Model Comparison

DeepSeek-V3 vs Llama 3.1 70B Instruct

DeepSeek-V3 significantly outperforms across most benchmarks. Llama 3.1 70B Instruct is 2.4x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

DeepSeek-V3 outperforms in 4 benchmarks (DROP, GPQA, MMLU, MMLU-Pro), while Llama 3.1 70B Instruct is better at 1 benchmark (IFEval).

DeepSeek-V3 significantly outperforms across most benchmarks.

Tue Apr 14 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Llama 3.1 70B Instruct costs less

For input processing, DeepSeek-V3 ($0.27/1M tokens) is 1.4x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

For output processing, DeepSeek-V3 ($1.10/1M tokens) is 5.5x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

In conclusion, DeepSeek-V3 is more expensive than Llama 3.1 70B Instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Apr 14 2026 • llm-stats.com
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
Meta
Llama 3.1 70B Instruct
Input tokens$0.20
Output tokens$0.20
Best providerLambda
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Model Size

Parameter count comparison

601.0B diff

DeepSeek-V3 has 601.0B more parameters than Llama 3.1 70B Instruct, making it 858.6% larger.

DeepSeek
DeepSeek-V3
671.0Bparameters
Meta
Llama 3.1 70B Instruct
70.0Bparameters
671.0B
DeepSeek-V3
70.0B
Llama 3.1 70B Instruct

Context Window

Maximum input and output token capacity

DeepSeek-V3 accepts 131,072 input tokens compared to Llama 3.1 70B Instruct's 128,000 tokens. DeepSeek-V3 can generate longer responses up to 131,072 tokens, while Llama 3.1 70B Instruct is limited to 128,000 tokens.

DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Meta
Llama 3.1 70B Instruct
Input128,000 tokens
Output128,000 tokens
Tue Apr 14 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), 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-V3

MIT + Model License (Commercial use allowed)

Open weights

Llama 3.1 70B Instruct

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 70B Instruct was released on 2024-07-23.

DeepSeek-V3 is 5 months newer than Llama 3.1 70B Instruct.

DeepSeek-V3

Dec 25, 2024

1.3 years ago

5mo newer
Llama 3.1 70B Instruct

Jul 23, 2024

1.7 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

DeepSeek-V3 is available from DeepSeek. Llama 3.1 70B Instruct is available from Lambda, DeepInfra, Hyperbolic, Groq, Cerebras, Together, Fireworks, Bedrock, Sambanova.

DeepSeek-V3

deepseek logo
DeepSeek
Input Price:Input: $0.27/1MOutput Price:Output: $1.10/1M

Llama 3.1 70B Instruct

lambda logo
Lambda
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.35/1MOutput Price:Output: $0.40/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $0.40/1MOutput Price:Output: $0.40/1M
groq logo
Groq
Input Price:Input: $0.59/1MOutput Price:Output: $0.78/1M
cerebras logo
Cerebras
Input Price:Input: $0.60/1MOutput Price:Output: $0.60/1M
together logo
Together
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
fireworks logo
Fireworks
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
bedrock logo
AWS Bedrock
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
sambanova logo
Sambanova
Input Price:Input: $5.00/1MOutput Price:Output: $10.00/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Higher DROP score (91.6% vs 79.6%)
Higher GPQA score (59.1% vs 41.7%)
Higher MMLU score (88.5% vs 83.6%)
Higher MMLU-Pro score (75.9% vs 66.4%)
Meta
Less expensive input tokens
Less expensive output tokens
Higher IFEval score (87.5% vs 86.1%)

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3
Meta
Llama 3.1 70B Instruct

FAQ

Common questions about DeepSeek-V3 vs Llama 3.1 70B Instruct

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V3 is made by DeepSeek and Llama 3.1 70B Instruct is made by Meta. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%. Llama 3.1 70B Instruct scores GSM-8K (CoT): 95.1%, ARC-C: 94.8%, API-Bank: 90.0%, IFEval: 87.5%, Multilingual MGSM (CoT): 86.9%.
Llama 3.1 70B Instruct is 1.4x cheaper for input tokens. DeepSeek-V3 costs $0.27/M input and $1.10/M output via deepseek. Llama 3.1 70B Instruct costs $0.20/M input and $0.20/M output via lambda.
DeepSeek-V3 supports 131K tokens and Llama 3.1 70B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (131K vs 128K), input pricing ($0.27 vs $0.20/M), licensing (MIT + Model License (Commercial use allowed) vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3 is developed by DeepSeek and Llama 3.1 70B Instruct is developed by Meta.