No ranking changed hands in the 16 October 2026 refresh of the thinQit Index — so here is the story the standings are telling underneath.
Index per dollar
The Index weights speed & price at only 5 %, which is deliberate — it is a capability score, not a buying guide. Read that column on its own and Muse Spark 1.3 (Meta) wins the frontier top 10 at 62.0, while still carrying an Index of 76.4 at #6 — 4.0 points off Claude Opus 5.5.
| Model | # | Index | Speed & price | $ / M tokens | Output tok/s |
|---|---|---|---|---|---|
| Muse Spark 1.3 | 6 | 76.4 | 62.0 | $2 | 257 |
| Hy4 preview | 2 | 78.9 | 48.0 | $1.13 | — |
| Claude Opus 5.5 | 1 | 80.4 | 23.9 | $8 | 97 |
| Kimi K3 | 9 | 73.4 | 17.6 | $6 | 45 |
| Claude Fable 5.1 | 3 | 78.3 | 10.5 | $20 | 63 |
| GPT-6 Astra | 8 | 74.7 | 8.1 | $20 | 49 |
| GPT-5.6 Sol | 10 | 73.2 | 7.7 | $8 | 0 |
| Claude Opus 5 | 7 | 75.8 | 5.8 | $10 | 0 |
| Claude Fable 5 | 4 | 77.9 | 0.0 | $20 | 0 |
Why it matters
Muse Spark 1.3 runs at $2 per million blended tokens against $8 for Claude Opus 5.5 — 4.0× cheaper for 4.0 Index points less. At the other end, Claude Fable 5 scores 0.0 on the same column: you are paying for the last few points of capability. For high-volume work — classification, extraction, summarisation — the cheaper model usually finishes the job at a fraction of the cost.
Also refreshed today: Claude Opus 5.5: Output speed 97.17 · Claude Fable 5.1: Output speed 62.67 · Muse Spark 1.3: Output speed 256.82 · GPT-6 Astra: Output speed 48.67 · Kimi K3: Output speed 45.02 · Qwen3.8 Max: Output speed 38.66 · GLM-5.3: Output speed 70.77 · DeepSeek V4 Pro: Output speed 93.08 and 29 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) | 72.2 |
| 3 | Seed-2.0-Mini | — | 71.7 |
| 4 | DeepSeek V4 Flash | 304.2B (24B active) | 70.3 |
| 5 | GLM-5.3-Flash | 321.3B (32B active) | 70.2 |
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, LiveBench, 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.
