Llama 3.3 (70B)

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.

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.

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.

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.

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.

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.

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.

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.

Executes the 70B model locally on user hardware via the Ollama runtime, exposing a REST API on localhost:11434 for model inference. The model runs entirely on local hardware without cloud dependencies, enabling offline operation and eliminating API latency and costs. Ollama handles model loading, quantization (method unspecified), GPU/CPU scheduling, and concurrent request management through its runtime.

Provides official language bindings for Python and JavaScript/TypeScript that wrap the Ollama HTTP API, enabling developers to interact with the model without direct HTTP calls. Additionally supports 20+ community-maintained bindings for languages like Go, Rust, Ruby, Java, and others. Bindings abstract the HTTP API layer and provide idiomatic interfaces for each language, but all ultimately communicate with the same Ollama runtime.

Llama 3.3 is available as a pre-configured model in several Ollama-integrated applications (Claude Code, Codex, OpenCode, OpenClaw, Hermes Agent) that provide domain-specific interfaces and workflows. These applications handle model loading, prompt engineering, and application-specific logic, allowing non-technical users to leverage the model without direct API interaction. The model serves as the inference engine while the application provides the user-facing functionality.

Llama 3.3 is available for cloud deployment through Ollama's cloud service with three pricing tiers (Free, Pro, Max) that control concurrent model instances and usage limits. Cloud deployment abstracts hardware management and provides managed inference without local infrastructure. The cloud service uses the same model but may apply different quantization or optimization strategies compared to local deployment, though specific differences are not documented.

Llama 3.3 documentation mentions a 'thinking' capability but provides no details on implementation, activation, or behavior. This likely refers to chain-of-thought reasoning where the model generates intermediate reasoning steps before producing final outputs, similar to OpenAI's o1 model. The feature is listed but not explained, making it impossible to assess how to use it or what benefits it provides.