DeepSeek V3.2 vs Llama 3.2 1B Instruct (Comparative Analysis)
Loading comparison form...
Comparative Analysis: DeepSeek V3.2 vs. Llama 3.2 1B Instruct
Want to try out these models side by side?Try Magica for free
Overview
Llama 3.2 1B Instruct was released 1 year before DeepSeek V3.2.
 DeepSeek V3.2 |  Llama 3.2 1B Instruct | |
|---|---|---|
Model Provider The organization behind this AI's development | DeepSeek | Meta |
Input Context Window Maximum input tokens this model can process at once | 163.8K tokens | 60K tokens |
Output Token Limit Maximum output tokens this model can generate at once | 65.5K tokens | 60K tokens |
Release Date When this model first became publicly available | December 1, 2025 7 months ago December 1st, 2025 | September 25, 2024 1 year ago September 25th, 2024 |
Knowledge Cutoff Latest training-data date reported by the provider | Not reported | December 31, 2023 |
Capabilities & Features
Compare supported features, modalities, and advanced capabilities
DeepSeek V3.2 | Llama 3.2 1B Instruct | |
|---|---|---|
Input Types Supported input formats | 📝Text | 📝Text |
Reasoning Controls Defaults and configurable reasoning effort | Optional | Not reported |
Output Types Supported output formats | 📝Text | 📝Text |
Tokenizer Text encoding system | DeepSeek | Llama3 |
Key Features Advanced capabilities | ✓Function Calling✓Structured Output✓Reasoning ModeContent Moderation | Function CallingStructured OutputReasoning ModeContent Moderation |
Open Source Model availability | Available on HuggingFace → | Available on HuggingFace → |
Pricing
DeepSeek V3.2 is roughly 9.0x more expensive compared to Llama 3.2 1B Instruct for input tokens and roughly 2.0x more expensive for output tokens.
| DeepSeek V3.2 | Llama 3.2 1B Instruct | |
|---|---|---|
Input Token Cost Cost per million input tokens | $0.27 per million tokens | $0.03 per million tokens |
Output Token Cost Cost per million output tokens | $0.40 per million tokens | $0.20 per million tokens |
Cache Read Cost Cost to reuse cached input tokens | $0.13 per million tokens | Not specified |
Benchmarks
Compare relevant benchmarks between DeepSeek V3.2 and Llama 3.2 1B Instruct.
| DeepSeek V3.2 | Llama 3.2 1B Instruct | |
|---|---|---|
Intelligence Index Overall model quality across independent evaluations | 32 (Artificial Analysis index; higher is better) | Benchmark not available. |
Coding Index Programming performance across independent evaluations | 44.2 (Artificial Analysis index; higher is better) | Benchmark not available. |
Agentic Index Ability to complete multi-step agentic tasks | 18.3 (Artificial Analysis index; higher is better) | Benchmark not available. |
Best Design Arena Score Highest human-preference Elo score across design arenas | 1,199 Elo (#55 in Website (50.2% win rate)) | Benchmark not available. |
At a Glance
Quick overview of what makes DeepSeek V3.2 and Llama 3.2 1B Instruct unique.
DeepSeek V3.2 by DeepSeek can use external tools and APIs, offers advanced reasoning, generates structured data. It can handle standard conversations with its 163.8K token context window. Very affordable at $0.27/M input and $0.40/M output tokens. Released December 1st, 2025.Llama 3.2 1B Instruct by Meta is a text-based AI model. It can handle standard conversations with its 60K token context window. Very affordable at $0.03/M input and $0.20/M output tokens. Released September 25th, 2024.Explore More Comparisons
Compare your models with top performers across different categories
Compare DeepSeek V3.2 with:
🚀Programming
Best models for coding and development
🎨Creative & Roleplay
Models optimized for creative writing
📢Marketing
Content creation and marketing tasks
💻Technology
Technical analysis and explanations
🔬Science
Scientific research and analysis
🌐Translation
Multilingual translation tasks
Compare Llama 3.2 1B Instruct with:
🚀Programming
Best models for coding and development
🎨Creative & Roleplay
Models optimized for creative writing
📢Marketing
Content creation and marketing tasks
💻Technology
Technical analysis and explanations
🔬Science
Scientific research and analysis
🌐Translation
Multilingual translation tasks