xAI: Grok 4.20 Multi-Agent

I have a 50-page technical specification PDF and three competing vendor proposals. Extract the key requirements, then build a comparison matrix showing which vendor meets each requirement. Flag any gaps or ambiguities.

This example would produce a structured analysis beginning with a numbered list of extracted requirements (security certifications, API response times, data residency constraints, etc.), followed by a three-column comparison table mapping each requirement to vendor compliance status. The model would highlight two requirements where the spec uses vague language ('reasonable uptime' without SLA definition) and note that Vendor B's proposal doesn't address the API versioning requirement at all. The output would span roughly 800 words with clear section headers, making the 2M token context window's document-handling strength immediately visible.

Showcases the massive context window handling multiple large documents simultaneously — a 50-page PDF plus three proposals easily fits in 2M tokens. The multi-agent architecture likely coordinates separate analysis and synthesis steps. At $6/Mtok output, a thorough 1000-token response costs $0.006, but long multi-document tasks will accumulate input costs quickly.

Review this codebase (attaching 12 Python files, ~8000 lines total). Identify architectural inconsistencies, suggest a refactor plan to consolidate the three different error-handling patterns, and estimate migration effort.

This example would produce a hierarchical breakdown: first, a diagram-style ASCII representation of the current architecture showing how modules interact; second, annotated code excerpts demonstrating the three error-handling patterns (try-except blocks, error return codes, and a custom Result wrapper); third, a phased refactor plan recommending the Result pattern as the standard with specific file-by-file migration steps; fourth, an effort estimate (roughly 3 developer-days, assuming test coverage exists). The response would reference specific line numbers and function names from the attached files, demonstrating that the model has genuinely parsed the entire codebase context.

Demonstrates file upload handling and the ability to reason across a mid-sized codebase within the 2M token window. The multi-agent framing suggests separate agents might handle static analysis versus refactor planning. However, without public benchmarks, we can't verify accuracy on complex refactoring logic compared to models with published HumanEval or SWE-bench scores.

Attached: a blurry photo of a handwritten flowchart on a whiteboard. Convert this to Mermaid diagram syntax, then explain three potential bottlenecks in the process it describes.

This example would first produce clean Mermaid code reconstructing the flowchart (likely 15-20 nodes with decision diamonds and process boxes), interpreting the handwritten labels despite the photo quality. Then it would provide a numbered analysis: (1) the approval step creates a single point of failure with no parallel path, (2) the data validation loop has no timeout, risking infinite retries, (3) the final aggregation step processes serially when the diagram suggests the inputs could be gathered concurrently. The explanation would reference specific node IDs from the generated Mermaid code.

Highlights image input capability combined with structured output generation and process analysis. The multi-agent architecture might split OCR/image understanding from logical reasoning. The 2M token context is overkill here but allows follow-up questions referencing the same image. Trade-off: at $2/Mtok input, image tokens add up faster than text, though a single photo is negligible.

When 2M tokens lets you process entire research libraries in one pass

A 4-person biotech consultancy needs to synthesize 40+ clinical trial PDFs (averaging 80 pages each) into a single comparative report every week. Grok 4.20's 2M token context window fits the entire corpus in one prompt—no chunking, no retrieval pipeline, no context-loss between documents. At $2/Mtok input, processing 1.5M tokens of source material costs $3 per report. The multi-agent architecture handles the synthesis workflow (extract → compare → draft → cite) without manual orchestration. If your documents are under 500K tokens combined, a standard 128K model at $0.15/Mtok saves you money. Above that threshold, Grok's window eliminates the engineering overhead of RAG systems and keeps cross-document reasoning intact. Buy this if you're routinely hitting context limits on document-heavy workflows.

Multi-agent vision workflows for content moderation at scale

A 12-person e-commerce platform reviews 8,000 user-uploaded product photos daily for policy violations (prohibited items, misleading angles, copyright issues). Grok's multi-agent mode lets you chain vision analysis (flag suspicious images) → text reasoning (check against policy rules) → decision routing (auto-approve, escalate, reject) in a single API call. The image modality handles photos natively without preprocessing. At $2 input / $6 output per Mtok, a typical 3-stage workflow costs ~$0.008 per image reviewed—$64/day for 8K images. Without public vision benchmarks, you'll want to pilot 500 images against your policy edge-cases before committing. If accuracy on your specific violations stays above 92%, the agent orchestration saves 15 hours/week versus manual review queues. Deploy this when your moderation backlog exceeds 2,000 items/day.

When contract review needs cross-reference across 50+ documents

A 3-attorney firm handles M&A due diligence where a single deal involves 60 contracts (NDAs, employment agreements, vendor MSAs, IP assignments) totaling 1.2M tokens. Grok's 2M window loads the entire deal room so the model can trace obligations across documents—finding where an NDA's confidentiality term conflicts with a vendor contract's disclosure clause, for example. The multi-agent setup runs parallel analysis tracks (financial terms, liability caps, termination rights) then consolidates findings. At $2/Mtok input, each deal review costs $2.40 in processing. Output generation (the memo) adds $6/Mtok, so a 20K-token report costs another $0.12. If your deals are under 200K tokens, Claude 3.5 Sonnet at $3/Mtok input is cheaper. Above 500K tokens, Grok's window and agent routing justify the cost by eliminating multi-pass workflows.