Model Comparison
DeepSeek-V2.5 vs Llama 3.2 90B InstructWhich is better in 2026?
Both models are evenly matched across the benchmarks. DeepSeek-V2.5 is 2.1x cheaper per token.
Verdict: DeepSeek-V2.5 vs Llama 3.2 90B Instruct — which is better?
DeepSeek-V2.5 (by DeepSeek) and Llama 3.2 90B Instruct (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-V2.5 outperforms in 1 benchmarks (MATH), while Llama 3.2 90B Instruct is better at 1 benchmark (MMLU). Both models are evenly matched across the benchmarks.
On price, DeepSeek-V2.5 is roughly 2.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Llama 3.2 90B Instruct also accepts a larger context window (128,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V2.5 if…
- cost matters — it's about 2.1x cheaper per token
Choose Llama 3.2 90B Instruct if…
- you process long inputs — it offers a 128,000 token context window
- you want the most recent training data — it shipped Sep 2024
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V2.5 outperforms in 1 benchmarks (MATH), while Llama 3.2 90B Instruct is better at 1 benchmark (MMLU).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 2.5x cheaper than Llama 3.2 90B Instruct ($0.35/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 1.4x cheaper than Llama 3.2 90B Instruct ($0.40/1M tokens).
In conclusion, Llama 3.2 90B Instruct is more expensive than DeepSeek-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V2.5 has 146.0B more parameters than Llama 3.2 90B Instruct, making it 162.2% larger.
Context Window
Maximum input and output token capacity
Llama 3.2 90B Instruct accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Llama 3.2 90B Instruct can generate longer responses up to 128,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
Input Capabilities
Supported data types and modalities
Llama 3.2 90B Instruct supports multimodal inputs, whereas DeepSeek-V2.5 does not.
Llama 3.2 90B Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
Llama 3.2 90B Instruct
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Llama 3.2 90B Instruct uses Llama 3.2.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Llama 3.2
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Llama 3.2 90B Instruct was released on 2024-09-25.
Llama 3.2 90B Instruct is 5 months newer than DeepSeek-V2.5.
May 8, 2024
2.1 years ago
Sep 25, 2024
1.8 years ago
4mo 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-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic.
DeepSeek-V2.5
Llama 3.2 90B Instruct
Outputs Comparison
Key Takeaways
DeepSeek-V2.5
View detailsDeepSeek
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Llama 3.2 90B Instruct side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V2.5 vs Llama 3.2 90B Instruct.