AI Coding Shootout — Snake Game with 6 Models (May 2026): Claude 4.7, GPT-5.5, DeepSeek V4 Pro, Kimi 2.6, GLM 5.1, MiniMax 2.7

1. Test setup

Every model received the same prompt, verbatim. Same macOS Apple Silicon machine, same Python 3.12, same shell, same python snake.py invocation.

Write a Snake game in Python with:
- pygame
- 600x600 window, 20px grid cell
- arrow keys, no instant reverse
- score +10 per food, shown in window title
- game over on wall or self collision, show Game Over and final score
- press R to restart
- single .py file, no module split

The prompt has four implicit checkpoints that look easy but separate the models:

• Instruction adherence: 600x600, 20px, single file — does the model actually respect the constraints?
• Game dev common sense: "no instant reverse" tests whether the model understands input buffering.
• External dependency awareness: pygame is not in stdlib — does the model proactively explain how to install it?
• Cross-platform polish: does the text render correctly on macOS, or does the model assume Windows?

2. Results at a glance

Note: results are a May 19, 2026 snapshot using each vendor's default web interface. Models iterate fast, so treat the ranking as time-bound, not eternal.

3. Claude Code 4.7 — engineering literacy on display

Claude is one of two vendors that proactively documents the environment before pasting any code. Before the snake script, it tells me to create a virtualenv and install pygame, with separate commands for macOS, Linux, and Windows.

python3 -m venv .venv
source .venv/bin/activate
pip install pygame
python snake.py

The 139-line program is modular without overengineering: random_food(), reset_game(), main(). My favorite detail is its pending-direction buffer:

if new_dir is not None:
    if (new_dir[0] + direction[0], new_dir[1] + direction[1]) != (0, 0):
        pending_direction = new_dir

If the snake is moving right and the player taps Up then Left within a single frame, naive direction handling would let "Left" pass the reverse check (because it is reversing the still-current "Right"), and the snake would suicide on the next tick. Claude defers direction updates to the next tick, which removes the bug at the root. The prompt never asked for this; the model surfaced it on its own.

The Game Over screen uses pygame.SRCALPHA to draw a translucent overlay on top of the still-rendered snake and food, so the player can see where they died. MiniMax skips this and we will see why it hurts.

4. GPT-5.5 — the prettiest delivery, the finest function granularity

GPT-5.5 matches Claude on delivery format: venv + pip first, code second. The difference is that its code reads like a designer touched it.

The 165 lines are sliced into seven single-purpose functions: draw_grid, draw_cell, draw_snake, draw_game_over, is_opposite, random_food_position, reset_game. Swapping a theme or changing cell size touches exactly one function.

The visual polish is the highest in this benchmark:

BACKGROUND_COLOR = (18, 22, 28)
GRID_COLOR = (31, 37, 46)
SNAKE_HEAD_COLOR = (72, 211, 132)
SNAKE_BODY_COLOR = (50, 174, 109)
FOOD_COLOR = (238, 87, 87)
TEXT_COLOR = (240, 244, 248)

# 2px inset + 4px radius
pygame.draw.rect(screen, color, rect.inflate(-2, -2), border_radius=4)

Compared with the standard "pure black + pure green + pure red" combo most other models reached for, GPT-5.5's low-saturation deep palette plus mint accent plus rounded cells make the running game feel like a real iOS-grade mini game.

One mild miss: the initial snake is 1 segment long instead of 3, so it grows slowly. But the prompt did not specify a starting length, so this is not strictly a bug.

5. DeepSeek V4 Pro — the cleanest domestic delivery

Among models that run, DeepSeek's 123 lines is the shortest, and nothing important is missing. Its most interesting move is using a single state dict for the whole game:

state = {
    "snake": [(cx, cy), (cx - 1, cy), (cx - 2, cy)],
    "dir": pygame.K_RIGHT,
    "food": random_food([...]),
    "score": 0,
    "alive": True,
    "move_timer": 0,
}

This functional flavor is uncommon in pygame tutorials, but it keeps the code crisp — resetting the game is literally state = reset(). DeepSeek is also the only model in this set to do dt-based timing:

dt = clock.tick(15)
state["move_timer"] += dt
if state["alive"] and state["move_timer"] >= 100:
    state["move_timer"] = 0
    # move one step

Render at 15 fps, but step the snake at a 100ms tick. This is the production-grade pattern — render rate and game-tick rate decoupled. Claude and GPT both used the simpler clock.tick(FPS), which is fine for a small game but does not scale to varying difficulty or pause/resume.

Two demerits: DeepSeek does not mention environment setup at all, and the initial run does not call set_caption, so the window opens with the default "pygame window" title until the player eats the first food. Engineer-grade code, slightly UX-naive presentation.

6. Kimi 2.6 — localization intent, font footgun

Kimi is the only model that writes the UI in Chinese on its own initiative:

pygame.display.set_caption("贪吃蛇 - 分数: 0")
score_text = font.render(f"最终分数: {score}", True, WHITE)
restart_text = font.render("按 R 键重新开始", True, GRAY)

The instinct is good and the international labs would not have made this choice. But Kimi hard-codes a Windows-only font:

font = pygame.font.SysFont("simhei", 36)
game_over_font = pygame.font.SysFont("simhei", 48)

simhei ships with Windows but does not exist on macOS or most Linux distributions. When pygame cannot find the requested family, it falls back to a default font that does not cover CJK ranges — so every Chinese glyph renders as a tofu block. That is the garbled-text experience on macOS.

