Model Comparison

Llama 3.1 70B Instruct vs Qwen2.5-Coder 7B Instruct

Llama 3.1 70B Instruct shows notably better performance in the majority of benchmarks.

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

Llama 3.1 70B Instruct outperforms in 3 benchmarks (ARC-C, MMLU, MMLU-Pro), while Qwen2.5-Coder 7B Instruct is better at 1 benchmark (HumanEval).

Llama 3.1 70B Instruct shows notably better performance in the majority of benchmarks.

Wed Apr 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Wed Apr 15 2026 • llm-stats.com
Meta
Llama 3.1 70B Instruct
Input tokens$0.20
Output tokens$0.20
Best providerLambda
Alibaba Cloud / Qwen Team
Qwen2.5-Coder 7B Instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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Model Size

Parameter count comparison

63.0B diff

Llama 3.1 70B Instruct has 63.0B more parameters than Qwen2.5-Coder 7B Instruct, making it 900.0% larger.

Meta
Llama 3.1 70B Instruct
70.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2.5-Coder 7B Instruct
7.0Bparameters
70.0B
Llama 3.1 70B Instruct
7.0B
Qwen2.5-Coder 7B Instruct

Context Window

Maximum input and output token capacity

Only Llama 3.1 70B Instruct specifies input context (128,000 tokens). Only Llama 3.1 70B Instruct specifies output context (128,000 tokens).

Meta
Llama 3.1 70B Instruct
Input128,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen2.5-Coder 7B Instruct
Input- tokens
Output- tokens
Wed Apr 15 2026 • llm-stats.com

License

Usage and distribution terms

Llama 3.1 70B Instruct is licensed under Llama 3.1 Community License, while Qwen2.5-Coder 7B Instruct uses Apache 2.0.

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

Qwen2.5-Coder 7B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Llama 3.1 70B Instruct was released on 2024-07-23, while Qwen2.5-Coder 7B Instruct was released on 2024-09-19.

Qwen2.5-Coder 7B Instruct is 2 months newer than Llama 3.1 70B Instruct.

Llama 3.1 70B Instruct

Jul 23, 2024

1.7 years ago

Qwen2.5-Coder 7B Instruct

Sep 19, 2024

1.6 years ago

1mo 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

Outputs Comparison

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Key Takeaways

Larger context window (128,000 tokens)
Higher ARC-C score (94.8% vs 60.9%)
Higher MMLU score (83.6% vs 67.6%)
Higher MMLU-Pro score (66.4% vs 40.1%)
Alibaba Cloud / Qwen Team

Qwen2.5-Coder 7B Instruct

View details

Alibaba Cloud / Qwen Team

Higher HumanEval score (88.4% vs 80.5%)

Detailed Comparison

AI Model Comparison Table
Feature
Meta
Llama 3.1 70B Instruct
Alibaba Cloud / Qwen Team
Qwen2.5-Coder 7B Instruct

FAQ

Common questions about Llama 3.1 70B Instruct vs Qwen2.5-Coder 7B Instruct

Llama 3.1 70B Instruct shows notably better performance in the majority of benchmarks. Llama 3.1 70B Instruct is made by Meta and Qwen2.5-Coder 7B Instruct is made by Alibaba Cloud / Qwen Team. 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%. Qwen2.5-Coder 7B Instruct scores HumanEval: 88.4%, GSM8k: 83.9%, MBPP: 83.5%, HellaSwag: 76.8%, Winogrande: 72.9%.
Llama 3.1 70B Instruct supports 128K tokens and Qwen2.5-Coder 7B Instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include licensing (Llama 3.1 Community License vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.
Llama 3.1 70B Instruct is developed by Meta and Qwen2.5-Coder 7B Instruct is developed by Alibaba Cloud / Qwen Team.