模型詳情
o4-mini
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.
模型規格
- 上下文長度
- 200K
- 最大輸出
- 100K
- 輸入/輸出模態
- 文字 / 圖片
- 發布日期
- 2025-04
02
API 端點
單一 MixRoute 閘道,OpenAI 相容
-
OpenAI-compatible
/v1/chat/completionsPOST
03
價格
固定費率,單位:/1M Tokens
輸入
$1.1000 /1M Tokens
補全
$1.1000 /1M Tokens
05
選型摘要
快速判斷是否適合你的工作負載
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.
08
Token 成本估算
基於本頁價格的即時估算,非實際帳單
此模型沒有快取讀取價格,不計算快取費用
單一端點,可驗證的決策
使用相同的請求格式測試此模型與替代路由
從真實工作負載開始,再由品質、總成本與故障條件決定是否導入正式環境流量。