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

Does not lock you into any one model. Bring your own API key and run it with GPT 5.6 Sol, Claude Fable, Gemini 3.6, an open weight model (like Kimi, GLM, DeepSeek, Inkling), or Ollama" — OpenWorker is

别再一个模型撸到底，你大部分任务都用不着 Fable 5 和 GPT-5.6 Sol 这么强（贵 !）的模型" — Article explicitly argues against using strongest/most expensive models for all tasks, advocating intelligent model selection based on task re

access to any model you want so when a new model drops you can try it immediately, no new vendors to onboard, just update a string" — Cloudflare AI Gateway demonstrates rapid model switching capabilit

Accuracy alone can't answer that." — Article reveals that traditional accuracy metrics miss critical failure modes in routing decisions (vacuous diversity, policy instability), fundamentally extending

The right model can be matched to the right task, whether that's a frontier model for complex reasoning or a lighter open source model for high-volume, lower-stakes work." — Article demonstrates how G

Sonnet 5 costs less per token than Opus 4.8 but used more tokens, resulting in higher cost and lower quality" — Article provides empirical evidence that per-token cost is misleading metric for model s

Sonnet 5 + a Fable 5 advisor tool gets ~92% of Fable 5's score at ~63% of the price" — Demonstrates practical model selection strategy where a lower-cost model (Sonnet 5) achieves comparable performan

Price per token != cost per task. Haiku is 10x the cost when evaluated on task completion." — Demonstrates that effective model selection requires measuring task-level costs, not just token pricing. E

The common setup is Sonnet as the executor, with Haiku for subagents or simple skills. The advisor pairs Opus on top of either one" — Article demonstrates specific model selection pattern: using Sonne

GLM used twice as many tokens (GLM 1.1m vs Opus 660K) but cost half as much (GLM $0.41 vs Opus $0.81)" — Real-world production test comparing two models on code generation, demonstrating cost-performa

Agent 会在一个任务里反复调用模型（读文件、写代码、调工具、自我纠错），token 消耗因此急剧放大" — Article demonstrates critical constraint: agents with unlimited model access cause uncontrolled token consumption. This directly motivates cost-

stronger model families can reduce the need for elaborate prompting by up to 30 to 50 percent compared with older ones for instruction-following tasks" — Article cites OpenAI guidance showing model ch

I'm mostly using GPT-5.4 right now, with some Opus 4.6 added in. The biggest reason is pretty simple: the larger context window of GPT-5.4 is very useful in this kind of work" — Author demonstrates de

[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

You're paying frontier prices for a model to do work a cheaper model would do just as well... As the frontier gets stronger, the frontier model stops being the thing that writes your code and becomes

Default to medium or high. Both are great. xhigh is incredibly capable but I don't find myself needing it much, even when orchestrating lots of subagents" — Author provides explicit guidance on reason

Fable 编排 + Sonnet worker，96% 分数 / 46% 成本，套利显现" — Article provides empirical data on when to use frontier intelligence (Fable) vs cheaper models (Sonnet), demonstrating cost-aware model selection.

a cost- and performance-aware LLM router" — DAAO implements dynamic LLM assignment based on cost and performance tradeoffs, exemplifying model selection strategy in practice

Anthropic's model boasts a 100K token window, while GPT-4 and Llama offer 128K and 32K respectively. Google's PaLM is available with an 8K window, and Cohere provides just over a 4K window." — Article

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