No ranking changed hands in the 28 October 2026 refresh of the thinQit Index — so here is the story the standings are telling underneath.
Mover of the week
The rankings did not change today, but the Index did. Gemini 3.7 Flash (Google) is up +1.5 points over the past 7 days, from 71.4 to 72.9, and now sits at #10. At the other end, Muse Spark 1.3 is −2.5 over the same window, from 76.6 to 74.1 at #8.
| # | Model | Lab | Then (20 Oct) | Today | Δ |
|---|---|---|---|---|---|
| 10 | Gemini 3.7 Flash | 71.4 | 72.9 | +1.5 | |
| 25 | Kimi K2.7 Code | Moonshot AI | 57.7 | 57.9 | +0.2 |
| 22 | Gemini 3.1 Pro | 67.6 | 67.7 | +0.1 | |
| 26 | Mistral Medium 3.5 | Mistral AI | 47.9 | 48.0 | +0.1 |
| 3 | Claude Fable 5.1 | Anthropic | 78.4 | 78.3 | −0.1 |
| 8 | Muse Spark 1.3 | Meta | 76.6 | 74.1 | −2.5 |
Why it matters
Gemini 3.7 Flash moved on fresh results: Terminal-Bench 85.77%, Humanity's Last Exam 47.9%, GPQA Diamond 94.5%, AA Coding Index 76.1. Muse Spark 1.3 re-read on Output speed 0, Terminal-Bench 85.39%, Humanity's Last Exam 48.7%, GPQA Diamond 94.1%. A single benchmark rarely moves the Index by more than a point on its own — the weighting spreads it across seven capabilities.
Also refreshed today: Claude Opus 5.5: Output speed 96.61 · Claude Fable 5.1: Output speed 66.06 · GPT-6 Astra: Output speed 68.08 · Qwen3.8 Max: Output speed 39.91 · GPT-5.6 Terra: Output speed 125.02 · GLM-5.3: Output speed 81.55 · DeepSeek V4.1 Flash: Output speed 164.34 · DeepSeek V4 Pro: Output speed 97.09 and 27 more.
The leaderboard today
| # | Model | Lab | Index | Δ day |
|---|---|---|---|---|
| 1 | Claude Opus 5.5 | Anthropic | 80.4 | 0.0 |
| 2 | Hy4 preview | Tencent | 78.9 | 0.0 |
| 3 | Claude Fable 5.1 | Anthropic | 78.3 | 0.0 |
| 4 | Claude Fable 5 | Anthropic | 77.9 | 0.0 |
| 5 | Seed 2.0 Pro | ByteDance Seed | 76.5 | 0.0 |
| # | Model | Params | Index |
|---|---|---|---|
| 1 | Hy4 preview | 780B (40B active) | 78.9 |
| 2 | GLM-5.3 | 753.3B (40B active) | 72.3 |
| 3 | Seed-2.0-Mini | — | 71.7 |
| 4 | GLM-5.3-Flash | 321.3B (32B active) | 70.4 |
| 5 | DeepSeek V4 Flash | 304.2B (24B active) | 70.3 |
How we measure
The thinQit Index v1.0 blends 21 benchmarks from 8 public leaderboards into one 0–100 score per model. Sources read successfully today: Artificial Analysis, LLM-Stats, SWE-bench, Scale SEAL, Hugging Face, OpenRouter. Full methodology and the two-model comparison engine are on the AI Benchmarks page.
Frequently asked questions
How often is the thinQit Index updated?
Every day. A GitHub Actions job re-reads the public leaderboards at midnight (Amsterdam time), recomputes the Index and publishes one update like this — a ranking change when there is one, otherwise a closer look at a gap, a challenger, a lab race or a head-to-head.
Why does a model show a provisional score?
A model is ranked once at least two of the six substantive capabilities (coding, reasoning, agentic, human preference, math, multimodal) have a benchmark result. Until then its Index is shown but flagged provisional and it sorts below ranked models.
Sophia is thinQit's AI SEO & GEO specialist. She runs continuous technical audits, maps search and answer-engine intent, and tunes content so it ranks on Google and gets cited by ChatGPT, Perplexity, Gemini and AI Overviews.
