Both Mistral AI Titan and Llama 4 are powerful language models with distinct strengths. Mistral AI Titan offers flexibility with both open-source and proprietary options, while Llama 4 emphasizes open-source fine-tuning and strong multilingual performance. The choice depends on specific use cases, licensing requirements, and desired inference speed.
| Attribute | Mistral AI Titan | Llama 4 |
|---|---|---|
| Model Size (Number of Parameters) | Varies; Mistral Large 2: 123 billion, Codestral: 22 billion, Mistral Nemo: 12B, Mistral 7B: 7 billion | Llama 4 Scout: 17 billion active parameters, 16 experts, and 109 billion total parameters. Llama 4 Maverick: 17 billion active parameters, 128 experts, and 400 billion total parameters. Llama 4 Behemoth: 288 billion active parameters, 16 experts, and nearly 2 trillion total parameters. |
| Context Window Length | Mistral Large: 32K tokens, Mixtral 8x22B: 64k, Some reports mention 8K sequence length | Llama 4 Scout: 10 million tokens. Llama 4 Maverick: 1 million tokens. |
| Training Data Size and Composition | Codestral: over 80 programming languages (Python, Java, C++, JavaScript, etc.) | Trained on more than 30 trillion tokens. Includes diverse text, image, and video datasets. Llama 4 Scout was pretrained on ~40 trillion tokens and Llama 4 Maverick was pretrained on ~22 trillion tokens of multimodal data. Mix of publicly available, licensed data, and data from Meta's products/services (Instagram, Facebook). Pre-training data cutoff is August 2024. |
| Availability (Open Source vs. Proprietary) | Both open-source and proprietary models available; some under Apache 2.0 license | Meta refers to Llama 4 models as open source. |
| Licensing Terms and Usage Restrictions | Models like Mistral 7B and Mixtral 8x7B: Apache License 2.0 (personal and commercial use); some licenses prohibit commercial use; attribution generally required | Llama 4 Community License Agreement. Grants a royalty-free, worldwide right to use, modify, reproduce, and distribute the models. Requires displaying "Built with Llama". If monthly active users exceed 700 million, a special license from Meta is required. Adherence to the Acceptable Use Policy is mandatory, prohibiting use for harmful activities. The rights granted under Section 1(a) of the Llama 4 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union with respect to any multimodal models included in Llama 4. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. |
| Inference Speed (Latency) | Mistral Tiny LLM: <100ms for standard queries; Mixtral 8x7B: 6x faster than Llama 2 70B | Mixture-of-Experts (MoE) architecture activates only a subset of parameters per input, allowing Scout and Maverick to deliver high performance while keeping inference costs low. The number of active parameters on a given token is always 17B. This reduces latencies on inference and training. |
| Fine-tuning Capabilities and Ease of Use | Fine-tuning API via La Plateforme; `mistral-finetune` codebase; Azure AI Foundry | Llama 4 enables open source fine-tuning efforts by pre-training on 200 languages. Developers may fine-tune Llama 4 models for languages beyond the 12 supported languages provided they comply with the Llama 4 Community License and the Acceptable Use Policy. |
| Multilingual Support (Number of Languages) | Mistral Large: English, French, Spanish, German, Italian; Mistral Nemo: over 100 languages | Pre-trained on data spanning over 200 languages. Includes over 100 languages with over 1 billion tokens each. Strong multilingual performance, with 10x increase in non-English tokens compared to Llama 3. Supports 12 languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. |
| Code Generation Performance (Benchmarks) | Codestral: supports over 80 programming languages; Codestral 25.01: 86.6% on Python-focused HumanEval | Llama 4 Maverick excels in coding tasks and logical reasoning. High accuracy in structured code generation. MBPP: Maverick's 77.6 pass@1 outperforms Llama 3.1 405B (74.4). |
| Reasoning and Logic Performance (Benchmarks) | Mistral Large: top-tier reasoning; Magistral: reasoning in European languages | Llama 4 Maverick demonstrates strong general reasoning, close to GPT-4o. MMLU Pro: 80.5. GPQA Diamond: 69.8. |
| Hallucination Rate and Factuality | Amazon Bedrock Knowledge Bases can decrease hallucinations and improve accuracy | Low hallucination rate post-DPO. |
| Safety and Bias Mitigation Techniques | Amazon Bedrock Guardrails can filter harmful content; techniques to filter/mitigate biased training data | MetaP training technique to reliably set critical model hyper-parameters. Trained to avoid generating harmful content. |
| Price | Not available | Not available |
| Ratings | Not available | overall:Not available, performance:Not available |