Mixtral 16x7B vs StableLM 3B Multimodal

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

At a Glance

Best Overall Performance

Mixtral 16x7B

Higher overall benchmarks

Best for Coding

Mixtral 16x7B

82.5% coding score

Best for Reasoning

Mixtral 16x7B

83% reasoning score

Best MMLU Score

Mixtral 16x7B

83.5% general knowledge

Compare Different Models

Detailed Comparison

Feature Mixtral 16x7B StableLM 3B Multimodal Winner
Provider Mistral AI Stability AI
Context Window 64k 16k
MMLU Score

General knowledge & reasoning

83.5% 76% Mixtral 16x7B
Coding Score

Code generation & debugging

82.5% 74% Mixtral 16x7B
Reasoning Score

Logic & problem-solving

83% 75% Mixtral 16x7B
Release Date 2025 2025
Vision Support ✓ Yes ✓ Yes
Function Calling ✓ Yes ✓ Yes

Performance Comparison

MMLU (General Knowledge)

Difference: 7.5%
Mixtral 16x7B 83.5%
StableLM 3B Multimodal 76%

Coding Performance

Difference: 8.5%
Mixtral 16x7B 82.5%
StableLM 3B Multimodal 74%

Reasoning & Logic

Difference: 8.0%
Mixtral 16x7B 83%
StableLM 3B Multimodal 75%

Expert Analysis

Performance Analysis

Mixtral 16x7B achieves superior scores across 3 of 3 key benchmarks, including coding (82.5%), demonstrating stronger general capabilities.

Final Verdict

Our comprehensive recommendation based on all factors

Mixtral 16x7B demonstrates superior coding capabilities with a 8.5-point advantage, making it the stronger choice for software development tasks. Enterprise teams requiring maximum accuracy should invest in Mixtral 16x7B for demanding workloads and complex tasks.

Our Recommendation

Enterprise teams and applications requiring maximum accuracy should choose Mixtral 16x7B for mission-critical deployments where performance is paramount.

Best For These Use Cases

Mixtral 16x7B Excels At:

  • Self-hosted AI agents
  • High-throughput inference
  • Research experiments
  • Domain-specific fine-tuning
  • Cost-efficient production

StableLM 3B Multimodal Excels At:

  • Multimodal research
  • Prototype AI assistants
  • Creative content
  • Fine-tuning experiments
  • Lightweight agents

Strengths & Weaknesses

Mixtral 16x7B

Strengths

  • Sparse MoE efficiency
  • Open-weight support
  • High inference throughput
  • Fine-tuning flexibility

Considerations

  • Complex MoE management
  • Limited prebuilt tools
  • Closed multimodal roadmap
  • Requires advanced infra
Full Mixtral 16x7B Review →

StableLM 3B Multimodal

Strengths

  • Open weights
  • Multimodal support
  • Lightweight
  • Creative generation

Considerations

  • Limited context
  • Moderate reasoning
  • Smaller model
  • Not enterprise-ready
Full StableLM 3B Multimodal Review →

Frequently Asked Questions

Which is better: Mixtral 16x7B or StableLM 3B Multimodal?

Mixtral 16x7B 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?

Mixtral 16x7B leads in overall performance with higher benchmark scores, while StableLM 3B Multimodal 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?

Mixtral 16x7B leads in coding performance with a score of 82.5%, making it 8.5 percentage points ahead of StableLM 3B Multimodal. This makes Mixtral 16x7B 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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