Language models > Llama 4 vs. Cohere Command X
Selecting the right large language model (LLM) is crucial for achieving optimal performance in various applications. Llama 4 and Cohere Command X represent distinct approaches to LLM design, catering to different priorities and use cases.
This comparison analyzes real-world performance, user feedback, and key differentiators to help you make an informed decision.
Quick Verdict
The choice hinges on your specific needs: Llama 4 excels with large context requirements and extensive customization, while Cohere Command X provides a more user-friendly experience and cost-effective solution for business applications.
- Llama 4 boasts a significantly larger context window, especially the Scout version, making it suitable for processing extensive documents.
- Cohere Command X offers a more accessible entry point with a free tier and lower hardware requirements.
- Both models support finetuning, but Llama 4's open-source nature facilitates broader customization.
Who Should Choose Which?
Choose Llama 4 if:
Organizations needing to process extremely long documents or requiring extensive customization through open-source finetuning.
Choose Cohere Command X if:
Businesses seeking a readily accessible, multilingual model optimized for business communications and deployable on fewer GPUs.
Key features – Side-by-Side
| Attribute | Llama 4 | Cohere Command X |
|---|---|---|
| Context Window Length | Llama 4 Scout: 10 million tokens, Llama 4 Maverick: 1 million tokens | 256K tokens |
| Finetuning Capabilities | Enables open source fine-tuning efforts by pre-training on 200 languages. Fine-tuning can adapt Llama 4 to specific datasets and application scenarios. | Offers T-Few and n-layer ("vanilla") finetuning. T-Few is parameter-efficient, introducing additional layers. Vanilla finetuning updates the last 25% of the baseline model weights. |
| Multilingual Support | Pre-trained on 200 languages, supports 12 languages in detail. | Supports 23 languages |
| Coding Proficiency | Llama 4 Maverick achieves 43.4% pass@1 on LiveCodeBench. | Excels in SQL-based queries |
| Reasoning Ability | Llama 4 Maverick scores 80.5% on MMLU Pro and 69.8% on GPQA Diamond. | Designed for complex reasoning tasks in business settings |
| Hallucination Rate | Demonstrates improved accuracy and processing speed while minimizing misleading information. | Cohere Command-R has a hallucination rate of 3.9% according to Vectara's HHEM. |
| API Availability and Pricing | Llama 4 Maverick can be served at $0.30 - $0.49/Mtok (3:1 blended) on a single host and $0.19/Mtok (3:1 blended) assuming distributed inference. | Available through Cohere API. Offers a free tier for learning and prototyping. Production tier pricing is based on input and output tokens. |
| Speed of Inference | On the Blackwell B200 GPU, TensorRT-LLM delivers a throughput of over 40K tokens per second with an NVIDIA-optimized FP8 version of Llama 4 Scout as well as over 30K tokens per second on Llama 4 Maverick. Cerebras regularly delivers over 2,500 TPS/user. | Can deliver tokens at a rate of up to 156 tokens/sec. |
| Memory Requirements | Llama 4 Scout (109B): A 4-bit quantized version requires ~55-60GB VRAM just for weights, plus KV cache overhead. Llama 4 Maverick (400B): Requires distributed inference across multiple powerful accelerators. | Can run on two GPUs (A100s or H100s). |
Overall Comparison
Pros and Cons
Llama 4
- Long context window (up to 10M tokens for Llama 4 Scout)
- Strong multilingual support (pre-trained on 200 languages)
- Good coding proficiency (43.4% pass@1 on LiveCodeBench for Llama 4 Maverick)
- Effective reasoning ability (80.5% on MMLU Pro and 69.8% on GPQA Diamond for Llama 4 Maverick)
- Minimizes hallucinations
- Customizable through fine-tuning
- Available on Hugging Face
- High memory requirements (55-60GB VRAM for Llama 4 Scout)
- Llama 4 Maverick requires distributed inference
Cohere Command X
- Extensive context window ideal for document-heavy workflows and complex agent tasks.
- Finetuning is faster and more cost-efficient than building from scratch.
- Finetuning is up to 15x more affordable than other industry-leading models.
- Optimized for multilingual business communications and translation.
- Advanced RAG capabilities with verifiable citations.
- Deployable on just two GPUs (A100s or H100s).
- No major disadvantages reported.
User Experiences and Feedback
Llama 4
- No highlights reported.
- No major complaints reported.
- No value feedback reported.
Cohere Command X
- No highlights reported.
- No major complaints reported.
- No value feedback reported.
Frequently Asked Questions
Which model is easier to deploy?
Cohere Command X, as it can run on fewer GPUs and offers a managed API.
Which model is better for multilingual tasks?
Both offer strong multilingual support, but Llama 4 is pre-trained on a larger number of languages.
Sources & Citations
- Official product specifications
- Expert reviews from tech publications
- User feedback from online forums
Information gathered through AI-assisted web search and analysis. Last updated: September 2025
Methodology & Transparency
Our comparison methodology combines multiple data sources to provide comprehensive, unbiased analysis:
- Data Collection: We gather information from official specifications, user reviews, and independent testing
- AI-Assisted Analysis: Advanced AI helps process large amounts of data while maintaining accuracy
- Human Oversight: All comparisons are reviewed for accuracy and relevance
- Regular Updates: Content is refreshed to reflect new information and user feedback
- Bias Mitigation: We strive for objectivity by considering multiple perspectives and sources
Versusly.ai uses AI-assisted content generation combined with human oversight to deliver comprehensive comparisons. We are transparent about our process and continuously work to improve accuracy and usefulness.