DeepSeek-V3 vs Llama 3.1 8B Instruct
DeepSeek-V3 leads the LLM Stats Score 15.6 to -2.6. Llama 3.1 8B Instruct is 18.9x cheaper per token.
DeepSeek · Meta · Updated for 2026
Which is better?
DeepSeek-V3 leads the overall LLM Stats Score 15.6 to -2.6, ranking #236 overall.
In the 5 individual benchmarks reported for both models, DeepSeek-V3 wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, Llama 3.1 8B Instruct is roughly 18.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V3
- overall performance matters — it scores 15.6 and ranks #236 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 5 of 5 exact shared results
- you want the most recent training data — it shipped Dec 2024
Choose Llama 3.1 8B Instruct
- cost matters — it's about 18.9x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V3 · 18 for Llama 3.1 8B Instruct
DeepSeek-V3 outperforms in 5 benchmarks (DROP, GPQA, IFEval, MMLU, MMLU-Pro), while Llama 3.1 8B Instruct is better at 0 benchmarks.
DeepSeek-V3 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3 ($0.27/1M tokens) is 13.5x more expensive than Llama 3.1 8B Instruct ($0.02/1M tokens).
For output processing, DeepSeek-V3 ($0.89/1M tokens) is 29.7x more expensive than Llama 3.1 8B Instruct ($0.03/1M tokens).
In conclusion, DeepSeek-V3 is more expensive than Llama 3.1 8B Instruct.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V3 has 663.0B more parameters than Llama 3.1 8B Instruct, making it 8287.5% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 131,072 tokens. Both models can generate responses up to 131,072 tokens.
License
Usage and distribution terms
DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Llama 3.1 8B Instruct uses Llama 3.1 Community License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT + Model License (Commercial use allowed)
Open weights
Llama 3.1 Community License
Open weights
Release Timeline
When each model was launched
DeepSeek-V3 was released on 2024-12-25, while Llama 3.1 8B Instruct was released on 2024-07-23.
DeepSeek-V3 is 5 months newer than Llama 3.1 8B Instruct.
Dec 25, 2024
1.8 years ago
5mo newerJul 23, 2024
2.2 years ago
Knowledge Cutoff
When training data ends
Llama 3.1 8B Instruct has a documented knowledge cutoff of 2023-12-31, while DeepSeek-V3's cutoff date is not specified.
We can confirm Llama 3.1 8B Instruct's training data extends to 2023-12-31, but cannot make a direct comparison without DeepSeek-V3's cutoff date.
—
Dec 2023
Provider Availability
DeepSeek-V3 is available from DeepSeek, DeepInfra. Llama 3.1 8B Instruct is available from DeepInfra, Lambda, Groq, Sambanova, Cerebras, Hyperbolic, Together, Fireworks, Bedrock.
DeepSeek-V3
Llama 3.1 8B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3 and Llama 3.1 8B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3 vs Llama 3.1 8B Instruct.