Home Models Coding Agents Compare Pricing Model Picker Source Data Local Models OpenClaw
Model data verified 2026-05-07

Build better coding agents with the right AI models.

Choose models for coding, agent workflows, long-context analysis, and production API routing. We focus on what developers actually need: code quality, tool use, context, price, and reliability.

Coding-agent focused Model routing guidance API price checks Source-reviewed data
Build coding agents → Pick a model

Verified Leaderboard

2026-05-07
1
GPT-5.5
9.8
2
Claude Opus 4.7
9.7
3
GPT-5.4
9.7
4
Gemini 3.1 Pro Preview
9.4
5
Claude Sonnet 4.6
9.3
View all models → Coding 40% / Reasoning 35% / Tool-use 25%
25
Models in source log
10+
AI providers
4
Core routing roles
Verified
Updated after source review

Start With the Job You Need Done

Skip archive browsing. These are the practical paths for developers choosing models for real systems.

Decision tool

Build Coding Agents

Model routing for planning, coding, review, repo Q&A, bulk edits, and private codebases.
Decision tool

Pick a Model

Answer workflow, budget, context, and deployment questions to get a practical shortlist.
Decision tool

Compare APIs

Compare model scores, context windows, provider tradeoffs, and pricing side by side.

Built for Coding and Agent Workflows

Most model sites stop at rankings. This one is structured around production decisions.

⚙

Agent routing guidance

Separate planning, coding, reviewing, and bulk work instead of forcing one model to do everything.

🔎

Developer-first criteria

Score coding quality, tool use, context, and price together because agent systems need all four.

⚡

Source-reviewed facts

Model availability, context, and pricing are tied back to source data so claims can be audited.

A Better AI Agent Stack

Use the right model for each stage instead of spending frontier-model money on every token.

1

Plan

GPT-5.5 / Claude Opus 4.7

Decompose work, identify files, set constraints, and decide when to escalate.

2

Edit

GPT-5.4 / GPT-5.2-Codex

Generate diffs, run tests, fix failures, and keep changes scoped.

3

Review

Claude Opus 4.7 / Sonnet 4.6

Audit regressions, edge cases, architecture risk, and unclear assumptions.

What We Track

Category Fields
Performance Coding score, tool-use score, reasoning score, context window
Cost Input/output price per 1M tokens, context pricing notes, provider tradeoffs
Latency Best-fit workflow, routing role, escalation path, production caveats
Reliability Source links, verification date, stale-claim audit flags

Provider and Deployment Guides

Go deeper when you already know your provider, budget, or deployment constraint.

Best OpenAI Models

GPT-5.5, GPT-5.4, GPT-5.2-Codex, and more

Best Anthropic Models

Claude Opus 4.7, Sonnet 4.6

Best Google Models

Gemini 3.1 Pro Preview, Gemini 3 Flash, and more

Local Model Guide

Private deployment, open weights, and self-hosting tradeoffs

Cheapest AI Models

Lower-cost API options for high-volume workloads

Source Data

Verification dates, source links, pricing, and context windows

Build a smarter model router

Start with coding-agent routing, then use the model picker to tune for cost, context, and deployment constraints.

Open coding-agent guide →