Model Comparison

DeepSeek-V3.2-Exp vs Llama 3.1 70B Instruct

DeepSeek-V3.2-Exp significantly outperforms across most benchmarks. Llama 3.1 70B Instruct is 1.5x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

DeepSeek-V3.2-Exp outperforms in 2 benchmarks (GPQA, MMLU-Pro), while Llama 3.1 70B Instruct is better at 0 benchmarks.

DeepSeek-V3.2-Exp significantly outperforms across most benchmarks.

Wed Apr 29 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-V3.2-Exp ($0.27/1M tokens) is 1.4x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

For output processing, DeepSeek-V3.2-Exp ($0.41/1M tokens) is 2.0x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

In conclusion, DeepSeek-V3.2-Exp 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
Wed Apr 29 2026 • llm-stats.com
DeepSeek
DeepSeek-V3.2-Exp
Input tokens$0.27
Output tokens$0.41
Best providerNovita
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

615.0B diff

DeepSeek-V3.2-Exp has 615.0B more parameters than Llama 3.1 70B Instruct, making it 878.6% larger.

DeepSeek
DeepSeek-V3.2-Exp
685.0Bparameters
Meta
Llama 3.1 70B Instruct
70.0Bparameters
685.0B
DeepSeek-V3.2-Exp
70.0B
Llama 3.1 70B Instruct

Context Window

Maximum input and output token capacity

DeepSeek-V3.2-Exp accepts 163,840 input tokens compared to Llama 3.1 70B Instruct's 128,000 tokens. Llama 3.1 70B Instruct can generate longer responses up to 128,000 tokens, while DeepSeek-V3.2-Exp is limited to 65,536 tokens.

DeepSeek
DeepSeek-V3.2-Exp
Input163,840 tokens
Output65,536 tokens
Meta
Llama 3.1 70B Instruct
Input128,000 tokens
Output128,000 tokens
Wed Apr 29 2026 • llm-stats.com

License

Usage and distribution terms

DeepSeek-V3.2-Exp is licensed under MIT, 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-V3.2-Exp

MIT

Open weights

Llama 3.1 70B Instruct

Llama 3.1 Community License

Open weights

Release Timeline

When each model was launched

DeepSeek-V3.2-Exp was released on 2025-09-29, while Llama 3.1 70B Instruct was released on 2024-07-23.

DeepSeek-V3.2-Exp is 14 months newer than Llama 3.1 70B Instruct.

DeepSeek-V3.2-Exp

Sep 29, 2025

7 months ago

1.2yr newer
Llama 3.1 70B Instruct

Jul 23, 2024

1.8 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-V3.2-Exp is available from Novita. Llama 3.1 70B Instruct is available from Lambda, DeepInfra, Hyperbolic, Groq, Cerebras, Together, Fireworks, Bedrock, Sambanova.

DeepSeek-V3.2-Exp

novita logo
Novita
Input Price:Input: $0.27/1MOutput Price:Output: $0.41/1M

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 (163,840 tokens)
Higher GPQA score (79.9% vs 41.7%)
Higher MMLU-Pro score (85.0% vs 66.4%)
Meta
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-V3.2-Exp
Meta
Llama 3.1 70B Instruct

FAQ

Common questions about DeepSeek-V3.2-Exp vs Llama 3.1 70B Instruct

DeepSeek-V3.2-Exp significantly outperforms across most benchmarks. DeepSeek-V3.2-Exp 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-V3.2-Exp scores SimpleQA: 97.1%, AIME 2025: 89.3%, MMLU-Pro: 85.0%, HMMT 2025: 83.6%, GPQA: 79.9%. 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-V3.2-Exp costs $0.27/M input and $0.41/M output via novita. Llama 3.1 70B Instruct costs $0.20/M input and $0.20/M output via lambda.
DeepSeek-V3.2-Exp supports 164K 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 (164K vs 128K), input pricing ($0.27 vs $0.20/M), licensing (MIT vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-V3.2-Exp is developed by DeepSeek and Llama 3.1 70B Instruct is developed by Meta.