I Tested DeepSeek V4 vs Opus 4.7 vs GPT 5.5: Which Model Should You Actually Use?

Three of the most talked‑about AI coding models right now are GPT 5.5, Claude Opus 4.7, and DeepSeek V4. On paper, all three look impressive. But if you’re an actual developer or power user, the real question is simple: which one should you use for real work?

This article walks through how these models compare on cost, benchmarks, and—most importantly—two practical tests: building a browser‑based 3D flight simulator and a WebGPU‑powered landing page.

Pricing: How Much Do These Models Really Cost?

Before performance, it’s worth understanding how much each model will cost you at scale. All three are accessed via APIs in these tests, even DeepSeek V4, which is technically open‑weight but far too large (around 1.6T parameters) for most local setups.

Here’s how output pricing (per 1M tokens) compares:

• GPT 5.5: $30 per 1M output tokens
• Claude Opus 4.7: $25 per 1M output tokens
• DeepSeek V4: ~$3.48 per 1M output tokens

For input tokens (per 1M):

• GPT 5.5: $5
• Claude Opus 4.7: $5
• DeepSeek V4: ~ $1.70

DeepSeek V4 is roughly 8x cheaper than the frontier models. GPT 5.5 is also about twice as expensive as GPT 5.4, but OpenAI claims it uses fewer tokens to complete the same tasks, so total cost per task may only be ~20% higher in practice.

Benchmarks: Strong Numbers, But Close Gaps

All three models report results on several coding benchmarks, including Swebench Verified, Swebench Pro, and TerminalBench 2.0. Benchmarks aren’t everything, but they do give a directional sense of capability.

Across these:

• Claude Opus 4.7 tends to win on Swebench Verified and Swebench Pro.
• GPT 5.5 dominates TerminalBench 2.0 with a score around 87.2—higher than Anthropic’s own internal Mythos model.
• DeepSeek V4 usually lands in third place, but often only a few points behind Opus while being dramatically cheaper.

The most interesting pattern is that DeepSeek V4 is consistently within “striking distance” of Opus 4.7 on coding benchmarks, despite its much lower price. For many workloads, that trade‑off may be worth it.

Long‑context performance is another wrinkle. In the 500k–1M token range, Claude Opus 4.7 appears to regress compared to 4.6 and performs noticeably worse than GPT 5.5 and DeepSeek V4. In practice, very few users work reliably at 500k+ tokens without running into context rot anyway, but it’s still a notable regression for Anthropic’s latest flagship.

If you want a deeper dive into benchmark behavior and the broader “compute war” between these models, check out this breakdown of GPT‑5.5 vs DeepSeek V4 benchmarks and positioning.

Test 1: Building a 3D Flight Simulator in the Browser

The first real‑world test was a browser‑based flight simulator built with Three.js. The prompt asked for:

• A plane that feels good to fly, with some sense of weight
• Strong visuals
• Freedom to choose structure, tools, and implementation details

The test wasn’t just about one‑shot output. Each model went through multiple iterations, with follow‑up prompts to fix issues and improve playability. Four factors were evaluated:

1. Time to build
2. Token (and cost) usage
3. Quality of the final simulator
4. Overall “vibes” and usability

Planning Phase: How Each Model Thinks

DeepSeek V4: Produced a short, bare‑bones plan—basic project structure and a few bullet points on physics, environment, camera, and HUD. Minimal detail.

GPT 5.5 (via Codeex): Generated a structured plan with a summary, key changes, implementation details, test plan, and assumptions. Clear and organized.

Claude Opus 4.7 (via Claude Code): Took the longest to plan (about 5 minutes) but went very deep. It described the flight model, stall behavior, controls, world layout, aircraft characteristics, and performance considerations. Easily the most thorough “design doc” of the three.

Implementation & Results

GPT 5.5:

• First pass took about 7 minutes and ~63k tokens. The sim had clouds, HUD elements (speed, altitude, vertical speed, heading), and a runway, but the plane was very hard to get off the ground.
• After a prompt to make it more “arcadey” and improve graphics, it produced a nicer‑looking version—but still tricky to fly.
• A third iteration fixed key issues (like starting with brakes locked and overly realistic takeoff settings). The final result:

• Plane could reliably take off and fly through rings
• HUD was fairly sophisticated and accurate
• Controls were still a bit janky, but the sim was playable and visually decent

Total: ~66k tokens and around 10–15 minutes of back‑and‑forth for a reasonably solid browser flight sim.

