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Llama 3.2 11B Vision 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 3.2 11B Vision and Qwen2.5 Coder 32B Instruct.

Meta

Llama 3.2 11B Vision

Meta's lightweight open weights vision model, optimized for mobile devices and local deployments. Capable of visual understanding, chart reading, and fast text generation.

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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**...

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Technical Specifications

SpecificationLlama 3.2 11B VisionQwen2.5 Coder 32B Instruct
ProviderMetaAlibaba
Context Window131,072 tokens128,000 tokens
Agent Suitability72/100N/A
Time to First Token (TTFT)150 msN/A
Deployment Modelself hostableself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-09-252024-11-11

API Pricing Comparison

Input Price per Million Tokens

Llama 3.2 11B Vision

$0.34

Qwen2.5 Coder 32B Instruct

$0.66

Output Price per Million Tokens

Llama 3.2 11B Vision

$0.34

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
7300.0%vs8120.0%
Llama 3.2 11B Vision
Qwen2.5 Coder 32B Instruct
HumanEvalPython coding & logic synthesis
7500.0%vs9150.0%
Llama 3.2 11B Vision
Qwen2.5 Coder 32B Instruct
MATHComplex mathematical problem solving
5800.0%vs6800.0%
Llama 3.2 11B Vision
Qwen2.5 Coder 32B Instruct
GPQAGraduate-level expert reasoning
3800.0%vs4050.0%
Llama 3.2 11B Vision
Qwen2.5 Coder 32B Instruct
HellaSwagCommonsense reasoning and inference
8200.0%vs8400.0%
Llama 3.2 11B Vision
Qwen2.5 Coder 32B Instruct
MT-BenchMulti-turn conversation flow quality
790.0%vs885.0%
Llama 3.2 11B Vision
Qwen2.5 Coder 32B Instruct

Llama 3.2 11B Vision Quirks & Gotchas

  • โ–ธLightweight vision model for edge/on-device deployments
  • โ–ธLimited tool calling โ€” use Llama 4 for production agentic tasks

Qwen2.5 Coder 32B Instruct Quirks & Gotchas

No developer gotchas reported.