The problem with "just use the best model"

In 2026 developers have access to 15+ production-quality LLMs. The instinct is to default to the strongest one for everything. That's expensive and often wrong — a model optimised for long-context reasoning is overkill for a one-line fix, and a fast cheap model is the wrong choice for a complex security audit.

The answer is routing. Know what each model is good at. Send the right task to the right model.

The model landscape

A practical overview of the models available in 2026 and where each one earns its place.

Model	Provider	Best for	Context
Claude Opus 4.7	Anthropic	Architecture reviews, complex refactors, security audits, multi-file changes	200k
Claude Sonnet 4.6	Anthropic	Day-to-day coding, PR reviews, agent loops, RAG pipelines	200k
Claude Haiku 4.5	Anthropic	Classification, routing, quick lookups, high-volume pipelines	200k
GPT-4o	OpenAI	Vision tasks, tool-use agents, OpenAI-native integrations	128k
GPT-4o mini	OpenAI	Simple completions, embeddings, lightweight agents	128k
o3 / o3-mini	OpenAI	Maths, logic puzzles, step-by-step planning	200k
Gemini 2.5 Pro	Google	Document analysis, 1M+ token codebases, multimodal	1M
Gemini Flash 2.0	Google	High-throughput tasks, batch classification	1M
DeepSeek R1	DeepSeek	Reasoning chains, maths, logic — at open-source cost	128k
DeepSeek V3	DeepSeek	Code completion, agentic coding tasks	128k
Mistral Large	Mistral	Regulated industries, EU data residency requirements	128k
Mistral 7B / 8x7B	Mistral	On-prem inference, privacy-sensitive workloads	32k
Llama 3.3 70B	Meta	Air-gapped environments, fine-tuning, research	128k
Llama 3.1 8B	Meta	Edge inference, local dev, experimentation	128k
Codestral	Mistral	Code completion, FIM (fill-in-middle), IDE integrations	32k

Which model for which task

Complex reasoning / architecture design

Use Claude Opus 4.7 or o3. These tasks need sustained multi-step reasoning across large context. Don't cheap out here — a bad architectural decision costs more than the token bill.

Day-to-day coding (PR review, bug fix, refactor)

Use Claude Sonnet 4.6 or GPT-4o. The sweet spot of quality and speed for the tasks that make up 80% of a developer's day.

High-volume classification / routing / triage

Use Claude Haiku 4.5 or GPT-4o mini or Gemini Flash 2.0. Fast, cheap, more than good enough for yes/no and category decisions. Running 10k classifications a day? This is where your model bill lives.

Long document / large codebase analysis

Use Gemini 2.5 Pro. The 1M context window is a genuine differentiator for whole-repo analysis, large PRD documents, or compliance scanning.

Maths, logic, step-by-step planning

Use o3 or DeepSeek R1. Chain-of-thought reasoning is where these models separate themselves. DeepSeek R1 is particularly compelling because it's open-weights — you can self-host and match o3 quality at a fraction of the cost.

Code completion / IDE integration

Use Codestral or Copilot (GPT-4o base). Purpose-built for fill-in-middle and fast completions. Codestral is optimised for this use case specifically.

GDPR / EU data residency requirements

Use Mistral Large. EU-based infrastructure, strong multilingual support. The right choice when your data can't leave Europe.

Air-gapped / self-hosted / privacy-sensitive

Use Llama 3.3 70B (self-hosted) or Mistral 7B/8x7B. Open-weights models you run on your own infrastructure. No API calls, no data leaving your perimeter.

Multimodal (images, diagrams, screenshots)

Use GPT-4o or Gemini 2.5 Pro. Both handle vision well. GPT-4o is more mature for tool-use with vision; Gemini shines on large documents with embedded images.

Getting started — code examples

Quick-start snippets for each major provider.

Claude (Anthropic SDK) — docs

import anthropic

client = anthropic.Anthropic()

message = client.messages.create(

model="claude-sonnet-4-6",

max_tokens=1024,

messages=[{"role": "user", "content": "Review this PR diff for security issues."}]

)

print(message.content[0].text)

OpenAI — docs

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(

model="gpt-4o",

messages=[{"role": "user", "content": "Explain the architecture of this codebase."}]

)

print(response.choices[0].message.content)

Google Gemini — docs

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")

model = genai.GenerativeModel("gemini-2.5-pro")

response = model.generate_content("Summarise this 500-page compliance document.")

print(response.text)

DeepSeek — docs

# DeepSeek is OpenAI-compatible

from openai import OpenAI

client = OpenAI(

api_key="YOUR_DEEPSEEK_KEY",

base_url="https://api.deepseek.com"

)

response = client.chat.completions.create(

model="deepseek-reasoner",

messages=[{"role": "user", "content": "Solve this step-by-step: ..."}]

)

print(response.choices[0].message.content)

Mistral — docs

from mistralai import Mistral

client = Mistral(api_key="YOUR_MISTRAL_KEY")

response = client.chat.complete(

model="mistral-large-latest",

messages=[{"role": "user", "content": "Classify this support ticket."}]

)

print(response.choices[0].message.content)

Llama via Ollama (local) — ollama.com

# Pull and run locally

ollama pull llama3.3

ollama run llama3.3 "Fix this bug in my Python script: ..."

import ollama

response = ollama.chat(

model="llama3.3",

messages=[{"role": "user", "content": "Refactor this function."}]

)

print(response["message"]["content"])

The problem nobody talks about: running 3+ models at once

In practice, most engineering teams don't use one model. They use Claude Code for agentic tasks, Copilot for completions, GPT-4o for specific tooling, and a self-hosted Llama for sensitive work. Each model has different:

Cost curves (you need per-model spend visibility)
Risk profiles (some models are more likely to write insecure code, hallucinate package names, or suggest overly permissive auth patterns)
Data handling policies (you may not want certain code sent to certain providers)

When developers run AI tools without governance, these differences are invisible. You don't know which tool caused the bad suggestion. You don't know which developer's session is costing $200/day. You don't know if a model suggested code that should never have left your perimeter.

This is the gap ConductGuard closes.

How ConductGuard helps when you run multiple models

ConductGuard sits between your developers and every AI coding tool they run — Claude Code, Codex, Cursor, and any tool that hooks into your IDE. It gives you three things:

1. Per-tool, per-developer spend visibility

See exactly how much each developer is spending per tool per day. Set hard caps. Get Slack alerts before budgets blow. The spend breakdown works across models — you can see your Claude bill vs. your OpenAI bill vs. your Codex usage in one dashboard.

2. Policy enforcement across every model

Write a rule once — "never send files from /secrets/ to any model", "block any tool call that modifies production infrastructure without approval", "warn on any model output that contains hardcoded credentials" — and it applies to every tool. The rule doesn't care whether the output came from Claude or GPT-4o.

3. Audit trail for every AI decision

Every tool call, every model output, every decision (allowed / blocked / warned) is logged with: developer identity, tool name, model used, timestamp, cost, and the rule that triggered. If something goes wrong, you can trace it in seconds.

# install and sync in 30 seconds

pip install conduct-cli

conduct guard sync

# ✓ Hook registered in Claude Code

# ✓ Hook registered in Codex

# ✓ Policies synced (8 rules active)

# ✓ Spend tracking active

Which AI model should you use — and for which task?

The problem with "just use the best model"

The model landscape

Which model for which task

Getting started — code examples

The problem nobody talks about: running 3+ models at once

How ConductGuard helps when you run multiple models

Run multiple AI models? Guard them all from one place.