DeepSeek V4 Pro Tested: Massive Open-Source Model With Surprising Real-World Results

DeepSeek V4 has landed, and it’s one of the most ambitious open-source AI releases so far. The flagship V4 Pro model combines massive scale, long context, and relatively low pricing, while also shipping as open weights that developers can download and run themselves—if they have the hardware.

This article walks through what DeepSeek V4 Pro is, how it’s priced, and how it actually behaves in real-world coding and simulation tests, from web-based OS mockups to 3D games, beat ’em ups, and interactive dashboards.

What DeepSeek V4 Brings to the Table

DeepSeek V4 arrives in two main flavors: Pro and Flash. V4 Pro is the flagship, API-first model, while Flash is a smaller but still huge variant aimed at more realistic local setups.

V4 Pro is a mixture-of-experts model with a total of around 1.66 trillion parameters, with about 49 billion active per token. It also supports a context length of up to 1 million tokens, putting it in the ultra-long-context category. That means it can, in theory, handle extremely large documents, codebases, or multi-step workflows in a single session.

If you want a deeper dive into the architecture, benchmarks, and how Pro compares to Flash, check out this breakdown of DeepSeek V4 Pro and Flash.

Open Weights and Local-Friendly Flash Model

One of the biggest headlines is that DeepSeek V4 is open weight. The model files are hosted on Hugging Face, which means anyone with enough compute can download and run them locally, without needing an internet connection.

While V4 Pro is realistically out of reach for most consumer hardware, the V4 Flash model—at around 158 billion parameters—is a more plausible candidate for hobbyists and enthusiasts with strong local setups. It’s still huge, but it opens the door to serious local experimentation with a modern, high-end model.

Compared to DeepSeek V3.2, the V4 models are also significantly more efficient in terms of compute and memory usage. That’s important both for cloud deployments and for anyone trying to squeeze as much as possible out of a single workstation or server.

Pricing: Aggressive Compared to GPT-5.5

DeepSeek is also competing hard on price. For V4 Pro, the most expensive listed scenario is:

• Around $1.74 per 1M input tokens
• Around $3.48 per 1M output tokens

For comparison, GPT-5.5 (as referenced in the test) is around $5 per 1M input tokens and $30 per 1M output tokens. That makes DeepSeek V4 Pro dramatically cheaper on the output side, which matters a lot for code generation, long-form reasoning, and content-heavy workflows.

In practice, this positions V4 Pro as a strong candidate for offloading “bulk” tasks—large-context analysis, long code generations, or iterative development—while more expensive models can be reserved for very specialized or niche jobs.

Benchmarks vs. Real-World Behavior

On paper, DeepSeek V4 Pro looks competitive with top-tier models like GPT-5 and Gemini Pro in many benchmarks. The official charts show it matching or beating them in several categories, though some of the comparisons use slightly older model versions.

However, benchmarks only tell part of the story. The more interesting question is how V4 Pro behaves when you ask it to build non-trivial things: multi-window web apps, 3D games, simulations, and interactive tools. That’s where this hands-on testing focused.

For a broader context on how V4 stacks up across tasks, you may also want to read these early impressions of DeepSeek V4.

Web OS Test: A Mini Desktop in the Browser

One of the first tests was a “browser OS” challenge. The model was asked to build a mini desktop environment in the browser with:

• A start menu and icons
• Two 3D games (one of them a simple GTA-style clone)
• A calculator, notepad, terminal, and file explorer
• Wallpaper changing
• A special feature of its own choosing

DeepSeek V4 Pro generated around 2,700 lines of code for this environment. The result included a start menu, hover effects, a working right-click menu, and a live clock. The GTA-style 3D game featured a basic city grid with buildings, trees, streets, and a drivable car, complete with mesh colliders and fullscreen support.

The second 3D game, a space shooter, was more basic and only loosely 3D. Initially, several apps—calculator, notepad, terminal, and file explorer—failed to load due to bugs. Using an external tool (Open Code) to debug and patch the script, those issues were fixed without regenerating everything from scratch.

