Model Comparison

Llama 3.1 70B Instruct vs DeepSeek-V3

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

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

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, Llama 3.1 70B Instruct ($0.20/1M tokens) is 1.4x cheaper than DeepSeek-V3 ($0.27/1M tokens).

For output processing, Llama 3.1 70B Instruct ($0.20/1M tokens) is 5.5x cheaper than DeepSeek-V3 ($1.10/1M 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
Meta
Llama 3.1 70B Instruct
Input tokens$0.20
Output tokens$0.20
Best providerLambda
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
Notice missing or incorrect data?Start an Issue→

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.

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

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.

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

License

Usage and distribution terms

Llama 3.1 70B Instruct is licensed under Llama 3.1 Community License, while DeepSeek-V3 uses MIT + Model License (Commercial use allowed).

License differences may affect how you can use these models in commercial or open-source projects.

Llama 3.1 70B Instruct

Llama 3.1 Community License

Open weights

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Release Timeline

When each model was launched

Llama 3.1 70B Instruct was released on 2024-07-23, while DeepSeek-V3 was released on 2024-12-25.

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

Llama 3.1 70B Instruct

Jul 23, 2024

1.7 years ago

DeepSeek-V3

Dec 25, 2024

1.3 years ago

5mo newer

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

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

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

DeepSeek-V3

deepseek logo
DeepSeek
Input Price:Input: $0.27/1MOutput Price:Output: $1.10/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Meta
Less expensive input tokens
Less expensive output tokens
Higher IFEval score (87.5% vs 86.1%)
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%)

Detailed Comparison

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

FAQ

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

DeepSeek-V3 significantly outperforms across most benchmarks. Llama 3.1 70B Instruct is made by Meta and DeepSeek-V3 is made by DeepSeek. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
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%. 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 is 1.4x cheaper for input tokens. Llama 3.1 70B Instruct costs $0.20/M input and $0.20/M output via lambda. DeepSeek-V3 costs $0.27/M input and $1.10/M output via deepseek.
Llama 3.1 70B Instruct supports 128K tokens and DeepSeek-V3 supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (128K vs 131K), input pricing ($0.20 vs $0.27/M), licensing (Llama 3.1 Community License vs MIT + Model License (Commercial use allowed)). See the full comparison above for benchmark-by-benchmark results.
Llama 3.1 70B Instruct is developed by Meta and DeepSeek-V3 is developed by DeepSeek.