DeepSeek V4 vs Opus 4.7 vs GPT 5.5: Which Coding Agent Actually Wins?

I tested all three models head-to-head — Opus 4.7 in Claude Code, GPT 5.5 in Codex, and DeepSeek V4 Pro in Open Code — across two real coding tasks: a Three.js flight simulator and a WebGPU landing page. GPT 5.5 won the flight sim outright. Opus 4.7 won the WebGPU page on taste. DeepSeek struggled on both, but at roughly 8x cheaper API pricing, it's still the right choice for simpler tasks. Here's the full breakdown — benchmarks, prices, and what each model actually built.

What's New With These Three Models?

In the last 24 hours, two of these models shipped fresh updates. GPT 5.5 is OpenAI's newest flagship, with benchmark scores that beat Anthropic's reportedly unreleased internal model (Mythos) on at least one major coding benchmark. DeepSeek V4 is the new open-source open-weight release with frontier-tier benchmarks at a fraction of the cost. Opus 4.7 is Anthropic's current flagship, holding the top spot on SWE-Bench Verified and SWE-Bench Pro.

That gives you three serious options if you're picking an AI coding agent right now — but the right choice depends on which job you're hiring it for.

How Much Does Each Model Cost?

Cost-per-million-tokens of output:

• GPT 5.5: $30
• Opus 4.7: $25
• DeepSeek V4 Pro: $3.48

Input tokens:

• GPT 5.5: $5 per 1M
• Opus 4.7: $5 per 1M
• DeepSeek V4 Pro: ~$1.70 per 1M

DeepSeek is roughly 8x cheaper than Opus on output, and Opus is slightly cheaper than GPT 5.5. That last one is unusual — historically OpenAI has been cheaper than Anthropic. GPT 5.5 is also about 2x the price of GPT 5.4, but OpenAI claims the model uses fewer tokens per task, so the effective price difference for the same workload is closer to 20% more.

One important caveat on DeepSeek: even though it's open-weight, you're not running this on your laptop. It's a 1.6 trillion parameter model — you'll be paying for API access either way. The "open-source" benefit here is leverage on price, not self-hosting.

What Do the Benchmarks Say?

All three models report numbers on three coding benchmarks that overlap: SWE-Bench Verified, SWE-Bench Pro, and Terminal Bench 2.0.

• SWE-Bench Verified: Opus 4.7 wins. DeepSeek V4 Pro lands ~5 points behind in third.
• SWE-Bench Pro: Opus 4.7 wins again.
• Terminal Bench 2.0: GPT 5.5 wins by a wide margin at 87.2 — higher than what Anthropic reportedly recorded for their unreleased Mythos model.

The interesting story isn't who wins each benchmark — it's how close DeepSeek is to the frontier despite the price gap. On SWE-Bench Verified, the spread between second and third place is ~1 point at 8x lower cost. That's meaningful for high-volume use cases.

There's also a long-context regression to flag: Opus 4.7 underperforms 4.6 in the 500K–1M token range. That's an odd anti-pattern — newer model, worse retrieval at long context. Most workflows don't operate in that range (context rot is real at every model that far out), but worth knowing if your use case lives there.

Test 1: Three.js Flight Simulator

I gave each agent the same prompt: build a flight simulator in Three.js that runs in the browser, feels good to fly, has weight to it, and uses whatever structure and tooling it thinks is correct.

All three got plan-mode questions out of the way (full sim vs arcade, terrain type, camera angle) and then went to work.

GPT 5.5 (Codex) — Winner

First pass: Took ~7 minutes, used 63K tokens. Plane wouldn't take off — something was wrong with the rotation speed.

Second + third passes: I prompted it to make takeoff easier and improve the graphics. It found that the brakes were locked on at spawn and the takeoff flaps weren't auto-set. Fixed both.

Final result: actually playable. Real altitude tracking, AGL, knots, heading, AOA indicator. Controls were a little janky but the simulator worked. Total: ~66K tokens, ~10 minutes including iteration.

Opus 4.7 (Claude Code) — Second Place

Plan took 5 minutes — and was by far the most thorough. It walked through stack layout, flight model, stalls, stall buzzer behavior, controls, world layout, aircraft specs, performance notes. Genuinely detailed.

First pass: 13 minutes, 11% context. The plane spawned mid-air, instantly stalled, and crashed. Couldn't recover.

Second + third passes: Tried to get it to spawn on the runway and feel more arcadey. Got tricycle gear and runway spawn but the simulator threw me into a fog bank with uncontrollable controls.

Total: ~150K tokens, ~20 minutes. The under-the-hood physics were probably correct — angle of attack, stall thresholds — but the user-facing controls didn't translate. Could probably get to 5.5's quality with better prompts and more iteration.

