A 438-billion-parameter reasoning model isn’t an obvious choice when speed is a priority. Multiverse Computing is betting that compression can make it one. 

On Wednesday, the Spanish company launched Quasar 438B, its first large-scale model and a system designed specifically for coding and enterprise agents. Quasar achieves a score of 43 on Artificial Analysis’ Intelligence Index and 69.3 on Terminal-Bench v2.1, while Artificial Analysis currently records its output speed at approximately 183 tokens per second. Multiverse is positioning Quasar as the European model with the highest score on the Intelligence Index, outperforming Mistral Medium 3.5 (30) and NVIDIA Nemotron 3 Ultra (38).

Multiverse is betting that a 438B-parameter model can be fast and inexpensive enough for agents that repeatedly reason, call tools, and check the results.

That puts Quasar in an interesting middle ground. It doesn’t match the strongest models on coding performance, but Multiverse is betting that a 438B-parameter model can be fast and inexpensive enough for agents that repeatedly reason, call tools, and check the results.

The model features a context window of one million tokens, is available in English and Spanish, and can be accessed via the Multiverse CompactifAI API.

Compression claims, missing details 

Multiverse built CompactifAI to shrink large AI models so they need less memory and compute to run. The company says it can reduce model size by 80% to 95% with only a small loss in accuracy, but it hasn’t disclosed how much Quasar was compressed or which model it started with.

In July, Multiverse announced a $570 million Series C to expand its library of compressed models and commercialize the technology. Quasar is the biggest test of that approach so far.

Multiverse hasn’t said what hardware is required to run Quasar or how much the compression reduces its memory and compute needs. That matters for agents, which may repeatedly call the model and other tools before finishing a task.

Multiverse hasn’t said what hardware it takes to run Quasar or how much the compression cuts its memory and compute needs.

Coding benchmarks show tradeoffs 

In Multiverse’s comparison, Quasar’s Terminal-Bench v2.1 score of 69.3 places it ahead of Mistral Medium 3.5 but still well behind the best frontier systems, while Claude Opus 5 achieves the highest score on that benchmark at 89.1.

Multiverse is pitching Quasar for software engineering, technical copilots, research, and workflow automation. Its 1-million-token context window gives agents room to work with large codebases and hold onto information as a task progresses, although processing more context also requires more compute. That can be especially important in coding, where code that passes every test can still trip up the next AI agent if it loses track of what came before.

Agent latency beyond throughput 

Artificial Analysis found that Quasar starts responding in about 1.1 seconds and can produce a 500-token response, including reasoning, in around 15.3 seconds. Those numbers are fast, but an agent also has to wait for tools, process growing context, and make repeated model calls over the course of a task. The agent tooling layer itself is still catching up to what these models need.

Those numbers are fast, but an agent also has to wait for tools, process growing context, and make repeated model calls over the course of a task.

Proprietary model, open questions 

Quasar is proprietary and only available through Multiverse’s API, so developers can’t inspect the weights or run it on their own hardware. For now, that also makes it difficult to know whether the speed Multiverse is reporting will carry over to everyday agent use.

Quasar also arrives as European AI companies are building more of their own model and compute infrastructure instead of relying on U.S. hyperscalers. Multiverse is taking a different route, using compression to make a 400B-plus model cheaper and faster to run. The next step is to see how that holds up against benchmarks.

The post Multiverse says its 438B model is fast enough for AI agents. The benchmarks tell a more complicated story. appeared first on The New Stack.

Thanks to increasingly capable models, some parts of your business are getting faster, more capable, and more productive every month. These teams are using artificial intelligence to compress timelines, surface insights, and automate work that has historically been time-consuming and tedious.

Meanwhile, other functions just down the hall are still waiting for a formal rollout, a governance approval, or someone to tell them what to do and how to start. The gap between the AI haves and have-nots in your organization is widening, and addressing it requires a new operating model.

Your teams need more support

When leaders notice the uneven distribution of capability across their business, the instinct is to treat it as a tooling problem. They push to get everyone access to the same platforms, provide general-use training, and hire some specialists to slot into IT.

But access is table stakes. It’s a good start, but it won’t get you to strong organizational adoption.

Harvard Business School reports that workers using these tools completed tasks 25% faster and produced results rated more than 40% higher in quality. But the same study also found that performance declined when people used the tools without understanding where they applied and where they didn’t. Fluency, not just access, drives results.

Departmental leaders need guidance on how to apply capabilities in the context of their day-to-day work. Without that knowledge, they can’t ask the right questions.

“Performance declined when people used the tools without understanding where they applied and where they didn’t. Fluency, not just access, drives results.”

Teams playing catch-up tend to focus on how to inject new tools into existing workflows, when they should be thinking about re-engineering processes entirely. They’re focused on evolution in a world undergoing revolution.

Reimagining a process also requires stepping back from it, which is easier said than done. Here’s how it plays out in practice:

An SDR team comes to IT with a specific, bounded ask: “improve our sales lead routing.” Completely reasonable. But only when someone from IT, with visibility across the broader system, dives into the problem does the real opportunity surface. The data pipeline supporting lead routing is unnecessarily complex. With the right support, the conversation shifts to overhauling the entire pipeline and opens the door to fully agentic lead follow-ups.

Departmental leaders don’t lack ambition but throwing a software license and Slack channel at them won’t build the right kind of adoption. Technical support and strategic guidance are required to reimagine work from first principles.

AI fluency must be a structural consideration

The typical pattern puts a centralized team in charge of taking requirements, interpreting them in isolation, and delivering capabilities to departments months later. This model can’t keep pace when AI capabilities launch weekly.

A more effective approach pairs a central “hub” that owns platform strategy, governance, and reusable patterns with AI engineers embedded directly inside business departments. AI engineers serve as “spokes” inside departments, helping them identify vertical use cases day-to-day and delivering the cross-functional visibility needed to make a real impact. The AI engineer who solved a problem for finance can share the pattern with someone facing the same challenge in operations.

“A more effective approach pairs a central “hub” with AI engineers embedded directly inside business departments.”

In a department just getting started, the embedded AI engineer is the primary technical capability: scouting, prototyping, building. In a more mature department, they shift toward enablement, feeding patterns back to the “hub” and helping teams navigate AI without getting buried in process. Over time, departments will organically become AI-fluent as they learn from the engineers.

Make fluency your advantage

The right operating model drives how a function actually works, and strong fluency strengthens processes and institutional knowledge, so outcomes improve over time. As the flywheel builds, each problem solved raises the ceiling of what your team can do independently. 

McKinsey finds that the right workflow redesign is the single biggest factor in whether an enterprise sees meaningful bottom-line impact. Knowing what to redesign depends on how your teams understand and work with AI.

Everyone is adopting AI capabilities. The question now is whether your operating model helps your teams see the best path forward for applying them. If it doesn’t, that’s the gap to close first.

The post Your organization prioritized AI adoption, but you actually need AI fluency. appeared first on The New Stack.