DeepSeek-V4-Flash (思考 · API)Normal level for large API models — the 284B parameter advantage is clear. Results on this page are for reference only.

DeepSeek-V4-Flash · OpenCode API · 作为参考reference
🧠 MoE · 284B (13B active) ☁️ OpenCode API 📅 2026-06-19 作为参考reference
加权总分Weighted Total
94.0
加权原始分 = TC×0.3 + BF×0.3 + HA×0.4Weighted raw score = TC×0.3 + BF×0.3 + HA×0.4
实用得分Effective Score
85.0
−9 重试扣分−9 retry penalty
ToolCall-15
100
15/15 通过 · 100% 🏆15/15 passed · 100% 🏆15/15 passed · 100% 🏆 · 重试Retry -2
BugFind-15
93.3
14/15 通过 · 93%14/15 passed · 93% · 重试Retry -5
HermesAgent-20
90
16/20 通过 · 80%16/20 passed · 80% · 重试Retry -2
📊 计分规则Scoring Rules能力上限Max Score = 加权原始分 = ToolCall×0.3 + BugFind×0.3 + HermesAgent×0.4= Weighted raw score = ToolCall×0.3 + BugFind×0.3 + HermesAgent×0.4
实用得分Effective Score = 能力上限 − 重试扣分(每题首次通过不扣分,重试后通过每重试1次扣1分,重试后仍失败不参与扣分)。Max Score − retry penalty (first pass = no penalty, each retry = +1pt, failed retries excluded).

📋 全部测试结果All Test Results

⚡ 此模型Models通过 OpenCode API 测试tested(非本地推理),成绩作为参考reference

全部 (50)All (50)
ToolCall (15)
BugFind (15)
HermesAgent (20)
❌ 失败 (4)❌ Failed (4)
#题目Question测试包Pack难度Difficulty结果Result得分Score重试Retrysandbox耗时Time失败类型Failure Type点评Comment

📊 模型评估总结Model Evaluation Summary

⚡ 此模型Models通过 OpenCode API 测试tested(非本地推理),成绩作为参考reference

优势Strengths

  • ToolCall 满分 100/100,工具调用能力扎实ToolCall full score 100/100, solid tool-calling capability.
  • HermesAgent 90 分,Agent 场景表现突出HermesAgent 90 points, outstanding performance in Agent scenarios.
  • BugFind 93.3 分,通过了 BF-03 TrapBugFind 93.3 points, passed BF-03 Trap.

⚠️ 不足Weaknesses

  • BF-10 Trap 题仍然失败Still failed BF-10 Trap question.
  • HA-07 代码批量处理(30分)和 HA-16 消息投递(30分)是主要失分点HA-07 batch code processing (30 points) and HA-16 message delivery (30 points) are the main point deductions.
  • HA-14 Cron 更新(70分)和 HA-17 并行委派(70分)差一点通过HA-14 Cron update (70 points) and HA-17 parallel delegation (70 points) narrowly missed passing.

📋 测试环境Test Environment

  • 推理方式Inference Method — OpenCode API(云端推理,非本地部署)— OpenCode API (cloud inference, non-local deployment)
  • 测试包Pack — ToolCall-15 / BugFind-15 / HermesAgent-20(共 50 题)— ToolCall-15 / BugFind-15 / HermesAgent-20 (total 50 questions)
  • 标准 — 错题可不断,直到多次后分数不再增加为止Standard — Errors can continue until the score stops increasing after multiple attempts.
  • 模型下载Model DownloadHF: SC117

DeepSeek-V4-Flash 94.0 分,实用 85.0 分(9 次重试)。作为 API 大模型,284B MoE(13B 激活)在参数量和训练数据上远超本地小参数模型。ToolCall 满分、BF 93.3、HA 90,三项均衡且都很强。本页成绩仅供参考。DeepSeek-V4-Flash: 94.0 points, 85.0 effective (9 retries). As a large API model, its 284B MoE (13B activated) parameters and training data far exceed local small models. ToolCall perfect, BF 93.3, HA 90 — well-balanced and strong across all three. Results on this page are for reference only.

一句话评价 TL;DR:API 大模型的正常水平——284B 参数量优势明显,本页成绩仅供参考。TL;DR: Normal level for large API models — the 284B parameter count is significant. Scores on this page are for reference only.