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SpaceXAI: Grok 4.5

Grok 4.5 is a model from SpaceXAI with frontier performance on coding, knowledge work, and STEM.

Anyone in the Project can @-mention SpaceXAI: Grok 4.5 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 4.5 delivers a massive 500K token context window at $2/$6 per Mtok, making it a strong choice for document-heavy workflows where you need to process entire codebases or multi-chapter reports in one pass. The pricing undercuts most frontier models on input tokens, though output costs sit in the mid-range. Without public benchmarks, you're trading proven performance data for xAI's architectural bet on extreme context handling. Reach for this when context length matters more than established track records.

Best for

Processing entire codebases in one context
Multi-document research and synthesis
Long-form content analysis under budget
File-heavy workflows with vision needs
Cost-sensitive high-context applications

Strengths

The 500K token window handles roughly 375,000 words in a single request — enough for a full novel or a medium-sized codebase without chunking. Input pricing at $2/Mtok beats GPT-4o ($2.50) and Claude Sonnet 4 ($3), making it economical for ingesting large documents. Multimodal support covers text, images, and file uploads, so you can mix screenshots with technical specs in one prompt. The architecture prioritizes context retention over multiple turns, useful for iterative document editing.

Trade-offs

No public benchmarks means you can't compare reasoning quality, code generation accuracy, or instruction-following against Claude, GPT-4, or Gemini on standardized tests. Early xAI models lagged behind Anthropic and OpenAI on complex reasoning tasks, and without MMLU or HumanEval scores here, you're flying blind on capability gaps. Output tokens cost $6/Mtok — cheaper than Claude Opus but pricier than GPT-4o Mini — so long responses eat budget fast. The model is new enough that community tooling and integration examples remain sparse.

Specifications

Provider: x-ai
Category: llm
Context length: 500,000 tokens
Max output: 450,000 tokens
Modalities: text, image, file
License: proprietary
Released: 2026-07-08

Pricing

Input: $2.00/Mtok
Output: $6.00/Mtok
Model ID: x-ai/grok-4.5

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

$56.32

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-level routing data is not available yet for this model.

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

Codebase Architecture Review

I've uploaded my full codebase as files. Review the architecture, identify tight coupling between modules, and recommend three refactoring priorities with specific file references.

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Multi-Chapter Book Summary

Summarize the key arguments in this 300-page manuscript, tracking how the author's thesis evolves across chapters. Highlight contradictions or gaps in reasoning.

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Contract Comparison Analysis

I've provided three vendor contracts as files. Compare their liability clauses, payment terms, and termination conditions. Flag any unusual provisions.

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Research Paper Synthesis

I've uploaded five research papers on neural scaling laws. Synthesize their findings, note conflicting results, and suggest which methodology is most rigorous.

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Technical Spec to Implementation

Here's a 200-page API specification. Generate Python client code with all endpoints, authentication, and error handling. Follow the naming conventions in section 4.

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