- Instruction Following
Free Models Router is an OpenRouter meta-model that automatically routes requests to compatible free models, providing no-cost inference across multiple underlying LLMs. It filters candidates based…
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Grok Build 0.1
- Instruction Following
Grok Build 0.1 is xAI’s fast, agentic coding model optimized for software engineering workflows, with a 256K-token context window and support for text and image inputs.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Grok Build 0.1?
Grok Build 0.1 is xAI’s coding-focused language model designed for agentic software development tasks such as web development, debugging, and multi-step code planning. It is mainly used to power interactive coding agents that perform planning, tool use, and function calling over long contexts, and can also serve as a cost‑effective general-purpose model for structured outputs and automation workflows. Grok Build 0.1 succeeds earlier xAI coding models like grok-code-fast-1 and belongs to the broader Grok model family.
Model capabilities
5 Core Capabilities
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Agentic Coding
Specialized in autonomous, multi-step coding workflows including planning, implementing, refactoring, and iterating on software projects and features.
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Web Development
Generates and updates front-end and back-end web application code, scaffolds projects, and helps integrate common frameworks, libraries, and APIs.
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Debugging Support
Analyzes error messages, stack traces, and failure cases to locate bugs, propose fixes, and improve overall code reliability and maintainability.
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Tool And MCP Integration
Supports function calling and MCP-style tool integration, enabling automated interaction with external APIs, services, and developer tooling pipelines.
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Code From Visuals
Accepts images like diagrams, UI mockups, or error screenshots to infer structure and generate or adjust corresponding implementation code.
Use cases
6 Most Valuable Use Cases
- General Web Question Answering
- Programming Help and Debugging
- Real-time News Summarization
- Market and Finance Insights
- Business Research Assistance
- AI and Science Explanations
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for Grok-class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.20 | $0.60 | 256K |
| xAI | US | ~250ms | ~40 tps | ~99.9% | ~$5.00 | ~$15.00 | ~128K |
| OpenAI (GPT-4o class) | Global | ~300ms | ~35 tps | 99.9% | ~$2.50 | ~$10.00 | 128K |
| Anthropic (Claude 3 class) | US East | ~320ms | ~30 tps | 99.9% | ~$3.00 | ~$15.00 | 200K |
| Google (Gemini 1.5 Pro class) | Global | ~350ms | ~25 tps | ~99.9% | ~$4.00 | ~$12.00 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | Grok Build 0.1 (xAI) | GPT-4o mini (OpenAI) | Claude 3.5 Haiku (Anthropic) |
|---|---|---|---|
| Model Type | Coding-optimized LLM | General-purpose LLM | General-purpose LLM |
| Context Window | 256K tokens | 128K tokens | 200K tokens |
| Input Price ($/1M tokens) | $1.00 | $0.15 | $0.80 |
| Output Price ($/1M tokens) | $2.00 | $0.60 | $4.00 |
| Max Output Tokens | — | — | 8K |
| Throughput | ≥100 tokens/s | — | — |
| Avg Latency | Low (coding-optimized) | Low | Very low |
| Uptime (API SLA) | — | — | — |
30-day usage via LLM API
- 2.4B
- Prompt tokens processed (30 days)
- 210M
- Completion tokens generated (30 days)
- 3.1M
- API requests served (30 days)
- 98.8%
- Avg API uptime (30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Intelligent Model Routing
Automatically route each request to the best model by cost, latency, and capability. One endpoint abstracts away provider churn and manual model selection.
One endpoint, smart routing -
Cost-Aware Execution
Optimize spend with per-request cost controls, price-aware routing, and detailed usage insights so you can ship faster without surprise bills or manual tuning.
Control and cut costs -
Resilient Fallback Logic
Define automatic fallbacks across providers and models to survive outages and rate limits, keeping your production workloads online without custom retry code.
Built-in reliability layer -
End-to-End Observability
Trace every call across providers with logs, metrics, and latency breakdowns, so you can debug, optimize, and benchmark models from a single pane of glass.
See every token, everywhere -
Task-Level Abstractions
Work at the level of tasks—chat, tools, RAG, workflows—instead of raw APIs, so you can swap underlying models without rewriting application logic.
Code to tasks, not APIs -
High-Throughput Batch Jobs
Run massive batch inference across providers with automatic chunking, retries, and progress tracking, maximizing throughput while staying within limits and budgets.
Scale jobs, not scripts
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a capable general-purpose chatbot for everyday Q&A and productivity tasks.
- You need an alternative to mainstream LLM providers for redundancy or vendor diversification.
- Your use case involves casual ideation, drafting short texts, or simple code snippets.
- You need quick, conversational assistance integrated into products targeting xAI’s ecosystem or audience.
- Your use case involves experimenting with xAI models to evaluate capabilities and future roadmap.
Avoid if...
- You need state-of-the-art reasoning, coding, or complex tool use comparable to top frontier models.
- Your workload requires rigorously tested enterprise guarantees around uptime, SLAs, and compliance certifications.
- You need mature ecosystem integrations, plugins, and SDKs already battle-tested across many industries.
- Your workload requires highly optimized inference costs or latency with detailed, public benchmarks.
- You need long-context processing, advanced fine-tuning options, or rich modality support beyond basic text.
FAQ
Frequently Asked Questions
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What is Grok Build 0.1?
Grok Build 0.1 is an xAI language model accessible through LLM.API for fast, general-purpose text generation and reasoning tasks.
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What is Grok Build 0.1 best suited for?
Grok Build 0.1 is best for rapid prototyping, chat-style assistants, and tools requiring concise reasoning over medium-length inputs.
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What context window does Grok Build 0.1 support via LLM.API?
Grok Build 0.1 supports a 32K token context window through LLM.API for prompts plus generated output combined.
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How fast is Grok Build 0.1 in terms of typical latency?
Grok Build 0.1 generally returns first tokens within a few hundred milliseconds, with full responses depending on output length and load.
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What modalities does Grok Build 0.1 support on LLM.API?
Grok Build 0.1 currently supports text input and text output only when accessed via LLM.API.
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How is Grok Build 0.1 priced on LLM.API?
Grok Build 0.1 is billed per token through LLM.API, with separate rates for input and output tokens defined in your LLM.API pricing plan.
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How do I call Grok Build 0.1 using LLM.API?
Set the model parameter to "xai:grok-build-0.1" in your LLM.API request and authenticate with your LLM.API key as usual.
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How does Grok Build 0.1 compare to larger flagship models?
Grok Build 0.1 typically offers lower cost and latency than frontier models but with reduced peak reasoning depth and nuanced instruction following.
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What are the main limitations of Grok Build 0.1?
Grok Build 0.1 can hallucinate facts, struggle with very long multi-step reasoning, and should not be used as a sole source for critical decisions.
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Can Grok Build 0.1 handle streaming responses on LLM.API?
Yes, Grok Build 0.1 supports server-sent event streaming on LLM.API when you enable streaming in the request options.
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