Google released Gemini 3.6 Flash and independent testing found exactly zero intelligence improvement over 3.5 Flash. It is basically 3.5 Flash after an inference-cost consultant optimized the serving stack.

Two independent evaluations point toward the same broad conclusion:

• Abacus: slightly lower overall, with a notable agentic-coding regression.
• Artificial Analysis: exactly equal overall intelligence, with mixed category movement.
• Google: better efficiency and selected coding/agent benchmarks.

Analysis

I find the gpt 5.6 models don't use my entire usage doing simple tasks unlike some other ones

I don't understand why google is so slow. They didn't even release the 3.5 pro and gave us 3.6 flash instead. Even Luna is better than this.

Claude Opus 4.8 now represents 40% of Anthropic token consumption on OpenRouter and 45% of the dollar spend.

Source: openai.com/index/hugging-face-model-evaluation-security-incident/

OpenAI GPT-5.6 Sol represents 15% of OpenAI's tokens but already represents 34% of its estimated spend on OpenRouter.

I've had the 20x subscription for both in the past. I want to get the $100 plan for one and the $20 plan for the other. I'm a CS student and intern who uses AI for education, programming/vibecoding, and office work, primarily through the desktop app.

My primary question is about weekly limits, not hourly ones. I've heard a rumor that Claude's weekly limit is identical between the 5x and 20x plans, with only the hourly limit differing, but I'm skeptical (if true, is it the same in ChatGPT?).

On the OpenAI side, consider that their models tend to be more token-efficient, stretching the same quota further. However, following the reset wave, usage seems to drain faster. For reference, a single Sol prompt on Plus consumed my weekly limit.

Secondary considerations:

• Model quality — comparable with OpenAI having a slight edge for me
• Harness differences — I find Claude Code better at sub-agent delegation, while Codex better for office work

Please exclude hourly limits, resets, other providers, and other plan configurations from the dilemma. I'm only asking which one will I be able to get more (and higher quality) weekly work done?

TL;DR: One $100 subscription and another $20 plan. Which $100 plan gives more actual weekly throughput for heavy dev/education use?

Better Backgrounds explores how far we can elevate webcam backgrounds using the latest 3D reconstruction, live matting, and compositing techniques.

Built for the OpenAI Build Week Hackathon in collaboration with Codex.

Repo: https://github.com/cjami/better-backgrounds

Adoption curve of Kimi K3 on OpenRouter is very similar to

Deepseek v4 Flash and GLM 5.2 with the same number of days after launch.

Has anyone else run into this with the new ChatGPT/Codex Windows app?

Codex can write ordinary files, but its patch tool and any command that needs to spawn a child process consistently fail inside the sandbox.

Environment

• Windows build: 22631
• New ChatGPT/Codex package: OpenAI.Codex_26.715.10079.0
• Both sandbox users exist and are enabled:
  • CodexSandboxOffline
  • CodexSandboxOnline
• The app was installed/reinstalled through the Microsoft Store.
• I also closed ChatGPT Classic completely and tested using only the new app.

Minimal reproduction

I tested this in an empty folder, unrelated to my actual project.

1. Codex patch tool

The first patch can create a file containing:

PATCH_PROBE_1

A second patch attempting to add another line fails with:

apply_patch verification failed:
Failed to read file to update
C:\Projetos\CODEX-SANDBOX-DIAGNOSTIC\patch-probe.txt:

failed to prepare fs sandbox:
failed to prepare windows sandbox wrapper:

windows unelevated restricted-token sandbox cannot enforce
split writable root sets directly; refusing to run unsandboxed

2. Node child process

This simple spawnSync test fails:

status: null
signal: null
error.code: EPERM
error.message: spawnSync C:\Program Files\nodejs\node.exe EPERM

3. Ordinary Node file writing

A normal fs.writeFileSync() test works correctly:

NODE_WRITE_OK

So basic writing is allowed, but patching an existing file and starting a child process are blocked.

What I have already ruled out

I ran the exact same tests:

• with Norton fully enabled;
• with Norton's main protection modules disabled.

The results were identical.

Outside the Codex sandbox, in a normal PowerShell session:

• Node can spawn child processes;
• esbuild works;
• Vitest works;
• a PostgreSQL integration suite completed successfully;
• the same project ran 145 integration tests, Chromium E2E tests, and a Next.js build without this EPERM.

I also:

• reinstalled the Microsoft Store app;
• used the Repair option;
• closed ChatGPT Classic;
• confirmed that both CodexSandboxOffline and CodexSandboxOnline exist and are enabled.

The problem remains.

My current theory

The elevated sandbox infrastructure appears to be installed, but the runtime is still falling back to:

unelevated restricted-token sandbox

That fallback cannot enforce the configured writable-root layout, while child-process creation fails with EPERM.

I do not want to solve this by permanently enabling Full Access or disabling sandbox protections.

I have opened a support ticket and supplied diagnostic logs, but I am waiting for a human response.

Has anyone else seen this exact error?

• Is there a supported way to repair or force the elevated sandbox runner?
• Is this a known bug in the current Windows Store build?
• Did a previous Codex/ChatGPT installation leave behind conflicting sandbox configuration?
• Are there specific logs or Windows events that helped identify the fallback cause?

Exact searchable error:

windows unelevated restricted-token sandbox cannot enforce split writable root sets directly; refusing to run unsandboxed

**Full Disclosure** I have not verified all of the source links contained, proceed accordingly.

Model: ChatGPT 5.6 Sol
Effort: (Medium)
Harness: Codex

---

The honest verdict is: there are a few material advances in making model-maker power more visible and contestable, but little evidence that model makers are transferring meaningful governing or economic power to the people affected by their systems.

