model selection strategy

Routine (80%) > DeepSeek at $0.14/M... Moderate (15%) > Sonnet at $3/M... Hard (5%) > Opus at $15/M" — Article provides a concrete example of selecting different models based on task complexity requir

Perplexity Computer runs on Opus 4.6 as its core reasoning engine and automatically selects the best model for specific subtasks. Gemini is used for deep research and creating subagents, Nano Banana f

[direct] "I configured tool-calls that route specific queries to different models: Gemini 3 for UI/UX design tasks, CODEX for certain coding or logic problems where a different perspective helps, Loca

The brain engine running our AI agents are LLMs (Large Language Models). These models are what actually power the agent's ability to reason, plan, and respond like a human would. Without an LLM, an ag

opus 4.7 via github copilot only supports medium thinking" — PSA explicitly states that Opus 4.7 (a specific model version) has a limited thinking capability when accessed via GitHub Copilot. This is

Model limitations where one AI excels but others struggle" — Article directly identifies model selection as a core problem - different models excel at different tasks, requiring strategic choice.

i use opus for everything i love opus 4.7, its an awesome model, and definitely not a regression from 4.6" — Author demonstrates deliberate model selection, choosing Opus 4.7 as the primary model for

On our 93-task coding benchmark, Claude Opus 4.7 lifted resolution by 13% over Opus 4.6, including four tasks neither Opus 4.6 nor Sonnet 4.6 could solve." — Article provides concrete benchmark metric

You can tell the orchestrator which model each teammate should run. For example: the debugger runs on Opus, the UI perf agent on Sonnet, and the UX quality agent on Haiku." — Demonstrates practical pe

[direct] "The model was a modern, frontier class LLM. The answer was still wrong, outdated, or dangerously confident. Trace those failures back and you find something more mundane and more uncomfortab

daily driver: opus 4.5 plan, audit, fix bugs: gpt 5.2 high hardest problems: gpt 5.2 pro" — Real-world demonstration of selecting different models (Opus 4.5, GPT 5.2) based on task complexity and requ

opus 4.5 and codex are a real step up from previous coding models" — Direct assertion that newer models (opus 4.5, codex) represent measurable improvement in coding capability over predecessors

Starting today, our new default agent is Thinking with Gemini 3 Pro." — Demonstrates concrete model selection decision: switching Stitch's default from a previous model to Gemini 3 Pro based on capabi

we verified a preview of an unreleased version of @OpenAI o3 (High) that scored 88% on ARC-AGI-1... Today, we've verified a new GPT-5.2 Pro (X-High) SOTA score of 90.5%" — Concrete empirical demonstra

A year ago, we verified a preview of an unreleased version of @OpenAI o3 (High) that scored 88% on ARC-AGI-1 at est. $4.5k/task. Today, we've verified a new GPT-5.2 Pro (X-High) SOTA score of 90.5% at

High-capability models for managers and complex tasks, efficient models for routine operations." — Article directly articulates the selection strategy: match model capability to task complexity and ro

Your AI Agent Is Failing Because of Context, Not the Model" — Article directly challenges the assumption that model selection is the primary failure point, arguing context engineering is more critical

Swap models without losing agent memory, session state, or historical conversations." — Directly advocates for the capability to swap underlying models while preserving agent state and history.

高难度推理：最强推理模型，用于架构设计、复杂重构、深层 bug 排查；机械性工作：小型高性价比模型，用于代码批量阅读、摘要生成、格式化操作" — Article explicitly discusses matching model capability tiers to task complexity—strongest reasoning models for architectural de

I cut Claude Code Agent Team token usage by 50%+ by switching teammate agents from Claude to @Zai_org's GLM-5." — Article provides concrete example of model substitution strategy in multi-agent teams

It's not just about picking the right model anymore (Haiku, Sonnet, Opus). Now you also need to think about which effort level to use before each task." — Article adds a new dimension to model selecti

[tested_on_hardware] "RTX 3060 12GB - Qwen 3.5 9B Q4 - 50 tok/s - 128K context" — Real-world model selection based on GPU memory constraints (12GB → 9B model)

model provider outages aren't edge cases — they're part of the operating environment" — Article extends model selection strategy by introducing provider redundancy as a practical operational requireme

Multi-provider selection, cost optimization, routing algorithms, dynamic model switching" — Strategy pattern is explicitly documented for dynamic model switching and multi-provider selection in LLM ap

