Gemini Ultra 2 appears to be a more advanced and versatile model with a larger context window, lower hallucination rates, comprehensive safety measures, and multimodal capabilities. However, Mistral AI Titan excels in code generation and offers competitive inference speeds. The choice between the two depends on the specific use case and priorities.
| Attribute | Mistral AI Titan | Gemini Ultra 2 |
|---|---|---|
| Context Window Size | Up to 128k tokens (Mistral AI models), 32K (Mixtral 8x7B) | Gemini Ultra has a context window of 32,768 tokens. Gemini 2.5 Pro ships with a 1 million token context window, with 2 million coming soon. |
| Finetuning Capabilities | Finetuning API via La Plateforme, `mistral-finetune` codebase available. Useful for customizing tone, specializing in a domain, improving via distillation, enhancing performance by mimicking complex prompts, and reducing cost/latency. | Gemini models can be fine-tuned using supervised fine-tuning, which adapts the model's behavior using a labeled dataset. You can tune text, image, audio, and document data types. |
| Multilingual Support | English, French, German, Spanish, Italian. Mistral Large 2 excels in English, French, German, Spanish, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, and Hindi. | Gemini was trained on diverse datasets spanning 109 languages and multiple cultural contexts. |
| Code Generation Performance | Codestral is designed for code generation, supporting over 80 coding languages. Codestral 25.01 outperforms other models with fewer than 100 billion parameters in Python, JavaScript, and fill-in-the-middle (FIM) tasks. In Python-focused HumanEval tests, Codestral 25.01 scored 86.6% | Gemini Ultra excels in several coding benchmarks, including HumanEval and Natural2Code. Gemini 2.5 Pro excels at creating visually compelling web apps and agentic code applications, along with code transformation and editing. |
| Reasoning Ability | Reasoning models available, employ a 'chain of thought' approach. Magistral models are designed for general-purpose use requiring longer thought processing and better accuracy. | Gemini AI has advanced reasoning capabilities that allow it to think more thoroughly when answering tough questions. Gemini Ultra achieves a state-of-the-art score of 59.4% on the MMMU benchmark, which requires deliberate reasoning. Gemini 2.5 models are capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy. |
| Hallucination Rate | Lower hallucination rates due to lightweight, fine-tuned architectures that prioritize accuracy. | Zhipu AI and Gemini Models Have The Lowest Hallucination Rates. Google's latest models are leading the pack: Gemini-2.0-Flash-001: 0.7% hallucination rate, Gemini-2.0-Pro-Exp: 0.8%, Gemini-2.0-Flash-Lite-Preview: 1.2%. |
| API Availability and Pricing | Available through Mistral AI La Plateforme, Amazon Bedrock, and Google Cloud's Vertex AI. Pricing details on Mistral AI platform. | Developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI. Gemini 2.5 Pro is available now in Google AI Studio and in the Gemini app for Gemini Advanced users, and will be coming to Vertex AI soon. |
| Inference Speed (Latency) | Mistral 7B benchmarked at 130-millisecond time to the first token, with 170 tokens per second and a total response time of 700 milliseconds. | Gemini 2.0 Flash has enhanced performance at similarly fast response times. |
| Training Data Size and Composition | Mistral Large 2 was trained on a large proportion of multilingual data. Diverse datasets improve performance across varied tasks. | Gemini was trained on a massive scale of multimodal data from web documents, books, and code, along with some of Google's internal data sources. Gemini Pro was trained on a dataset specifically designed for multimodal tasks, including text, code, images, and audio. Gemini Ultra was trained on an even larger dataset of text and code, also including code from open-source repositories. |
| Safety Measures and Bias Mitigation | 'Zero tolerance policy on child safety', collaborates with nonprofit Thorn. Vulnerabilities exist in generating harmful content. | Gemini has the most comprehensive safety evaluations of any Google AI model to date, including for bias and toxicity. Google tackles bias head-on with bias mitigation algorithms, training the model on diverse datasets spanning 109 languages and multiple cultural contexts, running fairness tests to ensure outputs don't reinforce stereotypes or exclude marginalized groups, and partnering with experts in ethics and sociology to audit the system's outputs. |
| Tool Use/Integration Capabilities | Natively handles function calling and JSON outputting. Integrations via IDE plugins (VS Code, JetBrains) and APIs (Google Cloud's Vertex AI, Azure AI Foundry). | Gemini can be used as a safety filter and for content moderation. Gemini 2.0 Flash supports multimodal outputs like natively generated images mixed with text and steerable text-to-speech (TTS) multilingual audio and can natively call tools like Google Search, code execution, and third-party user-defined functions. |
| Parameter Count | Mixtral 8x22B has 176 billion parameters (44B active). Mistral Large 2 has 123 billion parameters. | One estimate puts Gemini Ultra at over 1 trillion parameters. |
| Price | Not available | Gemini 2.5 Pro input price is free of charge, $1.25 per 1M tokens in USD for prompts <= 200k tokens, and $2.50 for prompts > 200k tokens. Output price (including thinking tokens) is free of charge, $10.00 per 1M tokens in USD for prompts. |