Grok 4.5
grok-4.5Compare grok-4.5 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 4.5
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 4.5
将问题条件转成清楚的分析
Grok 4.5 适合需要推理的编程和知识任务。你可以用它解释算法、检查技术方案、分析代码问题，或阅读专业资料并整理观点。对数学和科学技术内容，可以要求它写清前提与步骤，再通过样例或计算检验结论。
它支持文字、图片和文件输入，输出文字。图表、流程图和截图可以与文档放在一起分析，例如比较图中实验结果与文字说明，或根据界面截图和组件代码找出状态处理的差异。请求中标注关注区域、页码和文件名，能帮助结果与材料对应。
代码任务适合提供真实输入、错误信息和预期行为，再要求定位问题与最小修复。技术方案可以指定比较维度，如兼容性、资源消耗、失败恢复与实施成本，让它逐项解释取舍。这样比要求泛泛给出“最佳方案”更容易获得可执行建议。
它提供多档推理强度，也支持函数调用和结构化输出。应用可以为它配置查询、计算或业务工具，再把真实返回结果加入后续分析。较长的上下文便于保留相关材料，但应明确此次判断依据，避免新旧规则或不同时间的数据混在一起。
学习与工作问题
怎样检查推导是否可靠？ 要求给出定义、假设、单位和关键步骤，再用边界值或独立计算验证。
能直接生成可用代码吗？ 可以生成实现与测试建议，实际可用性仍需在目标环境中运行，特别要验证依赖、异常输入和兼容行为。
Use cases and prompting
用具体条件限定分析
提供完整问题与材料，说明希望比较或验证什么。
请比较这两个缓存更新方案。
系统采用数据库主写、Redis 查询，允许短暂延迟但不能长期显示旧值。
分别分析写入失败、缓存删除失败、并发读写和进程崩溃后的行为。
用时间线解释风险，给出验证实验与推荐方案，不假定消息投递绝对可靠。
图片分析标出重点，长文资料说明版本和有效范围。复杂问题可提高推理强度；需要程序处理结果时定义 JSON 字段，并让工具结果与最终结论保持对应。
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-4.5
What is grok-4.5?
Compare grok-4.5 API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call grok-4.5?
Create an API key with access to grok-4.5, 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-4.5 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-4.5?
The model catalog lists a context window of 500000 tokens. Check the selected endpoint for request limits.
What is the maximum output of grok-4.5?
The model catalog lists a maximum output of 450000 tokens. Your request settings may set a lower limit.
How should I evaluate grok-4.5 for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.