Quasar 438B is a 438-billion-parameter model from Spanish AI company Multiverse Computing, and per the Artificial Analysis benchmarks Julian Goldie cites in his video "Quasar 438B - The Top European AI Model!" (published 4 September 2026), it just became the highest-scoring European AI model they have evaluated — built specifically for AI agents rather than chat. That last part is the detail most coverage misses. This is not another chatbot chasing conversational polish. It is a model designed for multi-step workflows, long-document reasoning, complex automation, and tool calling.
📺 Watch: Quasar 438B - The Top European AI Model!
🔥 Get the Agent OS as a free bonus: AI Profit Boardroom members get the full Agent OS zip, prompt libraries, daily tutorials and weekly live coaching calls. → Get inside
Honest framing up front: Quasar is not suddenly the smartest model in the world. Claude Opus 5 is still comfortably ahead on raw intelligence. What Quasar represents is a genuinely strong European debut from a first major release — and on the two dimensions that decide whether an AI agent is usable in production, speed and long context, it punches well above its headline score.
One housekeeping note: you will occasionally see the name written "Quazar". Same model — this article uses the spelling from the video title.
What Is Quasar 438B?
Multiverse Computing is a Spanish AI company, and Quasar 438B is its flagship: 438 billion parameters, built from the ground up for AI agents. The design brief was never "make a better chatbot". It was: handle multi-step workflows, reason over very long documents, run complex automation, and call tools reliably.
That changes how you should judge it. A chat model gets graded on how good one answer feels. An agent model gets graded on whether it can hold a plan across dozens of steps, keep an enormous amount of context in its head, and use tools without wandering off. Read the benchmark numbers below through that lens and the release makes sense: mid-table overall intelligence, near-frontier long-context reasoning, and speed that keeps multi-call agent workflows moving.
The Quasar 438B Benchmark Numbers
Every figure in this section comes from the Artificial Analysis Intelligence Index as cited in Julian's video — independent benchmarks, not our own testing. The index combines nine evaluation areas, including agentic tasks, coding, scientific reasoning, long-context work, and problem solving, into one score.
Quasar 438B scored 43, which makes it the highest-scoring European model in that comparison. For context, Mistral Medium 3.5 scored 30 and Nvidia's Nemotron 3 Ultra scored 38, while Claude Opus 5 leads the index at 63.
| Model | Artificial Analysis Intelligence Index | Position |
|---|---|---|
| Claude Opus 5 | 63 | Index leader |
| Quasar 438B | 43 | Highest-scoring European model in the comparison |
| Nvidia Nemotron 3 Ultra | 38 | Comparison model cited in the video |
| Mistral Medium 3.5 | 30 | Comparison model cited in the video |
A 43 against a 63 is a twenty-point gap, and pretending otherwise would be hype. But a first major release from Multiverse Computing landing ahead of every other European entry is a real result. For anyone building agents in Europe, the question "is there a serious domestic option" just got a new answer.
One caveat on sources. When Julian eventually runs Quasar through Goldie Bench, his own model benchmark, those verdicts will be his own hands-on testing — he has not benchmarked it himself yet. Until then, treat these as reported numbers.
If you want to watch new models like this get pressure-tested on real business workflows within days of release, that is what happens inside AI Profit Boardroom — Julian's community of 3,000+ members building agents and automations, currently $69/mo locked in (normally $110).
Why Speed Matters More for Agents Than for Chat
Per the same Artificial Analysis data cited in the video, Quasar generates a 500-token response — including reasoning time — in 15.3 seconds. Only three models in the comparison were faster, and only one of those was also smarter. That is an interesting spot on the speed-versus-intelligence curve: near the front on pace, with almost nothing faster that also beats it on the benchmark.
Here is why that matters. An agent does not make one model call per task. A single task looks more like this:
- Read the request
- Plan the steps
- Call a tool
- Read the result
- Decide what to do next
- Call another tool
- Check the output
- Adjust
- Finish
That is easily 10 to 20 model calls for one piece of work. A model that dawdles on every call turns a five-minute workflow into a half-hour one, and chained setups like the Gauntlet Loop multi-agent pattern multiply the delay at every hop. Slow calls do not just annoy you — they grind whole workflows to a halt. Multiverse Computing clearly optimised Quasar for this, and it matters most in the context of real agent pipelines, not benchmark screenshots.
