Claude 4.6 Opus vs Claude 4.5 Sonnet

[https://www.anthropic.com/news/claude-sonnet-4-5](https://www.anthropic.com/news/claude-sonnet-4-5)

Zhipu AI (Z.ai) officially released **GLM-4.7** today, December 22, 2025. The new flagship shows major gains in coding and complex reasoning, specifically targeting Western SOTA models.

**LMArena Code Arena (Blind Test):** #1 among open-source models, outperforming **GPT-5.2**.

**LiveCodeBench V6:** Scored **84.8**, surpassing **Claude 4.5 Sonnet**.

**AIME 2025 (Math):** Outperformed both **Claude 4.5 Sonnet** and **GPT-5.1**.

**Human Last Exam (HLE):** Scored **42%** (38% improvement over GLM-4.6), approaching GPT-5.1 performance.

**τ²-Bench:** Reached parity with Claude 4.5 Sonnet in real-world interaction.

**Technical Specs & Features:**

**Context Window & Speed:** 200K tokens (128K max output) and 55+ tokens per second.

**Thinking Mode:** Includes a dedicated "Deep Thinking" mode for multi-step reasoning.

**Agentic Coding:** Optimized for end-to-end task execution in tools like Claude Code, Cline and Roo Code.

**Pricing:** Launching a $3/month plan for direct integration into coding agents.

**Source: Z.ai Official (GLM 4.7 Docs)**

Anyone else here doing full-stack Next.js in Cursor and watching the Claude quota evaporate before lunch? I used to be in the same boat — massive context windows from all the components, pages, and DB logic would smoke the default limits fast.
Not anymore. I’ve been on this setup for weeks and basically never hit a wall while still getting top-tier answers. Here’s exactly what I do:

**1. .cursorrules is non-negotiable**
I keep one in the root of every project. The key line I added: “Never explain the code to me. Just output the code blocks.”
That single rule saves me thousands of output tokens a day. No more walls of “here’s what I changed and why” — just the goods.

**2. Stopped using Cursor’s built-in Claude quota**
I killed the default Cursor Pro subscription for the heavy stuff. Instead I use my own API keys and point Cursor’s “OpenAI Compatible” base URL at LLM Router Gateway.
Inside [llmrouter](https://llmrouter.app/) routing settings I set up simple tags routing like this:

* **UI & CSS tweaks**: gemini-3.1-flash → gpt-5.4-mini
* **Deep backend / complex logic**: claude-opus-4.6 → deepseek-v3.2
* **General / quick questions**: llama-4-scout

I sorted the fallback chains by speed vs intelligence. The router auto-detects the query type, so 90% of my UI polish and small fixes go to Gemini (basically free + huge context). I only actually hit Claude Opus 4.6 when I’m doing nasty database refactors or tricky architecture stuff. My Anthropic bill dropped \~70% overnight.

**3. Cmd+K for everything small**
Don’t open the full chat sidebar just to rename a variable or extract a component. Highlight the code, hit Cmd+K, let a fast model handle the inline edit. Saves a ton of tokens and feels way snappier.
That’s it. Super simple but it completely changed how much I can actually use Cursor in a day.

How are you all managing the limits? Using a Cursor Team? Or did you build your own router hacks too? Drop your setups — always looking to steal better ideas.

Time and time again I find posts about these fine tunes that promise increased intelligence and reasoning with base models, and I continuously try them, realize they're botched, and delete them shortly after. I sometimes do resort to a lower quant since they are bigger, in this case, a 40b variant of Qwen 3.5 27b, but they seem to always let me down. I've resorted to not downloading any model with "Claude Opus 4.6" in the name.

Kudos to everyone who tries to make the foundation models more intelligent, but imo, it never works.

Note that this example is anecdotal evidence on a single prompt, but it's overall always the case of decreased intelligence when using with a local agent setup + llama.cpp in WSL2. This is irrespective of the quant as well - I've tried many.

One thing to notice however, the reasoning/thinking is significantly less, perhaps that's part of the problem.

Have any you found these better than base, ever?

The attached screenshots are:

./llama-server -hf mradermacher/Qwen3.5-27B-heretic-GGUF:Q4_K_S --temp 1.0 --top-p 0.8 --top-k 20 --min-p 0.00 --fit on --alias default --jinja --flash-attn on --ctx-size 262144 --ctx-checkpoints 256 --cache-ram -1 --cache-type-k q4_0 --cache-type-v q4_0 --threads 8 --threads-batch 16 --no-mmap

./llama-server -hf mradermacher/Qwen3.5-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-i1-GGUF:i1-Q3_K_S --temp 1.0 --top-p 0.8 --top-k 20 --min-p 0.00 --fit on --alias default --jinja --flash-attn on --ctx-size 131072 --ctx-checkpoints 256 --cache-ram -1 --cache-type-k q4_0 --cache-type-v q4_0 --threads 8 --threads-batch 16 --no-mmap

https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2842987

Prompt injection is essentially a way for malicious people to hijack the LLM's usual behavior. That may include fabricated evidence put into the model or the external context (eg a completely white-out text not seen by humans). The authors were able to get all the latest LLMs to recommend thalidomide in a hypothetical encounter with a pregnant woman, 80 to 100 percent of the time. That's a major reason I won't let an agentic AI touch private information or use an AI browser.