European AI has touched the threshold of the global frontier for the first time. Multiverse Computing has released Quasar 438B, the first large model in the company's history — a 438-billion-parameter reasoning model aimed at enterprise-scale agents and coding, supporting English and Spanish. It scores 43 on the Artificial Analysis Intelligence Index v4.1.1, which the company calls the highest result of any European model; across all 178 models tracked by AA, that score ranks 13th.

Where 43 Points Stands Globally

The v4.1.1 index combines nine evaluations: GDPval-AA v2, τ³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience and AA-LCR.

Among European peers, 43 is 13 points ahead of Mistral Medium 3.5 at 30; 5 ahead of NVIDIA Nemotron 3 Ultra at 38 — which carries 112 billion more parameters; and 1 ahead of Inkling at 42. The frontier is led by Claude Opus 5 at 63, so the gap remains clear, but the "Europe's best" claim now has data behind it. AA's own figures back the score's weight: the median among comparable reasoning models is just 18.

Speed Is the Second Selling Point

Quasar outputs 500 tokens (thinking time included) in 15.3 seconds. Only three models in the official comparison are faster: Nemotron 3.5 Lightning (9.4s, index 24), Gemini 3.5 Flash-Lite (10.8s, index 37), and Gemini 3.7 Flash (11.5s, index 56) — only the last of those is both faster and more capable. Read the other way, Mistral Medium 3.5 needs 18.8 seconds for the same response and Inkling needs 48.3, more than double Quasar's time.

Long context is where it comes closest to the frontier group: 75.0 on AA-LCR, level with Grok 4.6 (high), 0.7 behind Claude Opus 5 at 75.7 and just behind Qwen3.8 2.4T A95B at 75.3. The weak spot is equally clear — 69.3 on Terminal-Bench v2.1 trails Opus 5's 89.1 by nearly 20 points, and the company acknowledges this is the evaluation with the most headroom, where the next round of work is aimed.

The Independent Data Tells Another Story

Artificial Analysis' independent model page provides numbers the announcement didn't emphasize. Output speed is 176.2 tokens/s (median: 105.2), time to first token is 1.06s (median: 2.05s), context window is 1M tokens, and the model went live on August 10, 2026 — the performance genuinely holds up.

But both price and verbosity sit on the expensive side: $0.60 per 1M input tokens and $1.80 per 1M output tokens — 2.4x and 2x the class medians ($0.25/$0.90). More striking is token use: running the full Intelligence Index, Quasar generated 350 million output tokens, more than five times the median of 67M; AA spent $1,047.71 evaluating it. The "long thinking" of reasoning models is especially pronounced here. It is also a proprietary model — weights are not available, API access only.

What to Make of It

Multiverse Computing built its position on making AI more efficient and deployable, and Quasar pushes that methodology into the 400B+ parameter class for the first time: multi-step tasks that need planning, tool use, code execution and large context, with latency kept low enough to sit inside an interactive product rather than a batch job. The model is served through the company's CompactifAI API, so teams can test it without standing up infrastructure.

For Europe, this at least proves one thing: after Mistral, European models now have their first option inside the global top 15. But Quasar's ledger is also a reminder to buyers — scores are just the entry ticket, and in a per-token billing world, verbosity directly decides the monthly invoice. 43 points is good; the 350-million-token thinking bill is the second calculation to run before procurement.

Source: https://multiversecomputing.com/resources/introducing-quasar-438b-europe-s-leading-ai-model ; independent data: https://artificialanalysis.ai/models/quasar-438b