Many engineers and teams have already integrated AI into their everyday workflows, but when it comes to selecting tools, they still rely mostly on intuition or reputation. This often leads to fluctuating efficiency and uncontrollable costs. In a fast-iterating product market, chasing the “best tool” or blindly following benchmarks tends to focus attention on the tools themselves rather than the core question: what specific work scenarios do we need to accomplish?
A more reliable approach treats tool selection like workflow design: first break down your workflow into clear, distinct scenarios—such as daily Q&A, in-depth research, coding execution, code review and merge—and then for each scenario, match the most appropriate tools and models rather than searching for a one-size-fits-all solution. This makes your decisions explainable, replaceable, and stable over the long term—even if specific products change often, the framework remains valid.
Therefore, I recommend a three-step process when selecting AI tools: clarify the responsibilities and boundaries of each tool → match model capabilities to tasks → design layered procurement and redundancy. The combinations below are based on my personal experience from late 2025 to early 2026.
General AI Assistants: Q&A and Research
These tools don’t interact directly with your code repositories; their roles include answering daily questions, searching information, and supporting deeper, longer-chained research. Separating them from development execution tools helps clarify later selection decisions.
| Tool | Main Use | Notes |
|---|---|---|
| Gemini | Everyday technical Q&A | Globally stable access, top-tier search ability, strong hallucination control, $15/month good bang for the buck |
| Manus | In-depth research (technical reports, investment analysis) | Max model is expensive, but output quality justifies cost—$1 buys a logically clear, well-sourced deep analysis |
| Google NotebookLM | Deep research (knowledge organization and summary) | Uses doc libraries as knowledge sources, supports multi-turn, contextual deep Q&A and synthesis |
Development Tools (IDE/CLI): Execution and Delivery
Once you’re in the core development workflow, you need tools that directly interact with code, repositories, and your review process. This layer determines delivery efficiency.
| Tool | Core Positioning & Notes |
|---|---|
| OpenCode (CLI/Web UI) | Open-source alternative to Claude Code; fast iteration, strong customizability, stable access. Value lies not in single-point abilities but as an easy-to-access, simple-interaction multi-agent collaboration platform |
| GitHub Copilot (Web) | Typical workflow: select repo, describe requirements, launch VM, automatically fix issues, initiate PR review, discuss changes, merge. Suited for medium-complexity, end-to-end automated tasks |
| Zed (Editor) | Lightweight IDE; can integrate OpenCode in sidebar |
| Deprecated |
OpenCode’s key is the oh-my-opencode plugin, a multi-agent collaboration system with fine-grained role division. Although this design leads to frequent agent interactions, longer task chains, and high token consumption, it ensures delivery quality in complex technical scenarios. Once familiar with interaction patterns and matching models to roles, it supports hours-long, continuous autonomous technical discussions.
GitHub Copilot Web represents a different path: highly integrated, scenario-closed, striving for end-to-end automation. It minimizes multi-turn deep interactions and focuses on one-stop solutions from issue description to code merge. For clearly bounded medium-complexity tasks, it delivers extremely high efficiency and integrates seamlessly with GitHub’s development review flow—experience-wise, it’s close to contributing full code to open source projects.
Each has its strengths: OpenCode emphasizes control and depth, GitHub Copilot prioritizes automated closed loops. In practice, teams often use both complementarily—OpenCode for complex architectural discussions, Copilot for specific bug fixes.
Model Selection: Matching Capabilities by Scenario
After fixing tools, the next step is selecting models tailored to each task scenario.
Domain/business understanding tasks are special: they depend entirely on user-supplied context (business docs, historical code, decision records). Here pretrained model knowledge can cause hallucinations, so model choice is less sensitive—window size matters more.
Aside from domain understanding, typical scenarios get assigned different models and strategies:
| Scenario | Preferred Model | Secondary/Backup | Key Considerations & Notes |
|---|---|---|---|
| Task decomposition & workflow planning | Claude Opus 4.6 | Kimi K2.5 | Requires very strong logic and step planning, understanding complex constraints. Opus is near perfect here. |
| Solution and code review | GPT 5.3-Codex | Claude Opus 4.6 | Demands strict code quality review, vulnerability detection, and architectural judgment. |
| Comprehensive development (architecture discussion and coding) | Claude Opus 4.6 | GLM-5-Turbo | Needs instruction-following and deliverable code. GLM-5-Turbo is mostly reliable but less stable. |
| Independent closed problem solving | GPT 5.4 Pro | - | Single-model single-agent, good for one-off answer tasks like implementing an algorithm without external input. |
| Simple code implementation | Kimi K2.5 | MiniMax M2.5 | Highly cost-effective for function filling, simple scripts, data transforms. Requires clear scope and small tasks. MiniMax is faster but rougher. |
| Search & project understanding | Any cheap model | - | Focuses on combining MCP search and language server calls for live info and code semantics; model capability secondary. |
| Multi-language technical writing | Gemini / GPT / Claude | - | Styles differ: Gemini is rigorous, GPT fluent, Claude structurally clear—pick accordingly. |
| Chinese tech writing | Claude Opus 4.6 | GLM-5 | Greater emphasis on logic and accurate expression. |
| Development documentation writing | Claude Opus 4.6 | - | Needs clear, structured expression of complex technical decisions. Opus has strongest logic. |
To set expectations quickly: Opus resembles an experienced senior engineer whose outputs still require end-to-end verification; GLM-5 acts like a competent but less stable engineer; Kimi K2.5 and MiniMax M2.5 are cheap interns reliable only on deterministic tasks; Claude Sonnet/Haiku have many better cost-effective Chinese market alternatives and are generally unnecessary.
Cloud Service Procurement
Once models and tools are chosen, the final question is procurement. Key rule: don’t put your entire budget on a single vendor; diversify purchases by task risk level and keep redundancy on critical paths.
| Service | Monthly Fee | Notes |
|---|---|---|
| Zhipu Coding Plan Pro | ¥499 | Provides GLM-5 and others, but SLA unstable with occasional faults |
| Volcano Ark Coding Plan | ¥200 | Average model with delayed updates; benefits are ample quota and low latency, suited for low-cost bulk tasks |
| GitHub Copilot Pro+ | $39 | High quota, but token consumption triples when used with Claude Opus 4.6; strict cost control required |
| OpenCode Zen | Pay-as-you-go | Multi-model aggregation service, deeply integrated with OpenCode ecosystem for unified management and scheduling |
| PPIO | Pay-as-you-go | Provides GLM-5 service with good latency stability; backup when Zhipu is unstable |
Conclusion
Efficiency gains come not from chasing the strongest model but from continuously decomposing workflows and matching tools/models to scenarios. You don’t need to get everything perfect initially—starting by separating general AI assistants and development execution tools and clearly distinguishing scenarios already beats intuition-based tool selection. Later, refine model division and procurement based on actual pain points and budget.
Note that using multiple tools and models involves switching costs and learning curves, so factor in adaptation time.