/SIDE-BY-SIDE REVIEW
SHARE THIS:

Llama 3.1 8B vs Qwen2.5 Coder 32B Instruct

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Llama 3.1 8B and Qwen2.5 Coder 32B Instruct.

Meta

Llama 3.1 8B

Llama 3.1 8B is Meta's lightweight open-weight model from the Llama 3.1 generation, optimized for efficient deployment on consumer hardware and edge devices. Despite its compact 8-billion-parameter size, it delivers strong performance on instruction following, text summarization, and lightweight coding tasks. Lllama 3.1 8B is the most downloaded model in the Llama family and runs efficiently on laptops, single GPUs, and CPU via quantization โ€” making it the default choice for on-device AI applications and local prototyping.

View Full Specs
Alibaba

Qwen2.5 Coder 32B Instruct

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...

View Full Specs

Technical Specifications

SpecificationLlama 3.1 8BQwen2.5 Coder 32B Instruct
ProviderMetaAlibaba
Context Window131,072 tokens128,000 tokens
Agent Suitability74/100N/A
Time to First Token (TTFT)80 msN/A
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-07-232024-11-11

API Pricing Comparison

Input Price per Million Tokens

Llama 3.1 8B

$0.04

Qwen2.5 Coder 32B Instruct

$0.66

Output Price per Million Tokens

Llama 3.1 8B

$0.04

Qwen2.5 Coder 32B Instruct

$1.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
N/Avs8120.0%
Llama 3.1 8B
Qwen2.5 Coder 32B Instruct
HumanEvalPython coding & logic synthesis
N/Avs9150.0%
Llama 3.1 8B
Qwen2.5 Coder 32B Instruct
MATHComplex mathematical problem solving
N/Avs6800.0%
Llama 3.1 8B
Qwen2.5 Coder 32B Instruct
GPQAGraduate-level expert reasoning
N/Avs4050.0%
Llama 3.1 8B
Qwen2.5 Coder 32B Instruct
HellaSwagCommonsense reasoning and inference
N/Avs8400.0%
Llama 3.1 8B
Qwen2.5 Coder 32B Instruct
MT-BenchMulti-turn conversation flow quality
N/Avs885.0%
Llama 3.1 8B
Qwen2.5 Coder 32B Instruct

Llama 3.1 8B Quirks & Gotchas

  • โ–ธPerfect for CPU/edge deployment โ€” runs on Raspberry Pi with quantization
  • โ–ธLimited tool calling vs larger models โ€” best for simple classification and chat

Qwen2.5 Coder 32B Instruct Quirks & Gotchas

No developer gotchas reported.