Once fixed, the calculator worked correctly, notepad and file explorer were functional, and the terminal introduced the model’s “special feature”: text commands that could move and control windows (for example, moving the calculator window via a terminal command). This terminal-based window control was a clever touch and not something you typically see from a one-shot prompt.

3D Subway Scene and Beat ’Em Up Conversion

Next up was a “beautiful static subway scene” test: build a 3D subway station you can explore with keyboard movement and a brightness slider to adjust lighting.

The first attempt failed to load due to console errors, but again, Open Code was used to identify and fix the issues. Once running, the subway scene included:

• Arched ceilings and tiled floors
• Train tracks with yellow safety lines
• Vending machines and platforms
• Stairs (without proper colliders, so you could pass through)

Movement controls were inverted (S moved forward instead of W), but the overall layout was coherent. The brightness slider worked well, with a particularly nice “nighttime subway” look when dimmed.

A follow-up prompt then asked the model to turn this subway map into a 3D “commuter beat ’em up” with humanoid characters, rigged animations, and arcade-style mechanics. After some configuration fixes in Open Code, the result was surprisingly fun:

• Low-poly humanoid characters with walking and attack animations
• Multiple waves of enemies
• Combo counters and particle effects
• Sound effects that matched hits and combos, giving it a real arcade vibe

A second iteration further improved the feel, adding better camera behavior (though still a bit awkward), more polished hit feedback, and more satisfying combos. This subway beat ’em up ended up being one of the strongest, most entertaining results in the entire test.

Flight Combat Simulator: Good Shell, Light on Gameplay

The flight combat simulator test asked the model to create a 3D air combat game with multiple planes and enemies.

DeepSeek V4 Pro produced a promising start screen with three selectable planes (fighter jet, propeller plane, stealth jet), each previewed in 3D with hover effects. The environment included terrain, buildings, an airstrip, and clouds, and the planes had basic controls and weapons.

However, the core gameplay was underdeveloped:

• Plane control felt awkward due to the mouse-keyboard interaction
• No visible enemies appeared during testing
• Autopilot existed but didn’t add much depth

Visually and structurally, it showed potential, but as a game it felt more like a tech demo than a complete experience.

Ship Combat and Water Simulation

A ship combat simulator test was used mainly to see how well the model could handle water rendering and ship physics.

The water effects were actually quite good, with convincing surface motion and reflections. Unfortunately, the ship model was rendered underneath the water instead of on top of it. Attempts to have the model fix this via Open Code—by adjusting offsets and transforms—did not fully resolve the issue.

This was a recurring theme: DeepSeek V4 Pro often got 80–90% of the way there, but small spatial or physics bugs could be stubborn to eliminate without more manual intervention.

3D Printer Simulation: Thinking vs. No-Thinking

The 3D printer simulation test asked for a realistic core-XY style printer with a filament spool, extruder, and bed, plus shape selection (cube, circle, triangle) and visible layer-by-layer printing.

Two versions were run:

• Through DeepSeek’s own web UI with “thinking” (reasoning) enabled
• Through a custom chat interface with thinking disabled

With thinking enabled, the result was more coherent: the printer had a proper filament path from spool to extruder, a clear frame, and a moving bed that descended in steps as layers were added. The nozzle moved in patterns that matched the selected shape, and while the print process was simplified, it felt like a believable simulation.

With thinking disabled, the printer looked more basic. The nozzle motion didn’t align as clearly with the shapes, and the printing behavior was more of a simple “pancake stack” effect. This side-by-side comparison suggested that DeepSeek’s explicit reasoning mode can materially improve the quality of complex, multi-step simulations.

