DeepSeek-V2.5 vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct leads the LLM Stats Score 13.8 to 8.1. Llama 3.3 70B Instruct is 1.4x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
Llama 3.3 70B Instruct leads the overall LLM Stats Score 13.8 to 8.1, ranking #241 overall.
In the 3 individual benchmarks reported for both models, Llama 3.3 70B Instruct wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Llama 3.3 70B Instruct is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Llama 3.3 70B Instruct also accepts a larger context window (131,072 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
- you want predictable pricing at $0.14/M input and $0.28/M output
Choose Llama 3.3 70B Instruct
- overall performance matters — it scores 13.8 and ranks #241 on LLM Stats
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- cost matters — it's about 1.4x cheaper per token
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Dec 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 · 9 for Llama 3.3 70B Instruct
DeepSeek-V2.5 outperforms in 1 benchmarks (HumanEval), while Llama 3.3 70B Instruct is better at 2 benchmarks (MATH, MMLU).
Llama 3.3 70B Instruct shows notably better performance in the majority of 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 more expensive than Llama 3.3 70B Instruct ($0.10/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 1.4x more expensive than Llama 3.3 70B Instruct ($0.20/1M tokens).
In conclusion, DeepSeek-V2.5 is more expensive than Llama 3.3 70B Instruct.*
* 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 3.3 70B Instruct, making it 237.1% larger.
Context Window
Maximum input and output token capacity
Llama 3.3 70B Instruct accepts 131,072 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Llama 3.3 70B Instruct can generate longer responses up to 131,072 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 3.3 70B Instruct uses Llama 3.3 Community License Agreement.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Llama 3.3 Community License Agreement
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Llama 3.3 70B Instruct was released on 2024-12-06.
Llama 3.3 70B Instruct is 7 months newer than DeepSeek-V2.5.
May 8, 2024
2.3 years ago
Dec 6, 2024
1.8 years ago
7mo 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 3.3 70B Instruct is available from DeepInfra, Lambda, Hyperbolic, Groq, Sambanova, Cerebras, Bedrock, Together, Fireworks.
DeepSeek-V2.5
Llama 3.3 70B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Llama 3.3 70B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Llama 3.3 70B Instruct.