Claude 4.5 Sonnet

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

The leaderboard scores in the screenshot don’t match the hype cycle. On WebDev, Sonnet 4.5 sits around the second tier (score \~**1382**, grouped with “rank 4”), behind GPT-5 (high) (**1478**) and even Anthropic’s own Opus 4.1 variants (**1469**, **1461**). On the Text board it’s clustered in a big tie zone (\~**1440**) rather than leading.

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)**

I have been using Codex for a while (since Sonnet 4 was nerfed), it has so far has been a great experience. And now that Sonnet 4.5 is here. I really wanted to test which model among Sonnet 4.5 and GPT-5-codex offers more value.

So, I built an e-com app (I named it vibeshop as it is vibe coded) using both the models using CC and Codex CLI with respective LLMs, also added MCP to the mix for a complete agent coding setup.

I created a monorepo and used various packages to see how well the models could handle context. I built a clothing recommendation engine in TypeScript for a serverless environment to test performance under realistic constraints (I was really hoping that these models would make the architectural decisions on their own, and tell me that this can't be done in a serverless environment because of the computational load). The app takes user preferences, ranks outfits, and generates clean UI layouts for web and mobile.

Here's what I found out.

**Observations on Claude perf**

Claude Sonnet 4.5 started strong. It handled the design beautifully, with pixel-perfect layouts, proper hierarchy, and clear explanations of each step. I could never have done this lol. But as the project grew, it struggled with smaller details, like schema relations and handling HttpOnly tokens mapped to opaque IDs with TTL/cleanup to prevent spoofing or cross-user issues.

**Observations on GPT-5-codex**

GPT-5 Codex, on the other hand, had a better handling of the situation. It maintained context better, refactored safely, and produced working code almost immediately (though it still had some linter errors like unused variables). It understood file dependencies, handled cross-module logic cleanly, and seemed to “get” the project structure better. The only downside was the developer experience of Codex, the docs are still unclear and there is limited control, but the output quality made up for it.

Both models still produced long-running queries that would be problematic in a serverless setup. It would’ve been nice if they flagged that upfront, but you still see that architectural choices require a human designer to make final calls. By the end, Codex delivered the entire recommendation engine with fewer retries and far fewer context errors. Claude’s output looked cleaner on the surface, but Codex’s results actually held up in production.

Claude outdid GPT-5 in frontend implement and GPT-5 outshone Claude in debugging and implementing backend.

**Cost comparison:**

Claude Sonnet 4.5 + Claude Code: \~18M input + 117k output tokens, cost around $10.26. Produced more lint errors but UI looked clean.
GPT-5 Codex + Codex Agent: \~600k input + 103k output tokens, cost around $2.50. Fewer errors, clean UI, and better schema handling.

I wrote a full breakdown [Claude 4.5 Sonnet vs GPT-5 Codex](https://composio.dev/blog/claude-sonnet-4-5-vs-gpt-5-codex-best-model-for-agentic-coding),

Would love to know what combination of coding agent and models you use and how you found Sonnet 4.5 in comparison to GPT-5.