Grok-1.5 vs Qwen2.5-Coder 32B Instruct Comparison

Comparing Grok-1.5 and Qwen2.5-Coder 32B Instruct across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

5 benchmarks

Grok-1.5 outperforms in 2 benchmarks (MMLU, MMLU-Pro), while Qwen2.5-Coder 32B Instruct is better at 3 benchmarks (GSM8k, HumanEval, MATH).

Qwen2.5-Coder 32B Instruct has a slight edge in benchmark performance.

Tue Mar 17 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 17 2026 • llm-stats.com
xAI
Grok-1.5
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Alibaba Cloud / Qwen Team
Qwen2.5-Coder 32B Instruct
Input tokens$0.09
Output tokens$0.09
Best providerLambda
Notice missing or incorrect data?Start an Issue

Context Window

Maximum input and output token capacity

Only Qwen2.5-Coder 32B Instruct specifies input context (128,000 tokens). Only Qwen2.5-Coder 32B Instruct specifies output context (128,000 tokens).

xAI
Grok-1.5
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
Qwen2.5-Coder 32B Instruct
Input128,000 tokens
Output128,000 tokens
Tue Mar 17 2026 • llm-stats.com

License

Usage and distribution terms

Grok-1.5 is licensed under a proprietary license, while Qwen2.5-Coder 32B Instruct uses Apache 2.0.

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

Grok-1.5

Proprietary

Closed source

Qwen2.5-Coder 32B Instruct

Apache 2.0

Open weights

Release Timeline

When each model was launched

Grok-1.5 was released on 2024-03-28, while Qwen2.5-Coder 32B Instruct was released on 2024-09-19.

Qwen2.5-Coder 32B Instruct is 6 months newer than Grok-1.5.

Grok-1.5

Mar 28, 2024

2.0 years ago

Qwen2.5-Coder 32B Instruct

Sep 19, 2024

1.5 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

Outputs Comparison

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Key Takeaways

Higher MMLU score (81.3% vs 75.1%)
Higher MMLU-Pro score (51.0% vs 50.4%)
Larger context window (128,000 tokens)
Has open weights
Higher GSM8k score (91.1% vs 90.0%)
Higher HumanEval score (92.7% vs 74.1%)
Higher MATH score (57.2% vs 50.6%)

Detailed Comparison

AI Model Comparison Table
Feature
xAI
Grok-1.5
Alibaba Cloud / Qwen Team
Qwen2.5-Coder 32B Instruct