Model Comparison
DeepSeek-V3.2-Speciale vs Llama 4 MaverickWhich is better in 2026?
Comparing DeepSeek-V3.2-Speciale and Llama 4 Maverick across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-V3.2-Speciale vs Llama 4 Maverick — which is better?
DeepSeek-V3.2-Speciale (by DeepSeek) and Llama 4 Maverick (by Meta) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
On price, Llama 4 Maverick is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Llama 4 Maverick also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V3.2-Speciale if…
- you want the most recent training data — it shipped Dec 2025
Choose Llama 4 Maverick if…
- cost matters — it's about 1.1x cheaper per token
- you process long inputs — it offers a 1,000,000 token context window
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.2-Speciale and Llama 4 Maverick don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2-Speciale ($0.28/1M tokens) is 1.6x more expensive than Llama 4 Maverick ($0.17/1M tokens).
For output processing, DeepSeek-V3.2-Speciale ($0.42/1M tokens) is 1.4x cheaper than Llama 4 Maverick ($0.60/1M tokens).
In conclusion, DeepSeek-V3.2-Speciale is more expensive than Llama 4 Maverick.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.2-Speciale has 285.0B more parameters than Llama 4 Maverick, making it 71.3% larger.
Context Window
Maximum input and output token capacity
Llama 4 Maverick accepts 1,000,000 input tokens compared to DeepSeek-V3.2-Speciale's 131,072 tokens. Llama 4 Maverick can generate longer responses up to 1,000,000 tokens, while DeepSeek-V3.2-Speciale is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Llama 4 Maverick supports multimodal inputs, whereas DeepSeek-V3.2-Speciale does not.
Llama 4 Maverick can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.2-Speciale
Llama 4 Maverick
License
Usage and distribution terms
DeepSeek-V3.2-Speciale is licensed under MIT, while Llama 4 Maverick uses Llama 4 Community License Agreement.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Llama 4 Community License Agreement
Open weights
Release Timeline
When each model was launched
DeepSeek-V3.2-Speciale was released on 2025-12-01, while Llama 4 Maverick was released on 2025-04-05.
DeepSeek-V3.2-Speciale is 8 months newer than Llama 4 Maverick.
Dec 1, 2025
6 months ago
8mo newerApr 5, 2025
1.2 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V3.2-Speciale is available from DeepSeek. Llama 4 Maverick is available from DeepInfra, Novita, Lambda, Groq, Fireworks, Together, Sambanova.
DeepSeek-V3.2-Speciale
Llama 4 Maverick
Outputs Comparison
Key Takeaways
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.2-Speciale vs Llama 4 Maverick.