Stop Picking One AI Model: A Routing Guide for Coding Work in 2026

Every week someone asks the same question in a forum thread somewhere: which AI model should I use for coding? And every week the answers contradict each other, because the people answering are doing different work with different budgets and different constraints. I went through ten reviewer tests to find out where the disagreements actually come from, and the answer is cleaner than those threads suggest.

The disagreements come from the job. Change the job, and the winning model changes too. So instead of a ranking, here's a routing guide. Find your bottleneck, take the model that solves it.

If your bottleneck is raw quality: Claude Opus 4.8

When a refactor sprawls across forty files and the codebase is held together with duct tape, Opus 4.8 is the consensus pick. Alex Finn, Brian Casel, and AI Master all name it the strongest model for multi-file repository work, and it leads SWE-bench Verified at roughly 87.6%.

Two refinements from the reviews:

• Harness matters. Maximilian Schwarzmüller and Brian Casel both argue Claude Code is the best harness for Opus, since Anthropic built the model and the tool. Third-party harnesses run the same weights with worse results, because the models are trained on Claude Code's tool interface.
• Don't burn Opus on everything. Nate Herk found Sonnet handles most work fine and the Opus quota on the $200 Max plan evaporates fast. The pattern that works: Opus for planning, Sonnet for execution. Claude Code's plan mode explores the codebase and asks clarifying questions before touching files, which is exactly what makes the split affordable.

If your bottleneck is the bill at renewal: GPT-5.5

Nate Herk, Theo, and Steve from Builder.io measured the same thing independently: GPT-5.5/Codex uses roughly half the output tokens of Claude Opus on the same task, delivers comparable quality, and costs about half as much. On the $20 Plus plan, Codex feels more generous than Claude Code at the same price, and that's structural, not marketing. OpenAI builds for token efficiency.

Where it loses: creative front-end work. Nate Herk's live tests had Claude ahead on visual polish, and Builder.io switches to Sonnet for design-to-code because it beats both Opus and GPT-5 on that task. GPT-5.5 is the pick for reasoning-heavy, token-sensitive work.

If your bottleneck is the invoice itself: GLM 5.2 or DeepSeek V4

The 80%-quality-at-5-20%-price tier is real now.

• GLM 5.2. Vaibhav Sisinty reports it's the only Chinese model ranking second to Claude Opus 4.8 in blind tests. Nate Herk puts its coding ability at roughly Claude 4.7's level. Cost: $1.2 per million input and $4.10 per million output, versus $5 and $25 for Opus.
• DeepSeek V4. Alex Finn's numbers: 1-10% of Claude Code's cost while reaching 80% of Opus quality. Nate Herk measured ~60K tokens on a project where Sonnet 4.6 used 70K+ and Opus 4.7 used 100K+. At ¥2 RMB per million output tokens, it's the cheapest viable model on the market. Caveat: V4 Pro hit conversation interruptions inside coding agents, and Claude still wins visual taste. On logic-heavy functional work, DeepSeek is the stronger pick, price aside.

How to choose between them: DeepSeek wins on raw price. GLM wins on benchmark tier. Cost-optimized workflows often route between the two.

If your bottleneck is "the code can't leave this machine": OpenCode + local

OpenCode is the MIT-licensed agent where you bring your own model or run local via Ollama. $0 recurring, fully private, self-hosted. Julian Goldie and NeuralNine both cover it as the leading open-source option.

Can it replace Claude Code? Unsupervised Learning reported a full replacement in their workflow, with the only failures being Anthropic API overloads. NeuralNine pushes back: emotional favorite, not the practical choice. Set expectations for local models before you commit. NeuralNine's local testing called DeepSeek garbage and Qwen buggy, while Nate Herk rates GLM-5.2 at Claude 4.7's level. True local-only gets you 60-70% of Opus quality. Open weights over an API gets you 80-90%.

Structurally, OpenCode is the only tool in this space that has to be profitable on its own. Everything else is VC or Microsoft subsidized.

If your bottleneck is speed inside the IDE: Cursor

Tech With Tim found Cursor's composer 2.5 model matches or beats frontier models, and his direct comparisons of the same model in Cursor versus Claude Code showed a noticeable difference from the harness alone. Codevolution takes the other side: Claude Code is better for autonomous end-to-end feature work, Cursor makes you a faster coder. For inline autocomplete, the consensus picks are Claude Haiku 4.5, GPT-5.4 mini, and Gemini 3.5 Flash. Don't pay Opus prices for tab completion.

The comparison at a glance

The routing pattern that survived every review

Nate Herk, NeuralNine, and Theo all land on the same conclusion: there's no single best model in 2026, because the right pick depends on whether your bottleneck is quality, cost, openness, or workflow. The dominant pattern is multi-model routing. A primary model for the hard 20% of work, a fallback for the daily 80%.

Theo's production routing table is the concrete version: feature work and shell tasks to GPT-5.5, refactoring and code review to Claude Opus. Quality-sensitive teams pair Claude 4.8 with Claude Code, or GPT-5.5 with Codex CLI. And keep an escape hatch installed: Evan Burger's argument is that single-vendor lock-in is a strategic vulnerability when model quality fluctuates month to month, which Opus 4.7 demonstrated in public.

This guide condenses the full topic breakdown, which carries per-claim citations and timestamps for everything above.

Pick a rotation, not a winner.