#Benchmark

Cursor confirmed on August 14, 2026 that it has been acquired by SpaceX. The announcement runs thirteen sentences: it talks about GPUs, cheaper models, and the horizon, and says nothing about your code. We separate what's in a primary source from what's press-only (including the $60 billion), show what actually changes in the editor (Grok 4.6 in the house pool, Claude hidden by default, the Router choosing for you), and close with a checklist for anyone who depends on Cursor in production.

DeepSeek published DeepSeek-V4-Pro-0813 on its official pricing page, and the official V4 Pro release has finally dropped the preview label. The reported numbers, what's fact and what still has no public document, the comparison with Flash 0731 and Opus 4.8, and the notice on the pricing page itself that DeepSeek is going to raise prices soon.

The press says Muse Glimmer 30B requires a 5090. r/LocalLLaMA is posting screenshots of it running on a used 3090 from 2020. Both are right, and the explanation is in the VRAM budget: 17 GB of weights, 1.7 GB of KV cache, and an attention architecture designed to fit. Here's the math line by line, the tokens-per-second estimate on a 3090 with the work shown, and the verdict on when 24 GB is enough and when it isn't.

Gemini 3.5 Pro still isn't out, and every week there's a new date going around. I separated what Google has confirmed in writing from what's just a leak with no reproducible data, with the timeline of delays since I/O in May. And I built a 20-line watcher on the Gemini API models endpoint that pings you the minute gemini-3.5-pro shows up, plus a model fallback in Laravel so the switch becomes one line of .env.

DeepSeek republished the V4-Flash weights on July 31 without changing the model name in the API: anyone calling deepseek-v4-flash woke up running a different model, with no changelog. The real 0731 numbers (82.7 on Terminal Bench, but 79% in the independent measurement), where it beats GPT-5.6 Luna and where it loses, the 169 GB to run it locally and what to do if your agent points at a model name that became a moving target.

Alibaba dropped Qwen 3.8 Max, a 2.4-trillion-parameter MoE it claims is the second-best model in the world, behind only Fable 5. With no public benchmark at launch, the only independent test scored it 80/100 against Kimi K3's 83. Here: what it is, what it costs (Token Plan from $6 to $68), how to access it via API, and where each model in the Qwen 3.8 family fits.

Kimi K3 costs $3 input and $15 output per million tokens on the API (the chat at kimi.com is free), 1.7x cheaper than Claude Opus 4.8, not the "5x" your timeline is claiming. We did the honest pricing math, separated the verifiable benchmarks from the hype, and show where it beats Claude (and where it doesn't).

Claude Sonnet 5 is out. Instead of screenshotting the table, this post shows how to read the benchmarks in practice (cost per task, effort, with and without tools) for anyone using Claude Code or building AI into a product.

Claude Code or Codex? The answer comes with numbers: Codex opens a 13-point lead on Terminal-Bench (82.7% vs 69.4%) and Claude Code opens a 10-point lead on SWE-bench Pro (69.2% vs 58.6%), which is the benchmark for real multi-file problems. On SWE-bench Verified they tie. Here is the verdict by scenario, the real cost per dev, and the criterion that matters more than quality: how much control you want during the task.

Fable 5 or Opus 4.8, which one should you use? A straight verdict by task type, with the 10 concrete situations where Fable gets it done and Opus 4.8 did it badly or not at all: migration at scale, code from a screenshot, long-running agents and reasoning over documents. Plus what each one costs and the cases where Opus 4.8 is still the right call.