C++ Skateboard Game: Ambitious, But Janky

Another demanding test was a self-contained C++ skateboard game with a late-’90s California boardwalk vibe. The model had to:

• Inspect the system’s compilers and dependencies
• Plan the architecture in “plan” mode
• Generate and compile a full C++ project
• Create a playable 3D skateboarding scene

DeepSeek V4 Pro did a thorough job of planning and repeatedly refined its own code, recompiling several times as it found issues. The final result featured:

• A beachside boardwalk with multiple buildings
• Numerous humanoid NPCs
• A skater character with particle effects and trick attempts

However, the game remained rough. The skater’s proportions and animations were off, characters sometimes fell through the map, and camera behavior was inconsistent. The environment itself looked promising, but the overall experience felt like an early prototype rather than a polished mini-game.

Analytics Dashboard UI: Clean and On-Brand

To test front-end design ability, DeepSeek V4 Pro was asked to build an interactive analytics dashboard for a fictional AI SaaS product, focusing on layout and visual polish rather than complex backend logic.

The resulting UI was one of the cleaner outputs:

• A modern, card-based layout with charts, metrics, and navigation
• Sections for revenue, API calls, model usage, and customer accounts
• Fake but plausible data, including named plans and alerts
• Date range controls that updated chart visuals

Not every control was wired up (for example, search didn’t function), but as a static front-end prototype it looked like something you might see in a real product. This suggests V4 Pro is quite capable at UI scaffolding and design-oriented tasks.

Drum Kit Simulation with Autoplay Grooves

The drum kit simulation test asked for a 3D or 2.5D photorealistic drum kit playable via keyboard, plus an autoplay mode with four preset grooves (for example, rock, jazz, hip-hop).

In the DeepSeek web UI, the model produced a solid-looking kit:

• Nicely modeled hi-hat and stands
• Responsive drum hits mapped to keys
• Autoplay patterns for different styles

The hip-hop groove in particular stood out as surprisingly catchy, with timing and feel that made it sound like a real programmed beat rather than random hits. Overall, this was another area where V4 Pro’s sense of structure and rhythm came through well.

Overall Impressions: Powerful, But Clearly a Preview

Across all these tests, a consistent pattern emerged:

• DeepSeek V4 Pro is capable of building complex multi-file projects, 3D scenes, and interactive UIs from scratch.
• It often gets very close to a fully working result, but small bugs—especially in 3D positioning, physics, and camera control—are common.
• When paired with a tool like Open Code and given a chance to iteratively debug, it can significantly improve its own work and fix many issues.
• The explicit reasoning/thinking mode appears to meaningfully improve quality on more complex tasks.

At the same time, it doesn’t yet feel like a “mind-blowing” leap over other top models in real-world coding tasks. Many results are good and sometimes impressive, but not consistently outstanding. That aligns with the fact that this is labeled as a preview release.

Where DeepSeek V4 Pro really stands out is in its combination of:

• Open weights (for those with serious hardware)
• Massive 1M-token context
• Strong performance across a wide range of tasks
• Very aggressive pricing compared to proprietary competitors

That mix makes it a compelling option for developers who want a powerful, affordable model for large-context workloads, experimentation, and hybrid pipelines where different models handle different parts of a task.

Cost of Testing and Practical Takeaways

The entire suite of tests—multiple 3D games, simulations, UI builds, and iterative debugging via API—cost about $3.68 in total. Given the scale and complexity of what was attempted, that’s a strong signal that DeepSeek V4 Pro can be used heavily without breaking the bank.

In practical terms, a sensible strategy might look like this:

• Use DeepSeek V4 Pro for long-context reasoning, large code generations, and iterative prototyping where cost and context length matter most.
• Reserve more expensive proprietary models for extremely niche tasks (for example, legacy Windows XP .exe generation) or where you need the absolute highest reliability on the first try.
• Experiment with the V4 Flash model locally if you have a strong workstation and want to bring a capable, modern LLM fully on-prem.

DeepSeek V4 Pro may not recreate the exact shockwave of earlier DeepSeek releases, but as the largest open-weight model to date—paired with competitive performance and pricing—it’s a major step forward for the open AI ecosystem.