DeepSeek V4 Pro (Open Code) — Third

First pass: ~10 minutes, 63K tokens. The output was unusable — third-person camera was broken, graphics buggy, nothing identifiable on screen.

Second pass: Asked it to fix. Got a slightly visible plane shape but still nowhere near functional.

The verdict: DeepSeek would have needed very specific, hand-held prompting to recover here. GPT 5.5 got something usable from a vague prompt. DeepSeek didn't.

Test 1 winner: GPT 5.5 in Codex

Faster, cheaper, and the only one that produced a functional simulator. Opus 4.7 was second on potential, distant on execution. DeepSeek wasn't competitive.

Test 2: WebGPU Shader Landing Page

This one is a step above your standard SaaS landing page. WebGPU shader work is what you see on Awwwards-tier sites — sites like Igloo, where the page renders particle systems and shaders through your GPU.

I gave all three the same prompt: build a modern, visually striking landing page that makes smart use of GPU compute, with a hero concept of their choosing. All three got the same WebGPU/Three.js skill loaded.

GPT 5.5 (Codex)

Generated a "living signal field" hero with dense particles. First pass was way too bright — particles overwhelmed the hero copy. Second pass dialed brightness down and pushed the field to the right.

Final result: functional, slightly blurry, did what was asked. Not gorgeous, not ugly. ~107K tokens, ~6 minutes.

Opus 4.7 (Claude Code) — Winner on Taste

The output was understated — almost subliminal. The shader effect ran across the whole background as a film-grain-style sweep at 250,000 particles, with FPS tracking at the bottom of the screen.

Less flashy than GPT 5.5's. Better visually. The kind of restraint that reads as "designed" rather than "generated." Total: ~175K tokens.

This is a taste call. GPT 5.5's was technically more visible. Opus 4.7's was more elegant. If I were shipping a portfolio site, I'd ship Opus's.

DeepSeek V4 Pro (Open Code)

Particle field that loosely followed my mouse with strobing color shifts. Almost epileptic. Second pass added blue parallax effects and turned the central element into something that looked like a UFO.

Total: 130K tokens, $1.43. Cheap. Not usable.

Test 2 winner: Opus 4.7

Better aesthetic call. GPT 5.5 was a close second. DeepSeek was off the map.

Which Model Should You Actually Use?

Here's the honest take:

Use GPT 5.5 in Codex when:

• You need fast, functional output on technical tasks (game logic, simulators, complex interactions)
• You want lower latency and you're cost-sensitive
• The task has clear structure and the agent can grind on it

Use Opus 4.7 in Claude Code when:

• You need design taste (UI, marketing pages, anything visual)
• The plan needs to be deeply considered before execution
• You're working in a long-running session where its detailed planning pays off

Use DeepSeek V4 Pro in Open Code when:

• The task is simpler and clearly scoped
• Cost dominates the decision (you're running thousands of tasks per day)
• You're willing to give it more guidance per prompt to compensate

The bigger takeaway: there's no vendor lock-in here. What you actually learn working with any of these tools is AI fundamentals — prompting, planning, iteration patterns, tool use. Those skills transfer. Switching from Claude Code to Codex or vice versa is a configuration change, not a re-skilling project.

More competition at the frontier is good for everyone using these tools.

Frequently Asked Questions

Is DeepSeek V4 Pro worth using if it lost both tests?

For complex multi-step builds, no. For simpler tasks where cost matters more than peak quality — internal scripts, batch refactors, repetitive data cleanup — the 8x price difference makes it the right call. Don't pick a model in a vacuum. Pick it for the workload.

Can I run DeepSeek V4 locally since it's open-source?

Not realistically. The model is 1.6 trillion parameters — even at quantized precision, you're looking at hardware most teams don't have. The "open-weight" benefit is competitive pressure on API pricing and the ability to fine-tune, not personal-machine inference.

Why did Opus 4.7 do worse than 4.6 on long context?

Anthropic hasn't published a formal explanation, but the regression in the 500K–1M token range is real per the published benchmarks. Most workflows don't operate that far out, and context rot affects every model at long context, but it's worth knowing if you're working on use cases that genuinely need it.

Are these models interchangeable in their respective harnesses?

Mostly, yes. Codex, Claude Code, and Open Code are all coding agent harnesses with similar plan-mode flows, skill systems, and tool use patterns. The skills you learn in one transfer almost completely to the others. Don't treat any harness as a one-way commitment.

Should I trust benchmark scores when picking a model?

Take them with a grain of salt — but they're not useless. Pay attention to numbers reported by the model maker on a benchmark all three competitors also report. That's where you can do an apples-to-apples comparison. Cherry-picked benchmarks where only one model has results are marketing, not data.

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