Llama 3.3 (70B) vs Google Translate
Side-by-side comparison to help you choose.
| Feature | Llama 3.3 (70B) | Google Translate |
|---|---|---|
| Type | Model | Product |
| UnfragileRank | 24/100 | 30/100 |
| Adoption | 0 | 0 |
| Quality | 0 | 0 |
| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 13 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
Generates coherent multi-turn conversations and instruction-following responses using a transformer-based architecture with 70 billion parameters and 128K token context window. The model is instruction-tuned (method unspecified) to follow user directives across dialogue scenarios, supporting streaming output for real-time response generation. Processes chat messages in role/content format (user/assistant/system) and maintains conversation state across multiple turns within the 128K token limit.
Unique: 70B parameter count with 128K context window claims performance parity with Llama 3.1 405B through architectural efficiency improvements, deployed locally via Ollama with native streaming support and no cloud API latency
vs alternatives: Offers 128K context window and local execution without cloud costs, but lacks published benchmarks to verify claimed 405B-equivalent performance compared to GPT-4 or Claude
Generates text in 8 officially supported languages (English, German, French, Italian, Portuguese, Hindi, Spanish, Thai) with language-specific safety and helpfulness thresholds applied during training. The model can output text in other languages but Meta explicitly discourages this without custom fine-tuning and system controls. Language support is asymmetric — English receives full optimization while other languages have documented performance thresholds that may vary.
Unique: Explicitly documents language-specific safety thresholds and discourages unsupported language use without fine-tuning, unlike competitors that silently degrade or provide no guidance on multilingual safety
vs alternatives: More transparent about multilingual limitations than closed-source models, but narrower language support (8 vs 100+) and requires custom fine-tuning for expansion
Llama 3.3 documentation lists 'vision' as a supported capability but provides no details on image input formats, supported image types, resolution limits, or vision task types. The feature is mentioned but completely undocumented, making it impossible to assess whether this is a full multimodal model or limited image understanding.
Unique: Llama 3.3 lists vision capability but provides zero documentation on implementation, formats, or scope — impossible to assess multimodal capabilities
vs alternatives: Unknown — insufficient documentation to compare with documented multimodal models (GPT-4V, Claude 3.5, LLaVA)
Llama 3.3 documentation lists 'embeddings' as a supported capability but provides no details on embedding dimensions, similarity metrics, fine-tuning approach, or API format. The feature is mentioned but completely undocumented, making it impossible to assess whether embeddings are available or how to use them.
Unique: Llama 3.3 lists embeddings capability but provides zero documentation on API, dimensions, or quality — impossible to assess embedding suitability
vs alternatives: Unknown — insufficient documentation to compare with documented embedding models (OpenAI text-embedding-3, Sentence Transformers)
Llama 3.3 documentation lists 'web search' as a supported capability but provides no details on search provider, query format, result integration, or latency impact. The feature is mentioned but completely undocumented, making it impossible to assess whether web search is natively integrated or requires external configuration.
Unique: Llama 3.3 lists web search capability but provides zero documentation on implementation, provider, or activation — impossible to assess web search functionality
vs alternatives: Unknown — insufficient documentation to compare with documented web search integration (Perplexity, SearchGPT, Bing Chat)
Supports tool-use and function-calling capabilities through a developer-managed integration pattern where the model generates tool invocations and developers are responsible for executing those tools and returning results. The model does not directly call external APIs or services — instead, it generates structured requests that developers must route to their chosen tools and services. This pattern requires developers to implement clear policies for tool safety, security, and third-party service integrity assessment.
Unique: Explicitly delegates tool execution responsibility to developers rather than providing native tool-calling APIs, requiring custom integration but enabling fine-grained security control and custom tool ecosystems
vs alternatives: Offers more control than OpenAI/Anthropic function-calling but requires more implementation work; stronger for custom tool ecosystems, weaker for rapid prototyping
Generates structured outputs (JSON, XML, or other formats) by accepting schema definitions in prompts or system messages and producing model outputs that conform to specified structures. The implementation approach is not documented, but likely uses prompt engineering or constrained decoding to guide the model toward valid structured outputs. No native schema validation or error handling is provided — developers must validate outputs post-generation.
Unique: Supports structured output generation but delegates schema enforcement and validation to developers, providing flexibility but requiring custom validation logic
vs alternatives: More flexible than OpenAI's structured outputs but less reliable without native schema validation; suitable for custom extraction pipelines
Generates responses in streaming mode, returning tokens incrementally as they are generated rather than buffering the entire response. Ollama targets low time-to-first-token (TTFT) and high throughput through streaming, enabling real-time user-facing applications. The streaming implementation uses HTTP chunked transfer encoding or Server-Sent Events (SSE) to deliver tokens as they become available, reducing perceived latency in interactive applications.
Unique: Ollama's streaming implementation targets low TTFT and high throughput through local execution, avoiding cloud API round-trip latency, but specific performance metrics are undocumented
vs alternatives: Local streaming eliminates cloud API latency compared to OpenAI/Anthropic, but lacks published TTFT benchmarks to verify performance claims
+5 more capabilities
Translates written text input from one language to another using neural machine translation. Supports over 100 language pairs with context-aware processing for more natural output than statistical models.
Translates spoken language in real-time by capturing audio input and converting it to translated text or speech output. Enables live conversation between speakers of different languages.
Captures images using a device camera and translates visible text within the image to a target language. Useful for translating signs, menus, documents, and other printed or displayed text.
Translates entire documents by uploading files in various formats. Preserves original formatting and layout while translating content.
Automatically detects and translates web pages directly in the browser without requiring manual copy-paste. Provides seamless in-page translation with one-click activation.
Provides offline access to translation dictionaries for quick word and phrase lookups without requiring internet connection. Enables fast reference for individual terms.
Automatically detects the source language of input text and translates it to a target language without requiring manual language selection. Handles mixed-language content.
Google Translate scores higher at 30/100 vs Llama 3.3 (70B) at 24/100. Llama 3.3 (70B) leads on ecosystem, while Google Translate is stronger on quality.
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Converts text written in non-Latin scripts (e.g., Arabic, Chinese, Cyrillic) into Latin characters while also providing translation. Useful for reading unfamiliar writing systems.