Grok 4.6
grok-4.6Compare grok-4.6 API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 500K
- Maximum input window
- Max output
- 450K
- Maximum tokens per response
- Modalities
- →
Pricing by Supplier
| Input/ 1M tokens | Output/ 1M tokens | Cache Read/ 1M tokens |
|---|---|---|
$1.2 | $3.6 | $0.3 |
- Input/ 1M tokens
- $1.2
$2 - Output/ 1M tokens
- $3.6
$6 - Cache Read/ 1M tokens
- $0.3
$0.5
Capabilities / Supported modalities
Provider & data privacy
Grok tokenizer (BPE)Performance
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·Grok 4.6
This model is not included in the current benchmark snapshot.
Missing models or measurements are not zero scores.
Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.
About Grok 4.6
用推理处理技术与知识问题
Grok 4.6 适合代码、科学技术和专业资料分析。它能够根据问题条件推演方案，解释实现逻辑，或把多份材料组织成清楚的结论。开发者可以用它审查算法、排查代码和设计测试，分析人员可以用它比较文档、检查论证与整理技术报告。
它接受文字、图片和文件输入，输出文字。除了粘贴源码或业务规则，也可以提供图表、流程示意或页面截图，让它把可见信息与文字背景联系起来。例如，分析一张监控图与同期日志，提出需要验证的故障假设；阅读技术方案中的结构图，检查组件职责是否与正文一致。
推理强度可按任务设置。涉及较多条件、数学推导或复杂代码时，预留足够的思考与输出空间；简单问题则把范围和答案长度写清楚。对于很长的输入，标注文档名、时间和优先级，让它围绕关键问题建立证据关系，避免混用不同材料中的条件。
函数调用和结构化输出方便它衔接应用工具。模型可以提出查询与操作请求，系统负责实际执行，并返回结果供它继续分析。对于最新信息或实时数据，需要配置相应检索工具；只分析提供的材料时，可以要求结论严格对应输入事实。
使用方式
适合辅助数学或技术学习吗？ 可以要求它写清假设、定义、推导步骤和验证方法，并用另一个例子检查理解。
能看图找出根因吗？ 图片可以提供现象，根因还需要日志、配置或实验来验证；让它区分观察、假设和已确认的结论。
Use cases and prompting
把技术问题写成可检查的推理任务
提供完整条件、数据和需要解释的重点。
这是服务延迟图、连接池配置和同一时段的日志。
请解释延迟上升的可能路径，区分图上可见事实和需要验证的假设。
每个假设列出支持证据、反例与下一项检查，不直接把相关性判定为根因。
最后给出按信息价值排序的排查步骤。
代码任务附失败输入和预期输出；数学任务说明单位与边界条件。需要系统读取结果时定义 JSON 字段，工具调用后核对返回对象和时间范围，再形成结论。
API access
API documentation
Code samples
/v1/chat/completions| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Replace <YOUR_API_KEY> with the API key from your token settings.
Authentication
All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.
Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.
Supported parameters
| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| xAI | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about grok-4.6
What is grok-4.6?
Compare grok-4.6 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call grok-4.6?
Create an API key with access to grok-4.6, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is grok-4.6 priced?
Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.
What is the context window of grok-4.6?
The model catalog lists a context window of 500000 tokens. Check the selected endpoint for request limits.
What is the maximum output of grok-4.6?
The model catalog lists a maximum output of 450000 tokens. Your request settings may set a lower limit.
How should I evaluate grok-4.6 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.