Benchmark comparison of Vector build 1.2 updated with Grok 4.6 vs Grok build 0.3 (Terminal + App)

58.26.***.***
37

Grok Build 0.3 vs VectorBuild 1.2 (Grok 4.6)

Overview

A few days ago, Grok 4.6 was released, and Grok Build was also updated. The performance improvement related to agent coding is noticeable. If you use Grok Build, which is specialized for tasks and agents, you can't just pass it by. Over the weekend, I remotely completed an update of Vector Build to correspond with Grok 4.6 and ran benchmarks. Both Grok Build and Grok 4.6 are definitely better than 4.5.

Again, SpaceXAi, who open-sourced such a high-quality app, is really awesome.

Changes in Time, Cost, Tokens, Calls, and Quality Rate of Vector Build Compared to Grok Build

  • time: -10.15% (VectorBuild is about 10% faster for all 7 tasks)

  • cost: -21.99% (approximately 22% cost reduction)

  • tokens: -25.12% (approximately 25% token usage reduction)

  • calls: -10.67% (model calls decreased by approximately 11%)

  • quality: +0.43 points (average quality slightly increased)

  1. VectorBuild successfully reduced time, cost, tokens, and calls while maintaining and slightly improving quality.

  2. The efficiency reduction is greatest for tokens and costs, with calls and time being relatively moderate. In actual operation, "context retransmission reduction" has the biggest effect (the power of vectorization).

  3. Based on testing and runtime, when VectorBuild On was used, it worked in a way that significantly reduced time, cost, and tokens on average for tasks with clear completion. In other words, it is strong in problems with answers.

  4. On the other hand, for undefined tasks like app UI development, it may fall into over-verification patterns.

One thing I learned is that efficiency can actually decrease if the goal of app development is unclear. It seems that without subdividing tasks through plan mode and workflow mode, vector algorithms cannot exert their power.

Appendix: Grok Build's loop (approximate)

  • Request - Agent's own judgment - File/tool/subagent - Source modification/test - Model judges whether the loop continues

Appendix 2: Vector Build's loop

  • Request - Pre-memory vector injection - Completion condition establishment - Agent judgment - Source modification/test - Incomplete TodoGate - Model judges whether the loop continues - Memory/counterexample addition vectorization after completion

The harnesses I usually used are as follows:

Grok Build / Vector Build (1st tier, overwhelming speed performance)

Claude Code (2nd tier, stability)

Codex (3rd tier, slow but good problem-solving ability)

OpenCode (4th tier, Plan mode is good)

▶ Original source: https://github.com/JUNJOONHWAN/vectorbuild-releases/releases/tag/v1.2.0-vectorbuild.15

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2026.08.19 KEB 하나은행 고시회차 955회

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