Prisma went from 79% to 0% between model versions. Redis from 93% to 29%. These look less like preference shifts and more like extinction events." — Provides concrete evidence that model versions make

So I had Claude do a bunch of web research and then conduct a "bake off". There are so many options to choose from for both that it's a bit overwhelming if you want to pick the current all-around best

frontier models (Opus 4.5, GPT-5.2, Gemini 2.5 Pro) beating the leading open source models (DeepSeek V3.2, Kimi K2, Llama 4)" — Comparative benchmarking across 11 models provides empirical evidence fo

Conductor supports GitHub Copilot and Anthropic Claude as providers, with per-agent model overrides. You can mix them in a single workflow: run claude-haiku-4.5 for classification, gpt-5.2 for researc

Anthropic models have significantly lower violation rates than other models, with both Opus 4.6 and Opus 4.7 outperforming the rest of the field." — Provides empirical evidence for model selection bas

Claude Sonnet 4.5 — The best combination of speed and intelligence for most uses, including coding and agents." — Article demonstrates practical model selection guidance by comparing Claude versions o

The most powerful model in the world is useless if AI can't understand what you're actually asking for." — Article directly challenges the assumption that model capability/selection is the primary per

Large language models (such as OpenAI's GPT, Anthropic Claude, Google Gemini, Amazon Nova) provide the core reasoning capability for the agent" — Article explicitly identifies LLMs as foundational rea

Claude Code is kind of like if Codex was drunk... bit more creative, makes really dumb mistakes, probably shouldn't be trusted with prod" — Article characterizes Claude Code's unique tradeoff profile

supports multiple authentication methods including Anthropic direct API (API key or workload identity federation), Amazon Bedrock, Google Vertex AI, and Microsoft Foundry" — Claude Code Action enables

One of the most encouraging findings is that open-weight models are rapidly catching up to proprietary models in context engineering capabilities: GLM-4.6 from Zhipu AI achieves 56.83%, demonstrating

We believe in a multi-model future where teams can choose the right model for the task easily. Prompt optimization across models can help make those migrations easier and reduce the amount of manual t

Anthropic's Claude models are often regarded as some of the highest performing models for MCP tool usage, being good at determining when to request a tool, using the tool correctly, and hallucinating

Choosing the right AI model for your project often hinges on one critical specification: the context window. Whether you're processing lengthy documents, maintaining extended conversations, or analyzi

All users now default to `xhigh` effort for Opus 4.7, and `high` effort for all other models." — Article demonstrates model-aware configuration strategy where effort levels are differentiated by model

By investing in sophisticated context engineering, we were able to use Gemini Flash 2.0, a fast, cost-effective model, instead of requiring slower, more expensive models. The heavy lifting happens in

Claude Advisor pairs Opus as strategist with Sonnet or Haiku as executor." — Demonstrates deliberate model selection based on task role: Opus for complex planning, Sonnet/Haiku for faster execution, o

These systems demand dynamic model selection: a reasoning-heavy task might need a strong frontier model, while high-volume or latency-sensitive subtasks benefit from lighter, faster, or cheaper altern

Works with GPT, Claude, Llama, and other models." — LangChain's model agnosticism directly demonstrates the pattern of flexible model selection and switching

swap models anytime (claude, gemini, glm ...)" — Demonstrates practical implementation allowing runtime model selection across different providers without architectural changes

My results were very strong: qwen3.5:9b went from being useless to ~haiku-3.5 thru Claude Code level on cyber security tasks." — Quantified evidence that targeted prompt engineering and context strate

In that world, the most valuable asset an AI company holds isn't the model — it's the memory." — High-profile CEO makes explicit argument that corporate AI value shifts from model ownership to memory

When choosing a model for our agent, we start with correctness. If a model can't reliably complete the tasks we care about, nothing else matters. We run multiple models on our evals and refine the har

Rather than relying on a single model, the LLM Mesh integrates multiple LLMs, each specialized for a specific domain, such as legal analysis, customer sentiment, or technical support." — Article demon

a smaller model like claude 4.5 haiku equipped with high quality skills smokes a raw state of the art opus 4.5 model by about 6 percent (27.7 vs 22.0)" — Article provides quantitative data showing sma

integrate with a variety of LLM providers such as Azure OpenAI or AWS Bedrock" — Article explicitly discusses the ability to choose different LLM providers optimized for specific use cases