Long-running coding agents
Working through repository-scale implementation, debugging, refactoring, and verification loops when the host provides controlled file, terminal, and test tools.
Independent tool overview
Grok 4.6 at a glance
Grok 4.6 is SpaceXAI's active flagship model for coding, long-running agents, visual and interactive projects, and general knowledge work. It accepts text and images, has a 500,000-token context window, supports four reasoning-effort levels, and can use function calling, structured outputs, web search, X search, and code execution through supported products. It is a capable model, not an autonomous source of truth: benchmark claims are mostly vendor-published, tool actions need least-privilege controls, and important code, research, financial, legal, medical, and operational outputs require independent verification.
Visit the official Grok 4.6 site ↗.jpeg&w=3840&q=75)
- Product type
- Frontier multimodal reasoning model
- Current status
- Active
- Released
- August 12, 2026
- API model
- grok-4.6
- Context window
- 500,000 tokens
- Knowledge cutoff
- January 2026
- Input
- Text and images
- Output
- Text
- Reasoning
- Low, medium, high, or xhigh
- Standard API price
- $2 input, $0.50 cached input, $6 output per 1M tokens below 200K prompt tokens
- Consumer access
- Included with SuperGrok at $30/month
- Reviewed
- August 31, 2026
Overview
What Grok 4.6 is
SpaceXAI released Grok 4.6 on August 12, 2026 as the successor to Grok 4.5, with an emphasis on sustaining work across many agent steps and producing stronger first passes for coding, visual interfaces, research, and knowledge work.
The direct API model name is `grok-4.6`. Official documentation lists a 500,000-token context window, a January 2026 knowledge cutoff, text and image input, text output, and reasoning effort values of low, medium, high, and xhigh, with high as the default.
Grok 4.6 can be called through the Responses API or Chat Completions and supports developer-defined functions, JSON-schema structured outputs, web search, X search, and code execution. Long-running applications can also use prompt caching and context compaction to reduce repeated context cost and latency.
The model is available through the SpaceXAI API, Grok Build and its CLI, Cursor, GitHub Copilot, Gemini Enterprise Agent Platform, and model gateways including OpenRouter, Vercel, and Cloudflare. Availability, tool behavior, privacy, limits, and pricing differ across those hosts.
The announcement reports strong coding and agent benchmark results and describes improved self-testing, but those figures are point-in-time measurements under particular harnesses. Teams should evaluate the exact prompts, tools, repositories, languages, latency targets, failure costs, and human-review workflow they plan to use.
Use cases
Who Grok 4.6 is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Interactive application prototypes
Turning a concrete product idea into an initial web or visual experience and iterating on structure, behavior, and presentation.
Tool-using research
Combining reasoning with current web or X search, code execution, and structured extraction while preserving source-level human review.
Large-context knowledge work
Analyzing substantial document, image, code, or conversation context that fits inside the 500K-token window.
Structured automation
Returning schema-constrained objects after using developer functions or supported server-side tools.
Multi-model development stacks
Using Grok 4.6 through Cursor, GitHub Copilot, Gemini Enterprise Agent Platform, or a model gateway alongside other providers.
Capabilities
Core Grok 4.6 features
500K-token context
Supports large prompts and long agent trajectories, with higher long-context pricing once the prompt reaches 200,000 tokens.
Configurable reasoning effort
Lets API users trade latency and token use against deeper reasoning with low, medium, high, or xhigh settings.
Text and image understanding
Accepts written and visual inputs and returns text, enabling document, screenshot, diagram, and interface analysis.
Agentic coding
The model was trained and evaluated for multi-step software work and is the default model in Grok Build.
Function calling
Can request developer-defined functions with structured parameters; the application remains responsible for validating and executing the call.
Structured outputs
Can return JSON matching a supported schema, including after tool use, which reduces parsing failures but does not guarantee factual correctness.
Web and X search
Supported xAI tools can retrieve current web and X material for grounded answers, subject to source quality and search coverage.
Code execution
Supported hosts can let the model calculate, inspect, test, or transform data in an execution environment.
