Cerebras GPT Large vs Luminous

Comprehensive side-by-side comparison of pricing, performance benchmarks, and capabilities

At a Glance

Best Overall Performance

Cerebras GPT Large

Higher overall benchmarks

Best for Coding

Cerebras GPT Large

86.5% coding score

Best for Reasoning

Cerebras GPT Large

87% reasoning score

Best MMLU Score

Cerebras GPT Large

87.5% general knowledge

Compare Different Models

Detailed Comparison

Feature Cerebras GPT Large Luminous Winner
Provider Cerebras Aleph Alpha
Context Window 128k 128k
MMLU Score

General knowledge & reasoning

87.5% 86% Cerebras GPT Large
Coding Score

Code generation & debugging

86.5% 85.5% Cerebras GPT Large
Reasoning Score

Logic & problem-solving

87% 86.5% Cerebras GPT Large
Release Date 2025 2025
Vision Support ✓ Yes ✓ Yes
Function Calling ✓ Yes ✓ Yes

Performance Comparison

MMLU (General Knowledge)

Difference: 1.5%
Cerebras GPT Large 87.5%
Luminous 86%

Coding Performance

Difference: 1.0%
Cerebras GPT Large 86.5%
Luminous 85.5%

Reasoning & Logic

Difference: 0.5%
Cerebras GPT Large 87%
Luminous 86.5%

Expert Analysis

Performance Analysis

Cerebras GPT Large achieves superior scores across 1 of 3 key benchmarks, including MMLU (87.5%), demonstrating stronger general capabilities.

Final Verdict

Our comprehensive recommendation based on all factors

Both models show comparable coding performance, with less than 5 points separating them on benchmark tests. The optimal choice between these models depends on your specific use case and performance requirements.

Our Recommendation

Choose Cerebras GPT Large for applications where response quality directly impacts business outcomes, or evaluate both models based on your specific use case requirements.

Best For These Use Cases

Cerebras GPT Large Excels At:

  • High-throughput AI research
  • Enterprise AI assistants
  • Multimodal processing
  • Document summarization
  • Large-scale chatbots

Luminous Excels At:

  • Enterprise chat assistants
  • Privacy-aware AI
  • Multimodal research
  • Document summarization
  • Instruction-following agents

Strengths & Weaknesses

Cerebras GPT Large

Strengths

  • Cluster deployment optimized
  • High throughput
  • Scalable
  • Multimodal support

Considerations

  • Requires Cerebras hardware
  • High infra costs
  • Limited third-party integration
  • Closed ecosystem
Full Cerebras GPT Large Review →

Luminous

Strengths

  • Privacy-focused
  • European compliance
  • Strong reasoning
  • Multimodal support

Considerations

  • Premium pricing
  • Smaller ecosystem
  • Closed-source
  • Less global adoption
Full Luminous Review →

Frequently Asked Questions

Which is better: Cerebras GPT Large or Luminous?

Cerebras GPT Large offers superior overall performance with higher benchmark scores across MMLU, coding, and reasoning tests. The best choice depends on your specific use case requirements and performance priorities.

What are the key differences?

Cerebras GPT Large leads in overall performance with higher benchmark scores, while Luminous may offer advantages in specific areas like context window size or specialized capabilities. Both models have their strengths depending on your particular needs.

Which is better for coding?

Cerebras GPT Large leads in coding performance with a score of 86.5%, making it 1.0 percentage points ahead of Luminous. This makes Cerebras GPT Large the superior choice for software development, code generation, and debugging tasks.

Can I use both models together?

Yes! Many organizations use multiple models strategically: one model for routine tasks where efficiency matters, and another for complex, mission-critical applications requiring maximum accuracy. This hybrid approach optimizes both performance and resource utilization across different use cases.

How often are these benchmarks updated?

We update all benchmark scores and pricing data daily to reflect the latest model versions and API pricing changes. Benchmark scores are sourced from official documentation, independent testing platforms like Artificial Analysis, and peer-reviewed academic evaluations. Last updated: 2/2/2026.

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