GPT‑5.5 vs DeepSeek V4 Pro vs Claude Opus 4.7: KingBench 2.0 Coding & UI Showdown

Three of the most talked‑about large language models right now—GPT‑5.5, DeepSeek V4 Pro, and Claude Opus 4.7—were put through a refreshed benchmark called KingBench 2.0. Instead of just looking at static scores or leaderboards, this benchmark focuses on real coding tasks, front‑end behavior, and tricky edge cases that expose how these models actually behave in practice.

What Is KingBench 2.0 Testing?

KingBench 2.0 is designed to stress models across multiple aspects of coding, not just simple algorithm questions or agent workflows. Some tasks assume you might plug the model into an agent system, while others test what it can do on its own. The focus is on:

• Front‑end UI quality and stability
• Complex back‑end logic and state handling
• 3D and graphics tasks (like Three.js and SVG)
• Small interactive games and simulations
• Early attempts at more advanced workflows like fine‑tuning

The benchmark is still being managed via spreadsheets for now, but it already reveals clear patterns in how each model behaves.

DeepSeek V4 Pro and GPT‑5.5: What’s New on Paper?

DeepSeek recently launched two new models: DeepSeek V4 Pro and DeepSeek V4 Flash. V4 Pro is a huge Mixture‑of‑Experts model with around 1.6 trillion parameters, but only about 49 billion are active per request. V4 Flash is smaller at 284 billion parameters, with 13 billion active per pass. Both offer a 1 million token context window and are aggressively priced, especially compared to closed models.

Under the hood, DeepSeek is using the Muon optimizer, which has been discussed in other projects like Moonshot as a scalable alternative to more traditional optimizers. That makes V4 Pro one of the largest known deployments of Muon so far.

On the other side, GPT‑5.5 is positioned as OpenAI’s next big step after GPT‑4‑class models. It’s supposed to beat Claude Opus 4.7 across most benchmarks and fix some long‑standing UX issues—especially around front‑end code quality and token efficiency. It also reportedly uses fewer tokens for similar tasks, which matters a lot for API users.

If you want a deeper dive into how GPT‑5.5 stacks up more broadly against Claude, there’s a separate comparison in GPT 5.5 and ChatGPT Images 2 vs Claude Opus: Real Tests, Real Results.

Front‑End & Simulation Tasks: Where Models Fall Apart

Elevator Simulator: UI + Logic in One Test

One of the first KingBench 2.0 tasks is an elevator simulator: multiple floors, people spawning on floors, and an elevator that moves one person at a time to their destination until no one is left. This tests both front‑end rendering and back‑end state logic.

• DeepSeek V4 Pro: The elevator behavior is essentially wrong. Positions look random, and the simulation doesn’t feel coherent or usable.
• GPT‑5.5: The logic kind of works, but the UI flickers heavily and looks messy. It still leans on the same “card” style layout that has frustrated many users of previous GPT front‑end generations.
• Claude Opus 4.7: Produces a clean, stable, and visually appealing implementation. It behaves like something a competent human front‑end dev might ship on a first pass.

On this task, Opus 4.7 is clearly ahead.

3D Contact Lens Case in Three.js

Next up is a more unusual request: a 3D contact lens case in Three.js with clickable lids that open. This is deliberately outside the usual training distribution and tests 3D reasoning plus interactivity.

• DeepSeek V4 Pro: The result looks more like a block with two holes than a realistic case. It technically renders, but it fails the design intent.
• GPT‑5.5: Slightly better, but still not great. The lids can open, but only one side opens (and on the wrong side), which breaks the interaction model.
• Claude Opus 4.7: The best of the three, with a more plausible contact lens case. However, left/right labels are flipped and the cap opens from the bottom, which is unintuitive.

All three struggle here, but Opus is the least bad.

Folding Table in Three.js

Another 3D task asks for a folding table with a slider that lets the user fold and unfold it.

• DeepSeek V4 Pro: Surprisingly decent. Not perfect, but the folding logic works and the behavior is acceptable for a first pass.
• GPT‑5.5: Looks okay when unfolded, but the folded state is broken—overlapping parts and visually incorrect geometry.
• Claude Opus 4.7: Works, but not impressively. It’s usable, yet not as strong as you might expect given its performance on other UI tasks.