Prompt caching
A stable cache key can route conversation requests consistently and improve cache-hit economics for repeated context.
Context compaction
The API can replace a long conversation history with an opaque compacted item that preserves salient state for continued work.
OpenAI-compatible access
The API supports OpenAI-style clients through the SpaceXAI base URL as well as xAI's own SDK.
Partner availability
The model is offered in Cursor, GitHub Copilot, Gemini Enterprise Agent Platform, and gateways such as OpenRouter, Vercel, and Cloudflare.
Regional clusters
The direct model page currently lists us-east-1 and us-west-2 regions.
Process
How the Grok 4.6 workflow works
Step 1
Choose the right surface
Use Grok chat for individual work, Grok Build or an IDE host for controlled code tasks, and the API for applications requiring explicit prompts, tools, logging, and evaluation.
Step 2
Define the contract
Specify the objective, inputs, sources, tools, output schema, constraints, prohibited actions, success checks, latency and cost budget, and when a human must approve.
Step 3
Minimize context
Send only the necessary files and records. Remove secrets, personal data, irrelevant logs, generated artifacts, vendor code, and stale conversation turns before paying for a large prompt.
Step 4
Set reasoning deliberately
Start with low or medium for simple transformations and classification, and reserve high or xhigh for difficult reasoning where measured quality justifies added latency and output tokens.
Step 5
Constrain tool permissions
Give each tool a narrow schema, allowlist, account, directory, network scope, budget, and timeout. Keep destructive, financial, publishing, identity, and production actions behind explicit approval.
Step 6
Treat retrieved text as untrusted
Web pages, X posts, repository files, emails, and documents can contain prompt injection. Separate data from instructions and do not let retrieved content expand tool authority.
Step 7
Require citations and evidence
Ask the model to link claims to retrieved primary sources, exact file paths, test output, or calculations, then independently open and verify the evidence.
Step 8
Use schemas for automation
Request strict structured output, validate it again in application code, reject unexpected enums or ranges, and never treat schema compliance as proof that the values are true.
Step 9
Verify code in the real environment
Review diffs, dependencies, licenses, migrations, permissions, and commands; then run formatting, types, tests, builds, security checks, accessibility tests, and runtime acceptance.
Step 10
Evaluate representative tasks
Build a versioned test set with normal, ambiguous, adversarial, long-context, image, tool-failure, and high-impact cases. Compare quality, cost, latency, and reviewer effort with alternatives.
Step 11
Control cost and state
Track prompt, cached, reasoning, tool, and output usage. Use cache keys and compaction carefully, and test whether compressed state preserves every fact and instruction your workflow needs.
Step 12
Monitor and roll back
Pin the model name where possible, log model and prompt versions, sample production outputs, alert on tool and quality failures, cap spend, and keep a tested fallback for provider or behavior changes.
Cost
Grok 4.6 pricing and free plan
Grok 4.6 is available through consumer subscriptions, the direct usage-based API, and partner products. Direct API requests below 200,000 prompt tokens cost $2 per million input tokens, $0.50 per million cached input tokens, and $6 per million output tokens. At 200,000 prompt tokens or more, all tokens in the request use the long-context rates of $4, $1, and $12 respectively. Tool charges and partner markups or plan allowances can be additional; the launch post also advertises a faster service option at twice the standard token price.
Grok Free
$0/month
General consumer Grok access with limited usage; the current pricing page does not promise Grok 4.6 access on this tier.
- Limited real-time web and X search
- Voice mode
- Connectors
- Use the current model picker to confirm availability
SuperGrok
$30/month
Consumer plan that explicitly includes Grok 4.6 and higher limits.
- Grok 4.6 access
- Grok Bot access
- Connectors
- Expert
- Image and video generation
- Higher limits
SuperGrok Plus
$100/month
Higher-capacity consumer plan for frequent chat, media, voice, and build usage.
- Everything in SuperGrok
- Significantly higher usage
- 1080p video creation
- Faster replies
- Peak-time priority
- Early access
Direct API, short context
$2 input / $0.50 cached / $6 output per 1M tokens
Applies when the prompt is below 200,000 tokens.
