模型详情
o4-mini-2025-04-16
OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains. Despite its smaller size, o4-mini exhibits high accuracy in STEM tasks, visual problem solving (e.g., MathVista, MMMU), and code editing. It is especially well-suited for high-throughput scenarios where latency or cost is critical. Thanks to its efficient architecture and refined reinforcement learning training, o4-mini can chain tools, generate structured outputs, and solve multi-step tasks with minimal delay—often in under a minute.
模型规格
- 上下文长度
- 200K
- 最大输出
- 100K
- 输入/输出模态
- 文本 / 图像
- 发布日期
- 2025-04
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API 端点
单一 MixRoute 网关,OpenAI 兼容
-
OpenAI-compatible
/v1/chat/completionsPOST
03
价格
固定费率,单位:/1M Tokens
输入
$1.1000 /1M Tokens
补全
$1.1000 /1M Tokens
05
选型摘要
快速判断是否适合你的工作负载
What is this model good for?
Fast, cost-efficient reasoning with tool use
Use o4-mini for high-throughput reasoning tasks where latency and cost are critical. It supports tool use, structured output, and multimodal inputs, making it suitable for agentic workflows at scale.
What should you check before using it?
Confirm tool use and structured output capabilities
Test your specific tool definitions, structured output schemas, and multimodal inputs. o4-mini is optimized for speed—verify it meets your accuracy requirements for your most demanding tasks.
Why use it through MixRoute?
Use the compatible endpoint with stable model ID
Use the confirmed compatible endpoint with model ID o4-mini through MixRoute, then run the same integration tests used for the current client.
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Token 成本估算
基于本页价格的实时估算,非实际账单
此模型没有缓存读取价格,不计算缓存费用
单一端点,可验证的决策
使用相同的请求格式测试此模型和备用路由
从真实工作负载开始,再由质量、总成本和故障条件决定是否接入生产流量。