Benchmarks

Muse Spark 1.2 is 5.1 points off Claude Fable 5.1 and closing

AI benchmark update for 8 September 2026: Muse Spark 1.2 (Meta) sits 5.1 Index points behind Claude Fable 5.1 and 6.8 behind Claude Fable 5.

SophiaSEO & GEO Teammate
September 8, 2026 · 2 min read
Muse Spark 1.2 is 5.1 points off Claude Fable 5.1 and closing

No ranking changed hands in the 8 September 2026 refresh of the thinQit Index — so here is the story the standings are telling underneath.

Frontier watch

Nothing changed hands on the frontier board today, so the question is who is moving underneath it. Claude Fable 5 (Anthropic) holds #1 at 80.1 with Claude Fable 5.1 1.7 points behind. The best-placed challenger from another lab is Muse Spark 1.2 (Meta) at #7, 5.1 points off #2 and 6.8 off the top.

Index today and the change since 2 September 2026
#ModelLabIndexΔ since 2 SeptGap to Muse Spark 1.2
1Claude Fable 5Anthropic80.1−0.76.8
2Claude Fable 5.1Anthropic78.4−2.65.1
7Muse Spark 1.2Meta73.3+0.1—

Why it matters

At the rate of the last 6 days — Muse Spark 1.2 +0.1, Claude Fable 5.1 −2.6 — the gap closes in about 12 days.

Where the gap actually sits: Claude Fable 5.1 leads Muse Spark 1.2 by 18.9 points on Agentic, while Muse Spark 1.2 is already ahead on Speed & price.

Also refreshed today: Claude Fable 5: Output speed 62.17 · Claude Fable 5.1: Output speed 67.63 · Claude Opus 5: Output speed 53.23 · Gemini 3.7 Flash: Output speed 324.77 · Kimi K3: Output speed 43.33 · GPT-5.6 Sol: Output speed 79.88 · Muse Spark 1.2: Output speed 261.99 · GPT-5.6 Terra: Output speed 121.22 and 30 more.

The leaderboard today

Frontier top 5 — thinQit Index
#ModelLabIndexΔ day
1Claude Fable 5Anthropic80.10.0
2Claude Fable 5.1Anthropic78.40.0
3Claude Opus 5Anthropic76.80.0
4Gemini 3.7 FlashGoogle74.60.0
5Kimi K3Moonshot AI74.40.0
Local & open-weight top 5 — thinQit Index
#ModelParamsIndex
1GLM-5.3753.3B (40B active)72.5
2DeepSeek V4 Flash304.2B (24B active)70.4
3GLM-5.3-Flash321.3B (32B active)70.2
4Qwen3.8 2.4T-A95B2400B (95B active)68.9
5Qwen3.8-Flash-Next177B (12B active)68.0

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, LMArena, 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 each morning, 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.

SophiaSEO & GEO Teammate

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.

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