Grok-1.5 vs Llama 3.3 70B Instruct

Comparing Grok-1.5 by xAI and Llama 3.3 70B Instruct by Meta across benchmarks, pricing, and capabilities.

Llama 3.3 70B Instruct significantly outperforms across most benchmarks.

Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

Grok-1.5 outperforms in 0 benchmarks, while Llama 3.3 70B Instruct is better at 5 benchmarks (GPQA, HumanEval, MATH, MMLU, MMLU-Pro).

Llama 3.3 70B Instruct significantly outperforms across most benchmarks.

Tue Mar 24 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
Tue Mar 24 2026 • llm-stats.com
xAI
Grok-1.5
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Meta
Llama 3.3 70B Instruct
Input tokens$0.20
Output tokens$0.20
Best providerLambda
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Context Window

Maximum input and output token capacity

Only Llama 3.3 70B Instruct specifies input context (128,000 tokens). Only Llama 3.3 70B Instruct specifies output context (128,000 tokens).

xAI
Grok-1.5
Input- tokens
Output- tokens
Meta
Llama 3.3 70B Instruct
Input128,000 tokens
Output128,000 tokens
Tue Mar 24 2026 • llm-stats.com

License

Usage and distribution terms

Grok-1.5 is licensed under a proprietary license, while Llama 3.3 70B Instruct uses Llama 3.3 Community License Agreement.

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

Grok-1.5

Proprietary

Closed source

Llama 3.3 70B Instruct

Llama 3.3 Community License Agreement

Open weights

Release Timeline

When each model was launched

Grok-1.5 was released on 2024-03-28, while Llama 3.3 70B Instruct was released on 2024-12-06.

Llama 3.3 70B Instruct is 8 months newer than Grok-1.5.

Grok-1.5

Mar 28, 2024

2.0 years ago

Llama 3.3 70B Instruct

Dec 6, 2024

1.3 years ago

8mo 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)
Has open weights
Higher GPQA score (50.5% vs 35.9%)
Higher HumanEval score (88.4% vs 74.1%)
Higher MATH score (77.0% vs 50.6%)
Higher MMLU score (86.0% vs 81.3%)
Higher MMLU-Pro score (68.9% vs 51.0%)

Detailed Comparison

AI Model Comparison Table
Feature
xAI
Grok-1.5
Meta
Llama 3.3 70B Instruct