Grok Code Fast 1

Name: Grok Code Fast 1
Author: xAI

Released

2025-08-27

Last refreshed

2026-07-10

Status

Researched 58d ago

DeprecatedProprietaryCommercial use: conditionalCodingRAGAgentsLong contextClassificationJSON / Tool use

Grok Code Fast 1 is a legacy integration reference; evaluate Grok Build 0.1 before starting new work.

Use it for

Teams maintaining an existing integration
Workloads that can use a 262k context window
Buyers comparing 2 tracked provider routes

Do not use it for

New production launches
Vision or document-understanding workloads

Specifications

Family: Grok 4
Released: 2025-08-27
Context: 262k
Parameters: 314B
Architecture: Mixture of Experts
Specialization: code
Openness: Proprietary
License: ProprietaryCommercial use: conditional
Weights: Not released
Code: Unknown

Created by

xAI

Ethical AI for universal truth-seeking

San Francisco, California, United States

Founded 2023

Website

Pricing

Output / 1M

$1.50

Input / 1M

$0.200

Cheapest of 3 routes · OpenRouter

Providers(3)

OpenRouter Microsoft Foundry xAI Console

View 3 provider routes

Links

Website

This model is deprecated. xAI recommends switching to Grok Build 0.1.

About

xAI's Grok Code Fast 1 is a code-specialized Mixture-of-Experts model released August 27, 2025. It was retired on May 15, 2026; xAI lists grok-build-0.1 as its direct replacement for current coding-agent work.

Grok Code Fast 1 is a proprietary model in the Grok 4 family. The structured metadata tracks a 262k-token context window, function calling, tool use, and structured outputs. This page tracks provider routes through OpenRouter, Microsoft Foundry, and xAI Console, with the cheapest tracked route listed at $0.2 input and $1.5 output per 1M tokens. Headline tracked benchmarks include SWE-bench Verified 70.8.

Top use-case fit: coding, agents, and build tasks

Coding

1 relevant benchmark in the decision map.

RAG

Included by capability and metadata signals in the decision map.

Agents

1 relevant benchmark in the decision map.

Provider price ladder

Compare all 3

Compare API pricing across 2 providers for input and output tokens, batch, and cached reads when available.

Provider	Input / 1M	Output / 1M	Route
OpenRouter	$0.200	$1.50	Serverless
Microsoft Foundry	-	-	ServerlessPartial

Available via routers & gateways(6)

LiteLLM

Gateway

Open-source Python SDK and proxy server that unifies 100+ LLM APIs behind a single OpenAI-compatible interface, with load balancing, cost tracking, and configurable failover.

Free OSSMicrosoft Foundry

OpenRouter

Hybrid

Unified hybrid gateway to 400+ models from 60+ providers via a single OpenAI-compatible API, with optional auto-routing that selects the best model per prompt.

PassthroughxAI Console

Portkey

Gateway

Production AI gateway routing to 1,600+ LLMs with failover, load balancing, semantic caching, and guardrails; Apache 2.0 core is fully self-hostable with the complete feature set.

SubscriptionMicrosoft Foundry

Azure AI Foundry Model Router

Router

Microsoft Azure AI Foundry's native model router that uses a trained ML model to route each prompt in real time to the optimal Azure-hosted model, with Balanced/Cost/Quality mode selection and automatic failover.

PassthroughMicrosoft Foundry

Helicone

Gateway

Observability-first AI gateway with routing, caching, rate limiting, and request tracing; Apache 2.0 open-source core with a managed hosted tier for logging and analytics.

SubscriptionMicrosoft Foundry

Kong AI Gateway

Gateway

Multi-LLM AI gateway built on Kong Gateway 3.x, adding semantic routing, load balancing, guardrails, and MCP traffic analytics as plugins over Kong's existing API management platform.

SubscriptionMicrosoft Foundry

Capabilities

Function CallingTool UseStructured Outputs

Benchmark peer barsfor Coding

SWE-bench VerifiedRank 61 of 80

Claude Fable 5

96.0

Claude Mythos Preview

93.9

Claude Opus 4.8

88.6

Claude Opus 4.7

87.6

Grok Code Fast 1current

70.8

Benchmark scores(1)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.