DeepSeek V4:

• First pass took about 10 minutes and ~63k tokens—but the result was essentially unusable.
• The camera and graphics were completely broken: confusing views, glitchy visuals, and no coherent way to actually fly the plane.
• A second pass improved things slightly (you could at least see a plane), but the overall experience was still a mess.

To get anywhere near GPT 5.5’s first pass, you’d need to restart with a very detailed, hand‑holding prompt. For a typical “agent coder” workflow, DeepSeek V4 simply didn’t deliver here.

Claude Opus 4.7:

• Took the longest: roughly 20 minutes total (5 minutes planning + ~13 minutes coding) and around 150k tokens.
• First run spawned the plane instantly in the air, in heavy fog, stalling and crashing almost immediately—visually chaotic and hard to control.
• After feedback to start on the runway, make it easier to fly, and improve graphics, it made changes (e.g., tricycle gear, runway spawn) but still dropped the user into a foggy, hard‑to‑control scenario.
• A third pass tried to make controls more arcade‑like, but the sim remained difficult to fly and visually odd (trees on the runway, heavy fog, etc.).

Claude’s underlying physics and planning felt sophisticated, but the user experience never quite came together within a few iterations.

Flight Sim Verdict

Winner: GPT 5.5

• Best balance of speed, cost, and final quality
• Reached a playable, visually decent sim in relatively few iterations
• Used far fewer tokens than Claude Opus 4.7

Second: Claude Opus 4.7

• Great planning and rich technical detail
• Needed more time and prompts to become usable
• Most expensive and slowest overall

Last: DeepSeek V4

• Extremely cheap, but the output was so broken that it would likely be faster to start over with a different model or a much more constrained prompt.

If you’re interested in more hands‑on coding comparisons like this, there’s also a broader UI and coding showdown in this GPT‑5.5 vs DeepSeek V4 Pro vs Claude Opus 4.7 KingBench 2.0 test.

Test 2: WebGPU Landing Page with 3D Shaders

The second test pushed the models into high‑end front‑end territory: a modern landing page using WebGPU and shaders via Three.js—similar in spirit to the highly polished, game‑like experiences you see on awards‑style sites.

The prompt asked for:

• A modern, visually striking hero section
• Smart use of GPU compute and shader effects
• Freedom to choose stack, structure, and hero concept

All three models were given a helper “skill” explaining how to approach this kind of WebGPU + Three.js setup, so no model had a knowledge advantage. Interestingly, none of them asked clarifying questions in plan mode.

Plans: Similar Ideas, Different Execution

GPT 5.5: Planned a full‑bleed, interactive GPU‑driven hero with a “living signal field” of particles, pointer‑reactive compute simulation, and minimal, awards‑style copy.

DeepSeek V4: Proposed a hero with ~75,000 GPU‑computed particles, mouse interaction, and post‑processing effects like bloom, chromatic aberration, vignette, and film grain. The plan was short and to the point.

Claude Opus 4.7: Also aimed for an interactive particle‑based hero with bloom and mouse interaction. Conceptually similar to the others.

What They Actually Built

GPT 5.5:

• First pass took about 6 minutes and ~107k tokens.
• Produced a bright, particle‑based hero background with scroll‑linked animation and color shifts.
• The particles responded to mouse movement, and there were UI controls for different interaction modes (attract, repel, drift).
• The main issue: it was so bright and intense that it overpowered the hero text and made the particles hard to see clearly.

After feedback to reduce brightness and shift the effect more to the right, GPT 5.5 produced a toned‑down version. The particles were still a bit blurry and the design wasn’t exactly stunning, but it did satisfy the brief: a WebGPU‑powered, interactive, visually dynamic hero.

