DeepSeek-V2.5 vs Llama 3.2 90B Instruct
DeepSeek-V2.5 and Llama 3.2 90B Instruct are closely matched at 8.8 and 5.7 on the LLM Stats Score. DeepSeek-V2.5 is 2.1x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V2.5 and Llama 3.2 90B Instruct are closely matched on the overall LLM Stats Score at 8.8 and 5.7.
The models split the 2 individual benchmarks reported for both models evenly.
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.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V2.5
- cost matters — it's about 2.1x cheaper per token
Choose Llama 3.2 90B Instruct
- you process long inputs — it offers a 128,000 token context window
- you want the most recent training data — it shipped Sep 2024
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for DeepSeek-V2.5 · 13 for Llama 3.2 90B Instruct
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.
Human preference
Blind head-to-head votes and playground preference scores
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
Documented input modalities across available providers
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
2.3 years ago
Sep 25, 2024
1.9 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
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.
FAQ
Common questions about DeepSeek-V2.5 vs Llama 3.2 90B Instruct.