GPT-5.3-Codex vs Qwen3-Max
GPT-5.3-Codex (2026) and Qwen3-Max (2026) are agentic coding models from OpenAI and Alibaba. GPT-5.3-Codex ships a not-yet-sourced context window, while Qwen3-Max ships a 128K-token context window. On SWE-bench Verified, GPT-5.3-Codex leads by 6.2 pts. On pricing, Qwen3-Max costs $0.78/1M input tokens versus $1.75/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3-Max is ~124% cheaper at $0.78/1M; pay for GPT-5.3-Codex only for coding workflow support.
Specs
| Released | 2026-02-05 | 2026-01-15 |
| Context window | — | 128K |
| Parameters | — | — |
| Architecture | decoder only | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | - | 2025-12 |
Pricing and availability
| GPT-5.3-Codex | Qwen3-Max | |
|---|---|---|
| Input price | $1.75/1M tokens | $0.78/1M tokens |
| Output price | $14/1M tokens | $3.9/1M tokens |
| Providers |
Capabilities
| GPT-5.3-Codex | Qwen3-Max | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | GPT-5.3-Codex | Qwen3-Max |
|---|---|---|
| SWE-bench Verified | 85.0 | 78.8 |
| τ-bench | 77.8 | 76.8 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has GPT-5.3-Codex at 85 and Qwen3-Max at 78.8, with GPT-5.3-Codex ahead by 6.2 points; τ-bench has GPT-5.3-Codex at 77.8 and Qwen3-Max at 76.8, with GPT-5.3-Codex ahead by 1 points. The largest visible gap is 6.2 points on SWE-bench Verified, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on vision: Qwen3-Max and code execution: GPT-5.3-Codex. Both models share multimodal input, function calling, tool use, and structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, GPT-5.3-Codex lists $1.75/1M input and $14/1M output tokens, while Qwen3-Max lists $0.78/1M input and $3.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3-Max lower by about $3.71 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.
Choose GPT-5.3-Codex when coding workflow support are central to the workload. Choose Qwen3-Max when vision-heavy evaluation and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which is cheaper, GPT-5.3-Codex or Qwen3-Max?
Qwen3-Max is cheaper on tracked token pricing. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. Qwen3-Max costs $0.78/1M input and $3.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.3-Codex or Qwen3-Max open source?
GPT-5.3-Codex is listed under Proprietary. Qwen3-Max is listed under Proprietary. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, GPT-5.3-Codex or Qwen3-Max?
Qwen3-Max has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, GPT-5.3-Codex or Qwen3-Max?
Both GPT-5.3-Codex and Qwen3-Max expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for function calling, GPT-5.3-Codex or Qwen3-Max?
Both GPT-5.3-Codex and Qwen3-Max expose function calling. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run GPT-5.3-Codex and Qwen3-Max?
GPT-5.3-Codex is available on OpenRouter. Qwen3-Max is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.