The cross-platform fix is straightforward:

def load_chinese_font(size):
    candidates = ["PingFang SC", "Heiti SC", "Microsoft YaHei",
                  "simhei", "WenQuanYi Zen Hei", "Arial Unicode MS"]
    for name in candidates:
        path = pygame.font.match_font(name)
        if path:
            return pygame.font.Font(path, size)
    return pygame.font.SysFont(None, size)

Kimi is still a strong long-context reader for Chinese material, but in "ship a self-contained piece of code" terms it is half a step short of done.

7. Zhipu GLM 5.1 — surprising WASD support, same font trap

The thing I did not expect from GLM 5.1 is it volunteers WASD support. The prompt only mentioned arrow keys, but it added a second control scheme:

if event.key in (pygame.K_UP, pygame.K_w) and direction != (0, 1):
    next_dir = (0, -1)
elif event.key in (pygame.K_DOWN, pygame.K_s) and direction != (0, -1):
    next_dir = (0, 1)
elif event.key in (pygame.K_LEFT, pygame.K_a) and direction != (1, 0):
    next_dir = (-1, 0)
elif event.key in (pygame.K_RIGHT, pygame.K_d) and direction != (-1, 0):
    next_dir = (1, 0)

That extra mile is the kind of "give me a little more than asked" behavior I look for in a coding model. Visually, GLM also adds rounded corners and inset cells, so the look is roughly on par with GPT-5.5.

However, GLM hits the same trap as Kimi — its Chinese Game Over text uses SysFont(None, 48), which falls back to a Latin-only default, and the on-screen labels become tofu blocks on macOS.

A minor structural complaint: GLM 5.1 puts the game loop at module top-level, without a main() function or if __name__ == "__main__": guard. It runs, but it is harder to reuse, test, or import. Refactoring is a five-minute job, but it still costs you that five minutes.

8. MiniMax 2.7 — the only outright failure in this round

MiniMax 2.7 is the only model that did not run cleanly on my machine in this benchmark. On paper its 135 lines look the most "software-engineering-y" — separate Snake and Food classes, OOP discipline. In practice it lands several puzzling design choices.

First, the snake and food disappear the moment the game ends:

if game_over:
    # render Game Over text
    ...
else:
    snake.draw()
    food.draw()

The other five models freeze the scene and overlay text on top, so the player understands what killed them. MiniMax wipes the playfield first — a direct violation of game UX common sense.

Second, it uses pixel coordinates instead of grid coordinates:

self.body = [(WIDTH // 2, HEIGHT // 2)]  # (300, 300) in pixels
self.direction = (CELL_SIZE, 0)           # (20, 0) in pixels

The other five all use (grid_x, grid_y) internally and multiply by CELL_SIZE only at render time. MiniMax's choice makes future work — variable speed, dynamic grids, level editors — much more painful. There is also a def grow(self): pass dead method that no caller invokes, which is dressing up the class without doing the work.

MiniMax remains strong on video and audio multimodal tasks, but on pure coding in May 2026 it is clearly the weakest of this six.

9. The common failure mode: nobody but Claude and GPT explains the install

Stacking all six side by side, the loudest pattern is that only Claude 4.7 and GPT-5.5 proactively tell you to install pygame. The other four assume you already have it.

Experienced developers do not care. New developers hit ModuleNotFoundError: No module named 'pygame' on a fresh machine and quietly give up. That single line of UX is the difference between "AI wrote me a working game" and "AI's code does not run".

The second shared problem is cross-platform font handling. Both Kimi and GLM wanted Chinese UI labels — the intent is correct — but both hard-coded a single font name and shipped a broken experience on non-Windows systems. There are two safe paths:

• Avoid: keep UI strings in English (Claude / GPT's implicit choice).
• Probe: use pygame.font.match_font() with a candidate list (PingFang / Heiti / YaHei / WenQuanYi) and pick the first one that resolves.

The Chinese labs' gap is less about "can it write the algorithm" and more about "does the delivery actually survive in someone else's environment". This is a subtler problem to fix because it shows up only when the model assumes too much about its caller.

10. Domestic vs international — the gap has shifted, not closed

It is easy to summarize this round as "did Chinese models catch up to GPT/Claude or not". I would rather split it:

• Algorithmic correctness: largely caught up. DeepSeek, Kimi, and GLM all produce valid game loops, collision logic, and event handling.
• Engineering deliverability: still a clear gap. Environment hints, cross-platform fonts, Game Over visuals, extensibility — international models lead.
• Price/throughput: domestic still wins for batch and high-volume use. DeepSeek's token price is a fraction of Claude or GPT.

In other words, as of May 2026 domestic models can write code, but international models still ship more complete deliveries. If you just need a working algorithm, domestic is enough. If you need code that a new teammate can clone, follow the instructions, and run successfully on day one, the international pair is more reliable.

For more complex tasks (front-end SaaS console), see my earlier head-to-head tests: Claude Code 4.7 front-end review and Codex/GPT-5.5 front-end review. For the API layer, I wrote OpenAI vs Claude API full comparison.

11. My current routing

• Production code / teaching / onboarding juniors: Claude Code 4.7. Environment hints and input buffering signal "engineering literacy".
• UI prototypes / SaaS consoles / front-end demos: GPT-5.5. Color palette and function granularity feel designer-tuned.
• High-volume API calls / large batches / budget-sensitive workloads: DeepSeek V4 Pro. Code quality is solid and token cost is a fraction.
• Long Chinese-language documents / reading-heavy work: Kimi 2.6 still has an edge, though raw coding is not its strongest surface.
• When I want a small unexpected gift (WASD, extra UX): GLM 5.1 — engineering polish is uneven but it has imagination.
• MiniMax 2.7: not in my default routing for coding tasks right now. I will retest after the next iteration.

Day to day I sanity-check AI-generated code with these utilities: JSON Formatter, Text Diff, Word Counter, Regex Tester.

FAQ