Turing NLG vs Gopher
Comprehensive side-by-side comparison of pricing, performance benchmarks, and capabilities
At a Glance
Best Overall Performance
Gopher
Higher overall benchmarks
Best for Coding
Gopher
88% coding score
Best for Reasoning
Gopher
90% reasoning score
Best MMLU Score
Gopher
90.5% general knowledge
Compare Different Models
Detailed Comparison
| Feature | Turing NLG | Gopher | Winner |
|---|---|---|---|
| Provider | Microsoft | DeepMind | — |
| Context Window | 128k | 64k | — |
|
MMLU Score
General knowledge & reasoning | 87% | 90.5% | Gopher |
|
Coding Score
Code generation & debugging | 86% | 88% | Gopher |
|
Reasoning Score
Logic & problem-solving | 87.2% | 90% | Gopher |
| Release Date | 2025 | 2024 | — |
| Vision Support | ✓ Yes | — | — |
| Function Calling | ✓ Yes | ✓ Yes | — |
Performance Comparison
MMLU (General Knowledge)
Difference: 3.5%Coding Performance
Difference: 2.0%Reasoning & Logic
Difference: 2.8%Expert Analysis
Performance Analysis
Gopher outperforms across 3 of 3 benchmarks, with particularly strong coding abilities (88%).
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. Organizations with demanding workloads will benefit from Gopher's capabilities for routine and specialized tasks.
Our Recommendation
Choose Gopher 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
Turing NLG Excels At:
- Office assistant AI
- Enterprise chatbots
- Document summarization
- Email drafting AI
- Knowledge management
Gopher Excels At:
- Scientific research assistant
- Academic content generation
- High-level reasoning tasks
- Research document summarization
- Knowledge discovery
Strengths & Weaknesses
Turing NLG
Strengths
- • Enterprise integration
- • Good reasoning
- • Office productivity synergy
- • Azure cloud support
Considerations
- • Premium pricing
- • Closed weights
- • Limited open-source tooling
- • Large model deployment complexity
Gopher
Strengths
- • High reasoning accuracy
- • Strong academic knowledge
- • Open research integration
- • Scientific domain capabilities
Considerations
- • Limited multimodal support
- • Shorter context
- • Not fully enterprise-ready
- • Closed ecosystem for deployment
Frequently Asked Questions
Which is better: Turing NLG or Gopher?
Gopher 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?
Gopher leads in overall performance with higher benchmark scores, while Turing NLG 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?
Gopher leads in coding performance with a score of 88%, making it 2.0 percentage points better than Turing NLG. This makes Gopher 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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