Model Comparison
DeepSeek-V3.2 (Thinking) vs Llama 4 MaverickWhich is better in 2026?
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks. Llama 4 Maverick is 1.1x cheaper per token.
Verdict: DeepSeek-V3.2 (Thinking) vs Llama 4 Maverick — which is better?
DeepSeek-V3.2 (Thinking) (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.
DeepSeek-V3.2 (Thinking) outperforms in 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro), while Llama 4 Maverick is better at 0 benchmarks. DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
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 (Thinking) if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
- 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 (Thinking) outperforms in 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro), while Llama 4 Maverick is better at 0 benchmarks.
DeepSeek-V3.2 (Thinking) significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.2 (Thinking) ($0.28/1M tokens) is 1.6x more expensive than Llama 4 Maverick ($0.17/1M tokens).
For output processing, DeepSeek-V3.2 (Thinking) ($0.42/1M tokens) is 1.4x cheaper than Llama 4 Maverick ($0.60/1M tokens).
In conclusion, DeepSeek-V3.2 (Thinking) 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 (Thinking) 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 (Thinking)'s 131,072 tokens. Llama 4 Maverick can generate longer responses up to 1,000,000 tokens, while DeepSeek-V3.2 (Thinking) is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Llama 4 Maverick supports multimodal inputs, whereas DeepSeek-V3.2 (Thinking) 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 (Thinking)
Llama 4 Maverick
License
Usage and distribution terms
DeepSeek-V3.2 (Thinking) 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 (Thinking) was released on 2025-12-01, while Llama 4 Maverick was released on 2025-04-05.
DeepSeek-V3.2 (Thinking) is 8 months newer than Llama 4 Maverick.
Dec 1, 2025
7 months ago
8mo newerApr 5, 2025
1.3 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 (Thinking) is available from DeepSeek. Llama 4 Maverick is available from DeepInfra, Novita, Lambda, Groq, Fireworks, Together, Sambanova.
DeepSeek-V3.2 (Thinking)
Llama 4 Maverick
Outputs Comparison
Key Takeaways
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V3.2 (Thinking) and Llama 4 Maverick side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V3.2 (Thinking) vs Llama 4 Maverick.