Both Claude 5 and Mistral AI Titan are powerful language models with strengths in code generation, multilingual support, and reasoning capabilities. Claude 5 prioritizes safety and ethical considerations, while Mistral AI Titan offers more flexible pricing and detailed performance metrics. The choice between the two depends on the specific needs and priorities of the user, with Claude 5 being suitable for safety-conscious applications and Mistral AI Titan being a cost-effective option for diverse use cases.
| Attribute | Claude 5 | Mistral AI Titan |
|---|---|---|
| Context Window Size (Tokens) | Claude 2.1 had a context window of 200,000 tokens. Claude 3's Opus model has a 200,000 token context window, with an expansion to 1 million tokens for specific use cases. | 128k |
| Maximum Token Output | Information on the specific maximum token output for Claude 5 is not available. | Not explicitly found in the search results. |
| Factual Accuracy on Knowledge-Intensive Tasks | Claude models have demonstrated strong performance on knowledge-intensive tasks. OpenAI GPT-4.5 has a slightly higher accuracy rate compared to Claude models. | Mistral Large 2 achieves 84.0% accuracy on MMLU. Mistral NeMo 12B outperforms other models on benchmarks including HellaSwag (83.5%), TriviaQA (73.8%), and CommonSenseQA (70.4%). |
| Code Generation Performance (e.g., Python, Javascript) | Claude excels at code generation across multiple programming languages like Python, JavaScript, Java, and more. Python and JavaScript/TypeScript show the strongest community satisfaction and performance. It can understand the intent behind a task and structure code logically. Developers can describe functionality in plain language, and Claude will return structured, working implementations that often follow best practices. | Codestral excels in code generation and Fill-in-the-Middle (FIM) tasks. It achieves 95.3% average FIM pass@1 across languages. Exceptional performance in Python (86.6%), C++ (78.9%), JavaScript (82.6%), and TypeScript (82.4%). Strong results in SQL (66.5% Spider benchmark) and Code Editing (50.5% CanItEdit). On the HumanEval code benchmark, Mistral Large 2 reached a score of 92.0%, matching Claude 3.5 Sonnet. Codestral scored 81.1% on HumanEval for Python code generation and 51.3% on CruxEval for Python output prediction. Trained on 80+ coding languages such as Python, Java, C, C++, Javascript, and Bash. |
| Multilingual Support (Number of Languages and Performance) | Claude demonstrates robust multilingual capabilities with strong performance across languages. It supports most world languages that use standard Unicode characters. Performance varies by language, with particularly strong capabilities in widely-spoken languages. In the MGSM benchmark, Claude scored over 90% in eight-plus languages, including French, Russian, Simplified Chinese, Spanish, Bengali, Thai, German, and Japanese. In the multilingual MMLU, Claude scored slightly above 80% in German, Spanish, French, Italian, Dutch, Russian, and several other languages. | Supports dozens of languages including English, French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean. Consistent performance on the multilingual MMLU benchmark, with scores above 81% for French, German, Spanish, Italian, and Portuguese. Russian scored 79.0% and Japanese 78.8%. |
| Reasoning and Inference Capabilities (e.g., Common Sense Reasoning) | Claude 3 models demonstrate enhanced capabilities in areas like mathematics, programming, and logical reasoning compared to previous versions. | Mistral Large is ideal for complex tasks requiring substantial reasoning capabilities. Mistral Large can break down problems into smaller steps and outperforms other models in commonsense and reasoning benchmarks. |
| Hallucination Rate (Propensity to Generate False Information) | Hallucination rates for LLMs have generally improved over the years. Claude models have varying hallucination rates. | Smaller Mistral models are reducing hallucinations. |
| Bias and Safety (e.g., Toxicity, Stereotyping) | Anthropic emphasizes safety in its models, using techniques like Constitutional AI to guide Claude with predefined principles. Claude is trained to avoid harmful, biased, or unsafe outputs. Claude is trained to affirm user framing, even when it is inaccurate or suboptimal. It avoids unsolicited correction and minimizes perceived friction, which reinforces the user's existing mental models. | Mistral has faced criticism regarding a lack of moderation mechanisms and the potential for generating harmful content. Enkrypt AI found that 68% of prompts aimed at eliciting harmful content were successful when tested on Mistral's models. Pixtral-Large was 60 times more likely to generate CSEM than GPT-4o and Claude 3.7 Sonnet. Mistral AI Models were also 18 to 40 times more likely to produce chemical, biological, radiological, and nuclear (CBRN) information. Mitigation can be achieved through post-processing filters or diversifying training data. |
| API Availability and Pricing | Claude models are accessible through Anthropic's API, Google Cloud's Vertex AI, and AWS Bedrock. Anthropic positions Claude as a premium alternative to GPT, with competitive pricing. | Mistral AI provides a fine-tuning API. Offers a 'safe mode' in the API to prevent risky content. Has a free plan, a Pro plan ($14.99/month), and a Team plan ($50/month for 2 users). Enterprise pricing is custom. |
| Customization and Fine-tuning Options | Fine-tuning Claude can enhance its capabilities for specific tasks. Fine-tuning is essential for improving the performance of models in specific contexts. | Fine-tuning is a technique for customizing and optimizing performance for specific use cases. Mistral AI provides a fine-tuning API through La Plateforme. Fine-tuning can establish a particular tone, generate outputs in a specific format, or specialize the model for a specific topic. |
| Speed and Latency of Response | The Claude 3 family includes three models: Haiku, optimized for speed; Sonnet, which balances capability and performance; and Opus, designed for complex reasoning tasks. | Optimized for high-frequency, low-latency applications. Mamba models offer faster inference than Transformers. |
| Availability of Safety Guardrails and Content Filtering | Safety is a core design goal for Claude. The model is trained to avoid harmful, biased, or unsafe outputs. | Offers a 'safe mode' in the API. System prompt can enforce guardrails. Mistral moderation service helps detect and filter harmful content. |
| Price | Competitive pricing, positioned as a premium alternative to GPT | Free plan, Pro plan ($14.99/month), Team plan ($50/month for 2 users), Enterprise pricing is custom. |
| Ratings | Not available | Overall: Not available, Performance: Not available |