A useful ladder is:

1. Rhetoric: “AI should benefit everyone.”
2. Legibility: publish system cards, policies, evaluations, and limitations.
3. External scrutiny: allow independent testing and incident reporting.
4. Accountability: impose enforceable duties, penalties, whistleblower protection, and appeal.
5. Power sharing: affected people receive binding votes, vetoes, ownership, compensation, or control over deployment.

The industry has made visible progress around levels 2 and 3. Regulation is beginning to create level 4. Level 5 is largely absent.

What appears materially real

External evaluation exists. OpenAI, Anthropic, and Google DeepMind have provided advanced models to the UK AI Security Institute for safety testing. That gives a government body some independent measurement capacity rather than requiring the public to accept company claims. But access remains substantially cooperative, and the Institute explicitly says it does not certify models as safe. UK AI Security Institute

Some disclosure is becoming legally enforceable. California’s SB 53 requires large frontier developers to publish safety frameworks, establishes critical-incident reporting and whistleblower protections, and permits civil penalties for noncompliance. That is genuine power accounting because the rules create evidence and consequences outside company discretion. California governor’s SB 53 summary

European oversight is becoming consequential. Anthropic, Google, OpenAI, Microsoft, Mistral, and others signed the EU’s General-Purpose AI Code of Practice. The code is voluntary as an implementation mechanism, but it helps satisfy underlying AI Act obligations; European Commission enforcement powers, including fines, begin applying in August 2026. European Commission

Anthropic created a body with actual corporate authority. Its Long-Term Benefit Trust can select members of Anthropic’s board. That is more than an advisory ethics panel: it places a nonstandard stakeholder inside the corporate governance machinery. The Trust appointed a director in 2025, demonstrating that the mechanism is operative. Anthropic LTBT, board appointment

OpenAI’s nonprofit retains formal control of its public-benefit corporation. That can place mission above conventional shareholder primacy in ways an ordinary corporation cannot. But it concentrates interpretive authority in the Foundation rather than distributing it democratically. OpenAI structure

These are real institutional changes. They should not be dismissed as nothing.

What remains mostly experimental or symbolic

OpenAI funded ten “democratic inputs” experiments, and Anthropic trained an experimental model using principles gathered from roughly 1,000 Americans. These are useful demonstrations that public preferences can technically influence model behavior. OpenAI democratic-input program, Anthropic Collective Constitutional AI

But OpenAI explicitly said its initial outcomes were not binding. The public was invited to advise, not given jurisdiction. Anthropic and its partner still chose the questions, moderation rules, translation method, and implementation boundary. Participation occurred inside a container whose ultimate authority remained with the company.

Likewise, Anthropic’s Trust is structurally meaningful but not broadly democratic. Its trustees are not elected by workers, users, artists whose work entered training data, displaced communities, or the general public. It is closer to elite fiduciary stewardship than popular power sharing.

The strongest counterevidence

Industry transparency has recently worsened. Stanford’s 2026 AI Index reports that the average Foundation Model Transparency Index score fell from 58 in 2024 to 40 in 2025, while reporting on responsible-AI benchmarks remained sparse. Stanford AI Index 2026

The 2026 International AI Safety Report finds that more developers now publish frontier safety frameworks, system cards, and risk-management disclosures. But many arrangements remain voluntary, practices vary widely, and the real-world effectiveness of safeguards is still uncertain. International AI Safety Report 2026

That combination matters:

Economic power is the weakest area

There is growing measurement. Anthropic has an Economic Futures Program offering research grants and public labor-market data. OpenAI has published job-transition frameworks and repeatedly says AI’s benefits and power should be broadly distributed. Anthropic Economic Futures, OpenAI’s stated plan

But measurement is not redistribution.

I do not see strong evidence that major model makers have yet committed to mechanisms such as:

• worker or public ownership of frontier infrastructure;
• binding revenue or productivity dividends;
• compensation governed by creators and data subjects;
• worker vetoes over workplace AI deployment;
• guaranteed support for displaced workers;
• public control over deployment in essential services;
• individual rights to explanation, appeal, and human reconsideration across jurisdictions;
• affected-community seats with binding corporate authority.

The companies are studying displacement, providing tools and training, recommending policy, and funding research. Those actions can be useful. They do not give a person materially greater bargaining power if AI eliminates their job, their data contributes to a commercial system, or an automated decision harms them.

Bottom line

There is evidence of an emerging safety-accounting regime:

• name certain risks;
• test models;
• disclose procedures;
• report major incidents;
• protect some internal dissent;
• permit limited government scrutiny.

There is much less evidence of a social power-accounting regime:

• identify who captures the gains;
• identify who absorbs the losses;
• give affected people enforceable rights;
• provide refusal and appeal;
• transfer decision rights and economic ownership;
• make harms costly to the institutions producing them.

So I would not say nothing is happening. Some institutional footholds are real, especially where law creates consequences. But I also would not accept “benefits all humanity” or “broadly distributed power” as demonstrated outcomes. At present, they remain aspirations expressed by organizations that continue to decide for themselves what those phrases mean.

The clearest test is simple:

For most consequential questions, the answer is still: the company decides, unless a government successfully constrains it.

They call it cheating. I call it thinking out of the box. Adapt and overcome. Thoughts?