I spent a week watching ten different reviewers throw real work at the same five models: Claude Opus 4.8, GPT-5.5, GLM 5.2, DeepSeek V4, and OpenCode with a local model behind it. Nate Herk put in 100 hours. Theo compared Claude Code, Codex, and Cursor back to back. Brian Casel moved his whole product workflow over to Claude Code. Different setups, very different conclusions, and one finding every channel agreed on: no single model wins everywhere in 2026.
So I'm not going to crown a champion. You get a routing table instead.
The short version: grab Opus 4.8 when a refactor touches half the repo, use GPT-5.5 for everyday reasoning where token burn matters, drop down to GLM 5.2 or DeepSeek V4 when 80% of the quality at 5-20% of the price is good enough, and run OpenCode with a local model when the code can't leave your machine. I pulled all of this from a cross-analysis of ten expert reviews, and the pattern held on every channel.
Opus 4.8 owns the messy work
When a refactor sprawls across forty files and the codebase is held together with duct tape, reviewers converge hard on Opus 4.8. Alex Finn, Brian Casel, and AI Master all name it the strongest model for multi-file repository work. Maximilian Schwarzmüller and Brian Casel add a second layer: Claude Code is the best harness for Opus because Anthropic built both, and that tight integration is something third-party tools can't copy.
The price premium is where reviewers split. Alex Finn calls Opus "10× smarter than Sonnet." Nate Herk pushes back: Sonnet handles most work fine, and the Opus quota on the $200 Max plan evaporates fast if you lean on it for everything. The pattern that works in practice is cheaper than either side admits. Opus plans, Sonnet executes. Claude Code's plan mode is what makes this viable. It walks the codebase and asks clarifying questions before touching a single file.
GPT-5.5 wins the daily grind
Nate Herk, Theo, and Steve from Builder.io all flag the same thing: GPT-5.5/Codex uses roughly half the output tokens of Claude Opus on the same task, with comparable quality at about half the cost. Theo adds that Codex keeps improving while Opus 4.6 and 4.7 were regressions.
Is it flat out better at coding? On front-end work, no. Nate Herk's live tests still had Claude ahead on visual polish, and Builder.io deliberately switches to Sonnet for design-to-code because it beats both Opus and GPT-5 on that specific job. Token efficiency isn't luck either. OpenAI builds for it, and it's the structural reason Codex feels more generous on a $20 plan than Claude Code does at the same price.
The 80% tier: GLM 5.2 and DeepSeek V4
GLM 5.2 is the one that crashed the top tier. Vaibhav Sisinty reports it's the only Chinese model ranking second to Claude Opus 4.8 in blind tests. Pricing: $1.2 per million input tokens and $4.10 per million output, against $5 and $25 for Opus. About 5× cheaper for roughly 80% of the quality.
DeepSeek V4 is the budget workhorse. Alex Finn's numbers: it cuts Claude Code's cost to 1-10% of the original while reaching 80% of Opus quality on common tasks. Nate Herk measured ~60K tokens on a calculator project versus Sonnet 4.6's 70K+ and Opus 4.7's 100K+. At ¥2 RMB per million output tokens, it's the cheapest viable model on the market. The caveat is stability. DeepSeek V4 Pro hit conversation interruptions and repeated restarts inside coding agents, and Claude still wins on visual taste. For logic-heavy work, DeepSeek is occasionally the better option, price aside.
Free and local: OpenCode
OpenCode is the MIT-licensed agent where you bring your own model or run local via Ollama. Julian Goldie and NeuralNine both cover it seriously. Can it replace Claude Code? Unsupervised Learning says yes, and claims their only failures were Anthropic API overloads, not OpenCode itself. NeuralNine says no: it's the emotional favorite, not the practical one. The split tracks workload. Solo feature work, sure. Complex team refactors, keep Claude Code. OpenCode is also the only tool in this space that has to be profitable on its own. Everything else is VC or Microsoft subsidized.
Local quality is still uneven. NeuralNine's local testing was brutal, calling DeepSeek garbage locally, and Qwen buggy. Nate Herk counters that GLM-5.2's coding ability is comparable to Claude 4.7. Realistic expectations: 60-70% of Opus quality for true local-only, 80-90% if you accept open weights served over an API.
The comparison at a glance
| Dimension | Claude Opus 4.8 | GPT-5.5 / Codex | GLM 5.2 | DeepSeek V4 | OpenCode + local |
|---|---|---|---|---|---|
| Best for | Multi-file refactors | Daily reasoning, token efficiency | Cost-optimized quality | Extreme cost cutting | Free, private, self-hosted |
| Quality tier | Top (SWE-bench ~87.6% Verified) | Comparable, half the tokens | ~80% of Opus | ~80% of Opus | Depends on local model |
| Cost per 1M output | ~$25 | ~$10-15 | ~$4.10 | ~$0.30 (¥2 RMB) | $0 or BYO key |
| Cheapest way in | $20 Pro / $200 Max | $20 Plus (includes Codex) | API pay-per-use | API pay-per-use | Free (MIT, Ollama) |
| Lock-in | Anthropic only | OpenAI only | Open | Open | 75+ providers + local |
Pick by scenario
- Complex multi-file refactor → Claude Opus 4.8
- Daily reasoning and architecture work → GPT-5.5
- Most generous $20/mo plan → GPT-5.5 (Codex)
- 80% quality at 20% of the price → GLM 5.2
- Extreme cost cutting → DeepSeek V4
- Free, private, self-hosted → OpenCode + local model
- Model freedom per task → OpenCode
- IDE-native tab completion → Cursor (with Sonnet or Opus)
Two quick answers to questions I keep getting:
ChatGPT or Claude? Task dependent. Claude Opus won the visual front-end tests, and Codex used 2-5× fewer output tokens while winning research-heavy document work. Route by task type instead of crowning one.
What do working developers actually run? Two tools, not one. A primary for the hard 20% of work and a fallback for the daily 80%, switching the moment a usage cap bites.
Sources: Nate Herk's 100-hour Claude Code vs Codex test, Theo's three-way agent comparison, and Vaibhav Sisinty's GLM 5.2 breakdown.
Pick a rotation, not a winner.
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