The 1 Million Token Context Window
Quasar 438B ships with a 1-million-token context window — roughly 1,500 pages of text in a single prompt. Plenty of models advertise big windows and then fall apart when you fill them, which is why the long-context score is the most interesting number in the release: on the Artificial Analysis long-context reasoning benchmark, Quasar scored 75.0, matching Grok 4.6 (high) and coming within about one point of Claude Opus 5.
Sit with that for a second. On overall intelligence, Opus 5 is twenty points ahead. On reasoning over long documents, Multiverse Computing's model is essentially level with the leader. For agents, that is not a nice-to-have — "read all of this, then act" is the core move in most serious automation.
Julian's angle in the video is the one worth stealing: for a community or a business, long context plus tool calling means one agent can actually read all of your member data — every form, every transcript, every message — in a way no human team ever could.
Two Agent Workflows You Could Build on It
These are the prompt patterns Julian sketches in the video — what you would build, not results anyone has measured. Treat them as blueprints.
1. The member-success agent
Feed everything into the 1M-token window: onboarding forms, coaching-call transcripts, community messages, tutorial completion data, support tickets. Then prompt Quasar to find at-risk members, name what each one is blocked on, and write personalised check-ins that reference each member's exact situation — the question they asked three weeks ago, the tutorial they stalled on. Tool calling then triggers the sends and the tags. No human team reads every message from every member. An agent with a 1,500-page window can.
2. Coaching-call intelligence
Upload a full call transcript and prompt the model to extract every question asked, every tool mentioned, every problem raised. From that single pass you would generate an FAQ document, five tutorial ideas drawn from the most common questions, and follow-up emails segmented by business type. Tool calling posts the docs and sends the emails.
Both patterns lean on exactly what Multiverse Computing optimised for: a giant context window plus reliable tool calling. Neither needs Quasar to top any benchmark — mid-table intelligence with near-frontier long-context reasoning is plenty for agents doing this class of work. If you want the plumbing side — how agents, tools, and schedules actually connect — the Agent OS guide covers the architecture. And if you would rather map a workflow like this onto your own business with a human first, book a free AI strategy session and walk through it live.
Who Should Care — and Who Should Not
Quasar 438B deserves your attention if you are building agents that chew through long material: member data, contracts, call transcripts, research piles. It is also an obvious candidate if you are a European company that wants a European provider — Multiverse Computing now offers a domestic option that, per the Artificial Analysis benchmark profile, no longer means a painful capability sacrifice for agent work with heavy context.
Skip it if you simply want the smartest chat model available. The cited index has Claude Opus 5 twenty points clear, and Quasar was never built to win that fight. Model choice is also moving weekly — see how to use GPT-6 Astra for the other big release this cycle — and the honest answer for most businesses is a small stack of models, each doing what it is best at.
Quasar 438B FAQ
Is Quasar 438B better than Claude?
Not on raw intelligence. Per the Artificial Analysis Intelligence Index cited in Julian's video, Claude Opus 5 leads at 63 versus Quasar's 43. Where it gets genuinely close is long-context reasoning — 75.0, within about a point of Opus 5 — and speed, where only one faster model in the comparison was also smarter. For agent workloads built around huge documents, that trade can be worth it.
Who makes Quasar 438B?
Multiverse Computing, a Spanish AI company. Quasar 438B — 438 billion parameters — is its first major model release, and per the benchmarks cited in the video it debuted as the highest-scoring European model in that comparison, ahead of Mistral Medium 3.5 and Nvidia Nemotron 3 Ultra.
How big is the context window?
One million tokens, roughly 1,500 pages. More importantly, it holds up under load: Artificial Analysis scored it 75.0 on long-context reasoning, matching Grok 4.6 (high). For agents, the usable window matters far more than the advertised one.
Is Quasar 438B good as a chatbot?
It was not built to be one. Multiverse Computing designed it for multi-step agent work — planning, tool calling, reasoning across a huge context — not casual conversation. If chat is your main use case, pick on raw benchmark intelligence instead.
The Bottom Line
Multiverse Computing just gave Europe a model built for the agent era, and the Artificial Analysis benchmarks cited in Julian's video back it up: the top European score on the intelligence index at 43, near-frontier long-context reasoning at 75.0, and speed that keeps twenty-call agent workflows moving. Claude Opus 5 is still the smarter model, full stop. But "smartest" and "right for this job" are different questions, and Quasar 438B just made the second one much more interesting.
Two ways to act on it. Join AI Profit Boardroom and build alongside 3,000+ members already shipping agent workflows like the two above — $69/mo locked in (normally $110). Or book a free AI strategy session and get a straight answer on where an agent with a million tokens of context would pay for itself in your business first.
