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Reviews and evidence

GPT-5.6 Sol · Community source · Personal experience

X one-shot visual build: Sol looked better, but the game was broken and cost more

In a Command Code comparison with the same /design prompt and one attempt, Sol cost $0.32 and produced a nice interface but an unplayable browser game; GLM 5.3 cost $0.016 and was playable, while Opus 5 cost $0.37 and had the strongest clone.

Unverified: the original source could not be rechecked. Historical figures below are not current verified results.

Community sourcePersonal experienceEdited 2026-09-20

Test conditions

Model version
GPT-5.6 Sol; compared with GLM 5.3 and Opus 5
Provider / client
Command Code; provider and account not disclosed
Reasoning tier
Not disclosed
Tools
The /design visual-build toolchain; permissions not disclosed
Task set
One shared /design prompt to build a playable browser game
Sample / repeats
One attempt per model; no independent scorer or blind review
Publication / collection date
2026-08-17 / 2026-08-17
Traceable results
Sol $0.32, nice interface but unplayable; GLM $0.016 playable; Opus $0.37 strongest

Key data and applicable tasks

Summary

A comparison of the playable browser games produced by GLM 5.3, GPT-5.6 Sol, and Opus 5 under the same /design prompt; Sol's interface was good, but the game was unplayable, and it cost more than the other two.

Original article

The following is the visible body text extracted during this visit. It includes page navigation, machine translation, advertising, comments, and other page elements; verify against the original link before citing it.

To view keyboard shortcuts, press the question mark View keyboard shortcuts Home Explore Notifications Chat Grok Premium History Creator Studio Articles Profile More Post @liaocaoxuezhe Post See new posts Conversation noclipepe @noclipepe Show translation GLM 5.3 just outplayed GPT-5.6 Sol at its own game — for 20× less.

Соmmand Соde tested all three with the same /design prompt.

GLM 5.3: $0.016 · playable GPT-5.6 Sol: $0.32 · nice UI, broken game Opus 5: $0.37 · best Subway Surfers-style clone

GLM wasn’t the best overall.

But it made GPT’s result look very expensive. 0:37 Quote noclipepe @noclipepe · Aug 14 Article GPT vs Qwen vs Grok vs Opus: The Epic One-Shot Showdown Four AI Models, Three One-Shot Visual Tests - You Pick the Winner Same prompt. One attempt. No rescue round. I gave four AI models three visual builds: a premium product page, a playable browser game,... 4:53 PM · Aug 17, 2026 · 27 View 3 2 Post your reply

Reply Relevant people noclipepe @noclipepe Follow Loading the AI future… Testing frontier models, agents & experimental AI projects What’s happening What’s new Entertainment · Trending Hayden Trending Heroes, Kairi Rap · Trending Blueface Trending Chrisean Entertainment · Trending Remember the Titans Trending #Lanterns, Hal Jordan Entertainment · Trending Ice Princess Trending Michelle Trachtenberg Show more Terms · Privacy · Cookie · Accessibility · U.S. TIDA · Ad info · More © 2026 X Corp.

What this supports

  • Supports the point that visual-build usability cannot be replaced by appearance.
  • Supports recording relative cost and deliverable status for one shared-prompt attempt.

What this does not support

  • Does not support a statistical visual or cost ranking; one task and undisclosed tokens/harnesses matter.
  • $0.32 is not a current Sol price or the cost of all /design tasks.

Method, limits, and reproduction

The figures, task set, reasoning tier, and client conditions apply only to the listed source and collection snapshot. Different versions, harnesses, or providers must not be compared directly; undisclosed parameters remain unknown.

For a reproduction, fix the model version, provider or client, reasoning tier, tools, task-set version, sample count, and collection date, and record failures, retries, and human corrections. Full steps are in the source notes below.

Original source

X · noclipepe · Original publication date 2026-08-17 · Site edit date 2026-09-20

Open original source

GPT-5.6 Sol

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Read the full analysis

Overview · English

GPT-5.6 Sol: Specs, Access, Changes, and the Risks That Still Matter

OpenAI's current GPT-5.6 Sol model page lists a 1.05M context window, 128K max output, reasoning controls, and a time-sensitive API price card. Here is what those facts mean for API, Codex, and browser users.

Related reviews

Nate Herk: Sol costs less on creative builds, while Fable wins more blind selectionsNate Herk used the same /goal to compare Sol in Codex with Fable in Claude Code: Sol won the roughly seven-minute/$1 visual-object build, while Fable was selected for the bike game and scrolling site; refusals confounded the small API sample.Artificial Analysis: Sol's intelligence, coding-agent result, and cost per taskArtificial Analysis records Sol max at 59 on its Intelligence Index, about $1.04 per task, and 80 on its Coding Agent Index, with roughly 15,000 output tokens per task; models are paired with complete harnesses such as Codex.Lynkr ITSMBench: routing lowers cost while Sol's binary pass rate remains limitedLynkr routed Sol through pi on 89 enterprise IT-service tasks: 31% full-suite Pass@1, 35%/40% matched Pass@1/Pass@2, about $0.87–$0.90 per task, and 92–95% cache hits; many failures missed only a few assertions.Visual Studio Magazine: Sol's token efficiency and reasoning-slider limitsThe article describes one Sol model behind quick and deeper Plus/Pro responses and cites 80 on Coding Agent Index, 64.6% on SWE-Bench Pro, and about 15,000 output tokens per Intelligence task; Sol did not lead every evaluation.Configure Codex for a million-token context and auto-compactionThe source shows config.toml and one-session CLI examples for the model ID, a 1,000,000-token context budget, and a 900,000-token compaction threshold; confirm client support and keep a rollback configuration before editing.Deliver code with prediction, planning, review, and verificationSplit long-running coding into prediction, planning, implementation, adversarial review, and independent verification, checking the plan, tests, and stop conditions item by item; this is a commenter’s personal workflow, not Codex’s default configuration.Design a verifiable multi-agent workflow with the Responses APISeparate judgment from deterministic processing, then combine programmatic tool calls, parallel subagents, and prompt-cache boundaries into a long-running workflow whose cost, latency, citations, and failures can be reviewed.Give Codex an Occam rule against over-engineeringAsk a coding agent to choose the simplest implementation that satisfies demonstrated requirements, reuse or remove existing code before adding layers, and keep clear module boundaries; the rule is community guidance, not a guarantee.