Model Comparison
DeepSeek-V3.1 vs Llama 4 MaverickWhich is better in 2026?
DeepSeek-V3.1 significantly outperforms across most benchmarks. Llama 4 Maverick is 1.6x cheaper per token.
Verdict: DeepSeek-V3.1 vs Llama 4 Maverick — which is better?
DeepSeek-V3.1 (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-V3.1 outperforms in 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro), while Llama 4 Maverick is better at 0 benchmarks. DeepSeek-V3.1 significantly outperforms across most benchmarks.
On price, Llama 4 Maverick is roughly 1.6x 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-V3.1 if…
- you want the strongest raw capability — it leads on 3 of 3 shared benchmarks
Choose Llama 4 Maverick if…
- cost matters — it's about 1.6x cheaper per token
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Apr 2025
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V3.1 outperforms in 3 benchmarks (GPQA, LiveCodeBench, MMLU-Pro), while Llama 4 Maverick is better at 0 benchmarks.
DeepSeek-V3.1 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3.1 ($0.27/1M tokens) is 1.6x more expensive than Llama 4 Maverick ($0.17/1M tokens).
For output processing, DeepSeek-V3.1 ($1.00/1M tokens) is 1.7x more expensive than Llama 4 Maverick ($0.60/1M tokens).
In conclusion, DeepSeek-V3.1 is more expensive than Llama 4 Maverick.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3.1 has 271.0B more parameters than Llama 4 Maverick, making it 67.8% larger.
Context Window
Maximum input and output token capacity
Llama 4 Maverick accepts 1,000,000 input tokens compared to DeepSeek-V3.1's 163,840 tokens. Llama 4 Maverick can generate longer responses up to 1,000,000 tokens, while DeepSeek-V3.1 is limited to 163,840 tokens.
Input Capabilities
Supported data types and modalities
Llama 4 Maverick supports multimodal inputs, whereas DeepSeek-V3.1 does not.
Llama 4 Maverick can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3.1
Llama 4 Maverick
License
Usage and distribution terms
DeepSeek-V3.1 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-V3.1 was released on 2025-01-10, while Llama 4 Maverick was released on 2025-04-05.
Llama 4 Maverick is 3 months newer than DeepSeek-V3.1.
Jan 10, 2025
1.4 years ago
Apr 5, 2025
1.2 years ago
2mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V3.1 is available from DeepInfra, Novita. Llama 4 Maverick is available from DeepInfra, Novita, Lambda, Groq, Fireworks, Together, Sambanova.
DeepSeek-V3.1
Llama 4 Maverick
Outputs Comparison
Key Takeaways
DeepSeek-V3.1
View detailsDeepSeek
Detailed Comparison
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FAQ
Common questions about DeepSeek-V3.1 vs Llama 4 Maverick.