DeepSeek-V2.5 vs Llama 3.1 70B Instruct
DeepSeek-V2.5 and Llama 3.1 70B Instruct are closely matched at 8.1 and 7.6 on the LLM Stats Score. DeepSeek-V2.5 is 1.1x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V2.5 and Llama 3.1 70B Instruct are closely matched on the overall LLM Stats Score at 8.1 and 7.6.
The models split the 2 individual benchmarks reported for both models evenly.
On price, DeepSeek-V2.5 is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Llama 3.1 70B 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 1.1x cheaper per token
Choose Llama 3.1 70B Instruct
- you process long inputs — it offers a 128,000 token context window
- you want the most recent training data — it shipped Jul 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 · 18 for Llama 3.1 70B Instruct
DeepSeek-V2.5 outperforms in 1 benchmarks (HumanEval), while Llama 3.1 70B 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 1.4x cheaper than Llama 3.1 70B Instruct ($0.20/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 1.4x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).
In conclusion, Llama 3.1 70B 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 166.0B more parameters than Llama 3.1 70B Instruct, making it 237.1% larger.
Context Window
Maximum input and output token capacity
Llama 3.1 70B Instruct accepts 128,000 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Llama 3.1 70B Instruct can generate longer responses up to 128,000 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Llama 3.1 70B Instruct uses Llama 3.1 Community License.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Llama 3.1 Community License
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Llama 3.1 70B Instruct was released on 2024-07-23.
Llama 3.1 70B Instruct is 3 months newer than DeepSeek-V2.5.
May 8, 2024
2.4 years ago
Jul 23, 2024
2.2 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-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Llama 3.1 70B Instruct is available from Lambda, DeepInfra, Hyperbolic, Groq, Cerebras, Together, Fireworks, Bedrock, Sambanova.
DeepSeek-V2.5
Llama 3.1 70B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Llama 3.1 70B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Llama 3.1 70B Instruct.