1. Maximum-compute o-series model

• Uses significantly more compute per query compared to o1.
• Produces deeper, more reliable reasoning chains.
• Best suited for high-stakes tasks that need correctness over speed.

2. Trained with reinforcement learning for deliberate thinking

• Explicit "think-before-answer" architecture.
• Excels at complex reasoning requiring multi-step analysis.

3. Very strong at math, science, coding, and technical proofs

• Handles long derivations, algorithm design, and difficult logic problems.
• Produces structured and explainable reasoning trails.

4. Great for multi-turn reasoning workflows

• Responses API optimized: can think over multiple internal turns before responding.
• Ideal for agentic reasoning pipelines.

5. Large context window

• 200,000-token context for large documents, multi-file review, and long reasoning traces.

6. Multimodal input (text + image)

• Can analyze images for mathematical diagrams, charts, handwritten content, UI layouts, etc.
• Output is text only.

7. Consistency, reliability, and depth

• Designed for situations where accuracy matters more than latency or cost.
• Strong error-checking and self-correction abilities.

1. Flagship-level reasoning and math performance

• Designed for world-class reasoning depth, precision, and multi-step logical chains.
• Excels at STEM, mathematics, symbolic operations, proofs, and analytical workloads.

2. Powerful multimodal understanding

• Supports text, images, and other modalities.
• Handles cross-modal reasoning tasks requiring context synthesis.

3. Extreme capability across diverse tasks

• Positioned as a top-tier 'jack of all trades' model.
• Strong in natural language, coding, knowledge retrieval, and structured generation.

4. Large 256K context window

• Enables analysis of long documents, entire codebases, multi-document packs, and extensive agent sessions.
• Supports workloads that require persistent reasoning across large inputs.

5. Advanced developer tooling support

• Function calling for tool-augmented workflows.
• Structured outputs for predictable, schema-controlled generation.
• Integrates smoothly with agents and complex automation pipelines.

6. Efficient caching for cost reduction

• Cached input tokens discounted to $0.75 / 1M tokens.
• Encourages RAG, retrieval pipelines, and multi-step conversational workflows.

7. Production-ready performance

• Stable rate limits: 480 requests per minute.
• High token throughput: 2,000,000 tokens per minute.
• Available across multiple xAI regional clusters.

8. Optional Live Search augmentation

• Add-on: $25 per 1K sources.
• Enhances factual accuracy and real-time information retrieval.