Claude Opus 4.7:

• Used about 175k tokens and took slightly longer than GPT 5.5.
• The result was more understated: a subtle, full‑page GPU background with a particle field, gentle motion, and a kind of film‑grain aesthetic.
• It tracked frames per second and used around 250k particles under the hood.

Visually, it looked clean and modern but not especially flashy. After a second pass asking for something more dramatic, the changes were still fairly subtle. This felt more like a tasteful, restrained design than a showy awards‑style hero.

DeepSeek V4:

• Consumed around 130k tokens in total and took the longest to finish, but still cost under $1 thanks to its low pricing.
• First version: a chaotic particle field that reacted to the mouse, but felt visually noisy and borderline seizure‑inducing.
• Second version: added some parallax and background color work, with a UFO‑like shape reacting to the cursor. It was more coherent than the first attempt, but still fairly bland and not particularly polished.

WebGPU Landing Page Verdict

Winner (on taste): Claude Opus 4.7

• Produced the most aesthetically pleasing and balanced design, even if it wasn’t the flashiest.
• Felt more like something you might actually ship with minor tweaks.

Close Second: GPT 5.5

• Technically hit the brief well: interactive, GPU‑driven, and visually intense.
• Design leaned more toward “flashy but a bit ugly” without strong art direction from the prompt.

Last: DeepSeek V4

• Again, very cheap, but the baseline output lacked the polish and coherence you’d expect from a high‑end WebGPU experience.

So Which Model Should You Use?

Putting everything together—cost, benchmarks, and the two real‑world tests—here’s how these models shake out for most users.

GPT 5.5: The Most Robust All‑Rounder

Best for: Agent coders, complex coding tasks, and users who value speed + reliability over raw cost.

• Consistently strong in coding benchmarks, especially TerminalBench 2.0.
• In the flight simulator test, it was the clear winner: fastest to a playable result, with fewer tokens and less prompting overhead than Claude.
• In the WebGPU test, it delivered a technically solid implementation that just needed better art direction.

If you want a powerful, dependable coding model and don’t mind paying more than DeepSeek, GPT 5.5 is a very safe choice.

Claude Opus 4.7: Deep Planning and Strong Taste

Best for: Users who value detailed reasoning, rich planning, and more tasteful outputs—especially for UI and product work.

• Often wins on Swebench‑style coding benchmarks.
• Produced the most thoughtful, detailed plan for the flight simulator, though it struggled to translate that into a friendly user experience quickly.
• In the WebGPU test, it generated the nicest‑looking landing page, even if it was less dramatic than GPT’s version.

Opus 4.7 is slower and more expensive in these tests, but if you like its “vibes” and design sensibilities, it’s a strong alternative to GPT 5.5—especially in Claude Code–style workflows.

DeepSeek V4: Ultra‑Cheap, But Not Frontier‑Level (Yet)

Best for: Simpler tasks, cost‑sensitive workloads, and users who are willing to trade quality for price.

• On benchmarks, DeepSeek V4 is surprisingly close to Opus 4.7 while being about 8x cheaper.
• In real coding tests, it struggled: the flight simulator was essentially unusable, and the WebGPU landing page felt chaotic or bland without heavy prompt steering.
• You could likely get good results with highly specific, constrained prompts—but you don’t get the same “just works” baseline as GPT 5.5 or Opus 4.7.

DeepSeek V4 doesn’t feel like a true competitor to GPT 5.5 or Opus 4.7 for complex, agent‑driven coding yet. But if you’re extremely token‑conscious and your tasks are simpler, it can still make sense.

Final Takeaways

• GPT 5.5 vs Opus 4.7: Both are excellent, and your choice will likely come down to personal preference, pricing, and ecosystem (Codeex vs Claude Code). For coding agents and complex builds, either is viable.

• DeepSeek V4: Impressive on paper and incredibly cheap, but in these hands‑on tests, it under‑delivered for advanced interactive projects. It’s best seen as a budget option for simpler workloads rather than a full replacement for frontier models.

The good news: competition at the top end is real. Whether you lean toward GPT 5.5 or Claude Opus 4.7, you now have multiple strong options for serious AI‑assisted coding—and that’s a win for everyone building with these tools.