- Model: grok-4.6
- 500K maximum context
- Tool usage can add separate charges
- Fast service option is advertised at twice the price
Direct API, long context
$4 input / $1 cached / $12 output per 1M tokens
Applies to all tokens in a request once the prompt reaches 200,000 tokens.
- Long-context threshold is 200K prompt tokens
- Cached tokens also use the higher rate
- Output is billed at $12 per million
- Compact or trim context when appropriate
Business and Enterprise
Plan or contract-dependent
Team and enterprise products with administration, security, support, and custom infrastructure options.
- Role-based access controls
- SSO and SCIM on eligible plans
- Custom retention and ZDR options
- Dedicated infrastructure and data residency can be negotiated
- Contact sales for pricing
Pricing checked . Check current pricing at the source ↗
Assessment
Grok 4.6 strengths and limitations
Where it stands out
- Large 500K-token context window for substantial documents, codebases, images, and long tool trajectories.
- Four reasoning levels provide more control than a single fixed thinking mode.
- Strong vendor-reported results across coding, agent, knowledge-work, and visual-project evaluations.
- Text and image input supports mixed document, screenshot, diagram, and interface tasks.
- Function calling, structured outputs, search, and code execution support production agent designs.
- Schema-constrained output can reduce brittle parsing and make validation easier.
- Prompt caching and context compaction help manage cost and latency in long-lived conversations.
- OpenAI-compatible endpoints lower migration work for some existing applications.
- Broad host availability lets teams evaluate the same model in API, IDE, build-agent, gateway, and enterprise-agent contexts.
- Direct short-context API pricing is competitive for a flagship model.
- The direct API documentation clearly publishes the 200K long-context threshold and higher rates.
- API customer data is not used for training without explicit permission, and zero-data-retention options exist for eligible teams.
What to consider
- Grok 4.6 can hallucinate facts, citations, APIs, packages, test results, user intent, and tool outcomes despite the vendor's improved non-hallucination claims.
- Most headline capability and benchmark results in the launch materials are vendor-reported and depend on particular prompts, harnesses, reasoning settings, budgets, and scoring methods.
- The reviewed official materials did not surface a dedicated public Grok 4.6 system card with the detailed safety breakdown available for some earlier Grok releases.
- A January 2026 knowledge cutoff means later facts require current tools or supplied sources, and search results still require source-quality checks.
- A 500K context capacity does not guarantee reliable recall, attention, chronology, or instruction following across every part of a very large prompt.
- Requests at or above 200K prompt tokens double direct input, cached-input, and output rates for the entire request, not just the excess portion.
- There is no fixed text output limit in the current documentation, but extremely long outputs increase cost, latency, review burden, truncation risk in clients, and opportunities for errors.
- The direct model page says Batch API is not supported for Grok 4.6.
- Text is the only model output modality; image and video generation use separate Grok Imagine models and pricing.
- Tool calls can cause real side effects, and structured arguments can be valid JSON while still being malicious, unauthorized, or factually wrong.
- Web pages, X posts, emails, documents, code, and tool results can carry prompt injection or manipulated evidence into an agent loop.
- Context compaction is opaque and may omit a detail, exception, refusal, provenance link, or approval state that matters later.
- Caching depends on stable routing and prompt structure; cache misses can make actual cost higher than a simple estimate suggests.
- Availability, data handling, context, tools, model routing, and pricing can differ in Cursor, GitHub Copilot, Gemini Enterprise Agent Platform, and third-party gateways.
- Consumer Grok content may be used for model training unless the user changes data controls or uses Private Chat; Build mode has different persistence behavior and does not currently support Private Chat.
- Direct API requests are stored by default for 30 days for abuse auditing unless an eligible zero-data-retention configuration is enabled.
- ZDR disables or changes features that depend on stored state, including some Responses, Files, Collections, Batch, and media workflows.
- Grok 4.6 is not a qualified medical, legal, financial, compliance, security, or engineering authority and should not make unsupervised high-impact decisions.
