GPT-5.2 vs Qwen3 Max
GPT-5.2 is OpenAI's newest frontier model, built for agentic and long-context work with adaptive reasoning effort. Qwen3 Max is Alibaba's flagship text model, tuned for reasoning, instruction-following, and multilingual coverage. Both are text-focused workhorses, but they sit at opposite ends of the price-performance curve: GPT-5.2 costs more and benchmarks higher across the board, Qwen3 Max costs less and gives up rank in exchange.
Spec vs spec
| Spec | GPT-5.2 | Qwen3 Max |
|---|---|---|
| Context window | 400K | 262K |
| Max output | 128K | 33K |
| Input modalities | file, image, text | text |
| Output modalities | text | text |
| Knowledge cutoff | — | Jun 30, 2025 |
| Released | Dec 10, 2025 | Sep 23, 2025 |
| Reasoning | optional | optional |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
openai/gpt-5.2
Input · 1M tokens
$1.75 + 3%$1.80
Output · 1M tokens
$14.00 + 3%$14.42
Cache read · 1M tokens
$0.175 + 3%$0.180
FEE — FLAT, EVERY MODEL3%
qwen/qwen3-max
Input · 1M tokens
$0.780 + 3%$0.803
Output · 1M tokens
$3.90 + 3%$4.02
Cache read · 1M tokens
$0.156 + 3%$0.161
Cache write · 1M tokens
$0.975 + 3%$1.00
FEE — FLAT, EVERY MODEL3%
One workload, priced on both
10M input + 2M output tokens at each model's price, flat 3% fee included.
openai/gpt-5.2
$46.87
$45.50 provider + 3%
qwen/qwen3-maxCheaper
$16.07
$15.60 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| GPT-5.2 | Qwen3 Max | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| 3D | 1156 | #60 | 1150 | #62 |
| ASCII art | 1199 | #22 | 1175 | #32 |
| Code | 1219 | #42 | 1159 | #66 |
| Data viz | 1245 | #31 | 1149 | #64 |
| Game dev | 1260 | #29 | 1160 | #62 |
| SVG | 1197 | #31 | 1069 | #60 |
| UI components | 1243 | #32 | 1132 | #67 |
| Websites | 1237 | #34 | 1161 | #66 |
Head-to-head preference voting. How we filter and rank
Pricing math
GPT-5.2 costs $1.75/M input and $14.00/M output from the provider ($1.8025/M and $14.42/M on OpenKey after the flat 3% fee: $1.75 × 1.03 and $14.00 × 1.03). Qwen3 Max costs $0.78/M input and $3.90/M output ($0.8034/M and $4.017/M on OpenKey). Input tokens on GPT-5.2 run 2.24x the price of Qwen3 Max's; output tokens run about 3.6x higher ($14.00/M vs $3.90/M). On a concrete workload of 10M input + 2M output tokens, GPT-5.2 costs $45.50 and Qwen3 Max costs $15.60 — a $29.90 gap for that single run. If your workload is output-heavy (long generations, verbose completions), that gap widens fast because output is where GPT-5.2's premium is steepest.
Coding and agent benchmarks
On Design Arena's model-track categories, GPT-5.2 outranks Qwen3 Max everywhere both are scored: codecategories (rank 42 vs 66), dataviz (31 vs 64), gamedev (29 vs 62), uicomponent (32 vs 67), website (34 vs 66), asciiart (22 vs 32), svg (31 vs 60), and 3d (60 vs 62, the closest gap). GPT-5.2 also has agent-arena scores Qwen3 Max doesn't have listed at all — fullstack (rank 21), webapps (rank 19), mobileapps (rank 23), androidnative (rank 22), and godotgamedev (rank 13). If your use case is agentic coding, tool use, or app scaffolding, GPT-5.2 is the only one of the two with agent-track data to point to.
Context and output limits
GPT-5.2 supports a 400K token context window with a 128K max completion. Qwen3 Max supports 262,144 tokens context with a 32,768 max completion — GPT-5.2's context window is 1.53x larger. For long-document work — big codebases, long transcripts, multi-file review — GPT-5.2's headroom matters more the closer you get to Qwen3 Max's 262K ceiling. Qwen3 Max's smaller 32,768 max output also means it can't return as much text in one response, which matters if you're generating long reports or large code diffs in a single call.
Modality and parameter differences
GPT-5.2 accepts text, image, and file input and returns text — useful if your pipeline needs to read screenshots, PDFs, or other files directly. Qwen3 Max is text-in, text-out only. GPT-5.2 also exposes reasoning effort controls (xhigh, high, medium, low, none; default medium) so you can dial compute up or down per request. Qwen3 Max doesn't expose reasoning effort but supports logprobs, top_logprobs, presence_penalty, and temperature — parameters GPT-5.2 doesn't list — which matters if your app depends on sampling control or token-probability introspection rather than adaptive reasoning.
When to pick each
Use GPT-5.2 for agentic coding tasks, multi-file app generation, anything involving image or file input, or workloads where you need the largest context window available (400K tokens). Use Qwen3 Max for high-volume text generation, multilingual tasks, or any workload where the benchmark gap doesn't matter but the 3.6x output-price difference does — customer support automation, bulk summarization, or classification at scale. Both models are available on OpenKey with one API key and the same flat 3% fee on top of provider list price, so switching between them for A/B testing doesn't require separate accounts or billing setups.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| Agentic coding / app scaffolding | GPT-5.2 | Has agent-track Design Arena ranks (fullstack rank 21, webapps rank 19) that Qwen3 Max doesn't |
| High-volume text generation at scale | Qwen3 Max | $15.60 vs $45.50 on a 10M-in/2M-out workload |
| Long-document analysis (300K+ tokens) | GPT-5.2 | 400,000 token context vs 262,144, a 1.53x larger window |
| Image or file-based input pipelines | GPT-5.2 | Only one of the two that accepts image and file input; Qwen3 Max is text-only |
| Frontend / UI component generation | GPT-5.2 | Ranks 32 in uicomponent vs Qwen3 Max's rank 67 |
| Budget-constrained bulk output tasks | Qwen3 Max | Output tokens cost $3.90/M vs GPT-5.2's $14.00/M, roughly 3.6x cheaper |
Questions
- How much cheaper is Qwen3 Max than GPT-5.2?
- On a 10M input + 2M output token workload, Qwen3 Max costs $15.60 total versus $45.50 for GPT-5.2 — input tokens are 2.24x cheaper and output tokens run about 3.6x cheaper ($3.90/M vs $14.00/M) on Qwen3 Max.
- Which model has the larger context window?
- GPT-5.2 supports 400,000 tokens of context versus Qwen3 Max's 262,144 — a 1.53x difference. GPT-5.2 also allows a larger max completion (128,000 tokens vs 32,768), so it can return longer single responses.
- Does either model accept images?
- GPT-5.2 accepts text, image, and file input. Qwen3 Max is text-in, text-out only, so any workflow needing image or file understanding has to use GPT-5.2 or a different model entirely.
- Which model ranks higher for coding?
- GPT-5.2 outranks Qwen3 Max on every shared Design Arena model-track category, including codecategories (rank 42 vs rank 66) and dataviz (rank 31 vs rank 64). GPT-5.2 also has agent-track ranks like fullstack (rank 21) that Qwen3 Max isn't scored on at all.