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. Lightweight but thoughtful reasoning

• Designed to 'think before responding' with accessible raw thought traces.
• Excellent for logic puzzles, lightweight reasoning, and systematic tasks.

2. Extremely cost-efficient

• Only $0.30 per 1M input tokens and $0.50 per 1M output tokens.
• Cached token support lowers cost to $0.075 per 1M tokens.

3. Fast and responsive

• Optimized for low-latency applications and high-throughput use cases.
• Suitable for chatbots, assistants, and automation flows.

4. Supports modern developer features

• Function calling for tool-augmented workflows.
• Structured outputs for schema-controlled responses.
• Integrates cleanly with agents and pipelines.

5. Large 131K context window

• Can understand and work with long documents, transcripts, or multi-turn sessions.

6. Great for non-domain-heavy tasks

• Useful for summarization, rewriting, extraction, everyday reasoning, and app logic.
• Does not require domain expertise to operate effectively.

7. Compatible with enterprise infrastructure

• Stable rate limits: 480 requests per minute.
• Same API structure as all Grok 3 models.

8. Optional Live Search support

• $25 per 1K sources for real-time search augmentation.