This is one of the few tasks where DeepSeek V4 Pro holds its own reasonably well.

SVG Panda Eating a Burger

A simpler but still creative test: generate an SVG of a panda eating a burger.

• DeepSeek V4 Pro: The panda barely resembles a panda—more like a rock with features. Fails the visual brief.
• GPT‑5.5: Also weird and off‑model visually.
• Claude Opus 4.7: Not amazing, but clearly the best of the three. The panda and burger are at least recognizable and reasonably styled.

Again, Opus 4.7 shows stronger visual composition and SVG structure.

Game & Advanced Workflow Tasks

Bow and Arrow Simulator

Another key test is a simple bow‑and‑arrow game: aim, shoot, and handle basic physics or hit detection. This is great for testing interactivity, event handling, and clean UI structure.

• DeepSeek V4 Pro: The game is buggy and effectively non‑functional. It fails as a playable experience.
• GPT‑5.5: Delivers a working game. You can aim and shoot, and the logic is mostly fine. The main downside is the same overused card‑style UI that makes the result look generic and cluttered.
• Claude Opus 4.7: Produces a polished, professional‑looking mini‑game. The UI is clean, interactions feel natural, and the overall experience is closer to something you’d actually ship.

On interactive front‑end work, Opus 4.7 is clearly in the lead, with GPT‑5.5 in second and DeepSeek lagging behind.

Math and Fine‑Tuning Tasks

KingBench 2.0 also includes a tougher mathematics question and a more advanced request: fine‑tune a Gemma 4 model using a generated dataset for Pandaax.

• None of the three models successfully solve the new math problem.
• None are able to fully deliver a correct, end‑to‑end fine‑tuning workflow for Gemma 4 with generated data.

These tasks highlight that, even at the top end, current models still struggle with complex, multi‑step ML workflows that require precise, production‑ready instructions.

Pricing, Value, and Real‑World Trade‑Offs

DeepSeek’s biggest advantage is cost. DeepSeek V4 Pro offers a 1M token context window at around $1.74 per million input tokens and $3.78 per million output tokens, which is extremely cheap compared to most closed models. DeepSeek V4 Flash is even cheaper on input (around $0.04 per million tokens) with similarly low output pricing, also with a 1M context window.

GPT‑5.5, by contrast, is significantly more expensive. Even if it uses fewer tokens per task, the higher per‑token price means many API users will pay more over time. For heavy coding or agentic workloads, that difference adds up quickly.

Claude Opus 4.7 sits in an awkward middle ground: it’s arguably the best model overall in this test, but the user experience is hampered by strict rate limits and plan constraints, especially on coding‑focused tiers. That makes it harder to rely on for sustained development work, even if its raw quality is excellent. For more detail on how Opus 4.7 behaves in practice, there’s a dedicated look in DeepSeek V4 Pro Tested: Massive Open-Source Model With Surprising Real-World Results, which also gives additional context on DeepSeek’s positioning.

Which Model Is Actually Worth Using Right Now?

Based on these KingBench 2.0 tasks, a few patterns are clear:

• Claude Opus 4.7 is the most consistently strong model for real‑world coding, UI, and small interactive projects. Its outputs look and feel closer to human‑written code and design. The downside is rate limits and plan restrictions, which hurt the overall experience.
• GPT‑5.5 is good at some things—like working bow‑and‑arrow games and improved token efficiency—but still struggles with messy front‑end layouts and doesn’t clearly beat Opus in real‑world quality. Its higher cost also makes it harder to justify purely on value.
• DeepSeek V4 Pro is cheap and huge on paper, but its real‑world performance in this benchmark is middling. It’s not terrible, but it’s not good enough to recommend as a primary coding model if quality is your top priority.

If you care most about polished front‑end code and interactive experiences, Opus 4.7 is still the standout—assuming you can live with the usage limits. If cost is your main concern and you’re willing to accept weaker quality, DeepSeek V4 Pro (or V4 Flash) is attractive from a pricing standpoint. GPT‑5.5 sits in between: capable, but currently overpriced relative to what DeepSeek offers and not clearly superior to Opus in hands‑on tests.

The bottom line: model choice in 2025 is less about raw benchmark scores and more about the balance between quality, cost, and platform limits. KingBench 2.0 shows that on real coding tasks, those trade‑offs are more visible than ever.