- Model, provider, policy, and partner behavior can change quickly, so production applications need regression tests, version records, spend caps, and fallbacks.
Compare
Grok 4.6 alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Consumer
GPT-5.2
Choose GPT-5.2 when an established OpenAI API model and its broader product ecosystem better fit the application's existing stack.
Explore GPT-5.2 →Project Management
Claude
Choose Claude when Anthropic's model behavior, long-document work, coding ecosystem, and enterprise controls better match the team.
Explore Claude →Consumer
Gemini 3.1 Pro
Choose Gemini 3.1 Pro when Google Cloud integration, multimodal workflows, and Gemini's surrounding agent platform are the priority.
Explore Gemini 3.1 Pro →Consumer
Grok 4.3
Choose Grok 4.3 for a lower-cost, 1M-context Grok option when maximum Grok 4.6 agent performance is unnecessary.
Explore Grok 4.3 →Coding
Cursor
Choose Cursor when the main requirement is a complete repository-aware coding product rather than direct access to one model.
Explore Cursor →Questions
Grok 4.6 FAQs
What is Grok 4.6?
Grok 4.6 is SpaceXAI's current flagship model for coding, agentic tasks, visual and interactive projects, and general knowledge work.
When was Grok 4.6 released?
SpaceXAI announced and released it on August 12, 2026.
What is the Grok 4.6 context window?
The direct API documentation lists 500,000 tokens.
What is its knowledge cutoff?
The current developer guide lists January 2026. Use search or supplied primary sources for later information.
Does Grok 4.6 accept images?
Yes. It accepts text and image inputs and produces text output.
Can Grok 4.6 generate images or video?
Not through the `grok-4.6` model itself. SpaceXAI offers separate Grok Imagine image and video models and consumer features.
What reasoning settings are available?
Low, medium, high, and xhigh are supported, with high documented as the default.
How much does the Grok 4.6 API cost?
Below 200K prompt tokens, direct pricing is $2 input, $0.50 cached input, and $6 output per million tokens. At 200K prompt tokens or more, the rates rise to $4, $1, and $12 for the entire request.
Is Grok 4.6 available for free?
The launch offered free trials in supported products, but the current consumer pricing page explicitly lists Grok 4.6 under the $30-per-month SuperGrok plan, not the Free tier. Partner allowances vary.
Where can I use Grok 4.6?
It is available through the SpaceXAI API, Grok Build, Cursor, GitHub Copilot, Gemini Enterprise Agent Platform, and gateways including OpenRouter, Vercel, and Cloudflare.
Does Grok 4.6 support structured JSON?
Yes. Supported structured-output schemas can enforce the output shape, including after tool use. The application must still verify values and business rules.
Does xAI train on API data?
SpaceXAI says it does not train on API inputs or outputs without explicit permission. Direct requests are retained by default for 30 days for abuse auditing unless an eligible ZDR configuration is enabled.
Does xAI train on consumer Grok chats?
Consumer content may be used unless the user turns off Improve the Model or uses Private Chat where available. Grok Build sessions have separate persistence rules and currently do not offer Private Chat.
Can Grok 4.6 run tools safely on its own?
No model should receive broad unsupervised authority. Validate arguments, isolate execution, restrict credentials and network access, cap steps and spend, and require approval for external side effects.
Should I choose Grok 4.6 from benchmark scores alone?
No. Run representative evaluations in the exact host and tool setup you will use, including failures, cost, latency, reviewer effort, and adversarial inputs.
Bottom line
Our Grok 4.6 verdict
Grok 4.6 is a credible flagship choice for teams that need long-context coding, tool use, structured automation, or rapid visual application work, especially when they already use Cursor, Grok Build, or the SpaceXAI API. Its 500K context, four reasoning levels, and competitive short-context price are attractive. The long-context price step, opaque compaction, partner differences, limited public safety detail, and ordinary model failure modes make disciplined evaluation essential. Give it narrow tools, verified sources, typed outputs, hard budgets, and a human approval path—not blanket authority.
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