LLMx-ai

SpaceXAI: Grok Build 0.1

Grok Build 0.1 is SpaceXAI’s fast coding model trained specifically for agentic software engineering workflows. It supports text and image inputs with text output, and is optimized for interactive coding...

Anyone in the Project can @-mention SpaceXAI: Grok Build 0.1 with the team's shared context - pooled credits, one chat, one memory.

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Starter is free forever - 1 Project, 100 credits/month, 1 MCP. No card.

Verdict

Grok Build 0.1 offers a massive 256K context window at aggressive pricing — $1 input/$2 output per Mtok undercuts most frontier models. The trade-off is minimal public benchmark data, so you're betting on xAI's engineering without the usual proof points. Reach for this when you need long-context processing at scale and can tolerate some uncertainty around reasoning quality. Best suited for teams already comfortable with xAI's ecosystem or willing to run their own evals.

Best for

Long-context document processing on a budget
High-volume summarization tasks
Exploratory work with large codebases
Cost-sensitive multimodal workflows

Strengths

The 256K context window handles entire codebases, legal documents, or research papers in a single call. Pricing sits well below GPT-4o and Claude Sonnet — you get frontier-class context capacity at roughly half the cost. Multimodal support (text, image, file) covers common use cases without requiring separate models. The aggressive pricing makes this viable for high-throughput production workloads where context length matters more than peak reasoning.

Trade-offs

No public benchmarks means you're flying blind on reasoning quality, code generation accuracy, and instruction-following compared to Claude or GPT-4o. Early build number (0.1) signals this is a young model — expect rough edges and potential API changes. Without MMLU, HumanEval, or GPQA scores, you'll need to run your own evals before committing production traffic. Teams used to Claude's reliability or GPT-4's polish may find gaps in nuanced reasoning or complex multi-step tasks.

Specifications

Provider: x-ai
Category: llm
Context length: 256,000 tokens
Max output: 230,400 tokens
Modalities: text, image, file
License: proprietary
Released: 2026-05-20

Pricing

Input: $1.00/Mtok
Output: $2.00/Mtok
Model ID: x-ai/grok-build-0.1

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.

Team cost calculator

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

$22.88

17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.

Providers

Provider	Context	Input	Output	P50 latency	Throughput	30d uptime
x-ai	256k	$1.00/Mtok	$2.00/Mtok	—	—	—

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Projects have used this model yet to share anonymised team stats. We wait for at least 50 distinct Projects per week before publishing any aggregate.

Starter prompts

Summarize Legal Document

Read this entire contract and produce a structured summary: (1) parties and effective dates, (2) core obligations for each party, (3) termination clauses, (4) liability caps, (5) risks or unusual terms. Use bullet points.

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Analyze Codebase Structure

Review all files in this repository. Describe the overall architecture, list the main modules and their responsibilities, identify any circular dependencies, and flag files that seem out of place or poorly named.

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Extract Data from Invoice Images

Extract the following from this invoice image: vendor name, invoice number, date, line items with quantities and prices, subtotal, tax, total. Return as JSON.

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Compare Research Papers

I'm providing three research papers. Compare their methodologies, findings, and conclusions. Highlight where they agree, where they conflict, and any gaps none of them address.

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Generate Test Cases from Spec

Read this full product requirements document. Generate a test plan covering happy paths, edge cases, error conditions, and performance scenarios. Organize by feature area.

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