GPT-5.2-Codex vs Qwen3 Coder 480B A35B

Pricing math on a real workload

On OpenKey, provider prices get a flat 3% fee added. GPT-5.2-Codex: $1.75/M input becomes $1.8025/M, $14.00/M output becomes $14.42/M. Qwen3 Coder: $0.22/M input becomes $0.2266/M, $1.80/M output becomes $1.854/M.

For a 10M-input / 2M-output workload, GPT-5.2-Codex costs **$45.50** and Qwen3 Coder costs **$5.80** — GPT-5.2-Codex is roughly 7.95x more expensive on input pricing alone. If you're running high-volume batch coding jobs (linting, test generation, refactors across a large repo), that gap compounds fast. If you're running a handful of high-stakes agentic sessions per day, the absolute dollar difference is small enough that quality wins.

Coding and agent benchmarks

The two models were benchmarked on different Design Arena categories, so this isn't a direct head-to-head, but the numbers tell you where each one is strong. GPT-5.2-Codex's best agent-arena results: Godot game dev (elo 1187, rank 12, 48% win rate) and Android native (elo 1176, rank 15, 47.5% win rate) — its weakest is fullstack (elo 1060, rank 27, 37% win rate).

Qwen3 Coder's model-arena results run higher across the board: website (elo 1201, rank 55, 61.7% win rate), UI component (elo 1170, rank 56, 61.5% win rate), and code categories (elo 1192, rank 54, 61.2% win rate). Note these are separate arenas (agents vs models), so treat rank numbers as context, not a direct ladder comparison.

Context and long-document work

Qwen3 Coder's 1,048,576-token context window is about 2.6x GPT-5.2-Codex's 400,000 (context ratio 0.38 favoring Qwen3 Coder). If your job is ingesting a large monorepo, a big dependency graph, or long log files in one pass, Qwen3 Coder's window gives you more room before you need chunking or retrieval. GPT-5.2-Codex tops out at 128,000 max completion tokens vs Qwen3 Coder's 65,536, so for very long single-response output GPT-5.2-Codex has the edge, even though its total context is smaller.

Modality and tool support

GPT-5.2-Codex accepts text and image input; Qwen3 Coder is text-only. If your workflow involves screenshots, diagrams, or UI mockups as input, GPT-5.2-Codex is the only option here. GPT-5.2-Codex also runs mandatory reasoning (efforts: xhigh, high, medium, low; default medium) with parameters like `include_reasoning` and `reasoning` exposed — useful for tasks that benefit from visible chain-of-thought tuning. Qwen3 Coder has no reasoning mode but exposes a wider sampling parameter set (`top_k`, `min_p`, `repetition_penalty`, `logit_bias`, `logprobs`), which matters if you're doing fine-grained sampling control or classic completion-style tuning rather than agentic tool loops.

When to pick each

Go with GPT-5.2-Codex for interactive coding sessions where accuracy on Android/mobile/game-dev tasks matters more than cost, or when you need image input alongside code. Go with Qwen3 Coder for high-volume agentic pipelines, long-context codebase analysis, or any workload where the 7.95x input price gap actually shows up on your bill. Cost-sensitive teams running thousands of calls a day should default to Qwen3 Coder and only reach for GPT-5.2-Codex on tasks where its benchmark categories show a clear edge.

Questions

How much cheaper is Qwen3 Coder than GPT-5.2-Codex?

On input tokens, Qwen3 Coder is about 7.95x cheaper ($0.2266/M vs $1.8025/M on OpenKey, both provider price plus the flat 3% fee). On a 10M-input/2M-output workload, that's $5.80 total for Qwen3 Coder versus $45.50 for GPT-5.2-Codex.

Which model has a bigger context window?

Qwen3 Coder handles 1,048,576 tokens of context, versus GPT-5.2-Codex's 400,000 — a context ratio of 0.38 in Qwen3 Coder's favor. If you're feeding in a full repo or long logs, Qwen3 Coder gives you more headroom before you need to chunk input.

Does either model accept image input?

GPT-5.2-Codex does — it supports text and image input modalities. Qwen3 Coder is text-only in both input and output. If your pipeline needs to read screenshots or UI mockups, GPT-5.2-Codex is the only choice of the two.

Which model wins on coding benchmarks?

They were tested in different Design Arena arenas, so it's not a direct match. GPT-5.2-Codex's best agent-arena score is Godot game dev at elo 1187 (rank 12, 48% win rate). Qwen3 Coder's best model-arena score is website at elo 1201 (rank 55, 61.7% win rate) — higher elo, but a different competitive pool.