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GPT-5.3-Codex vs Qwen2.5-72B

GPT-5.3-Codex (2026) and Qwen2.5-72B (2025) are agentic coding models from OpenAI and Alibaba. GPT-5.3-Codex ships a not-yet-sourced context window, while Qwen2.5-72B ships a 128k-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing. The goal is to make the tradeoff clear before deeper testing.

GPT-5.3-Codex is safer overall; choose Qwen2.5-72B when provider fit matters.

Specs

Released2026-02-052025-10-10
Context window128k
Parameters72B
Architecturedecoder only-
LicenseProprietaryOpen Source
Knowledge cutoff-2024-09

Pricing and availability

GPT-5.3-CodexQwen2.5-72B
Input price$1.75/1M tokens-
Output price$14/1M tokens-
Providers-

Capabilities

GPT-5.3-CodexQwen2.5-72B
Vision
Multimodal
Reasoning
Function calling
Tool use
Structured outputs
Code execution

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on multimodal input: GPT-5.3-Codex, structured outputs: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. Both models share function calling and tool use, 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.

Pricing coverage is uneven: GPT-5.3-Codex has $1.75/1M input tokens and Qwen2.5-72B has no token price sourced yet. Provider availability is 1 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.

Choose GPT-5.3-Codex when coding workflow support and broader provider choice are central to the workload. Choose Qwen2.5-72B when provider fit are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.

FAQ

Is GPT-5.3-Codex or Qwen2.5-72B open source?

GPT-5.3-Codex is listed under Proprietary. Qwen2.5-72B is listed under Open Source. 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 multimodal input, GPT-5.3-Codex or Qwen2.5-72B?

GPT-5.3-Codex has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Which is better for function calling, GPT-5.3-Codex or Qwen2.5-72B?

Both GPT-5.3-Codex and Qwen2.5-72B 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.

Which is better for tool use, GPT-5.3-Codex or Qwen2.5-72B?

Both GPT-5.3-Codex and Qwen2.5-72B expose tool use. 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 structured outputs, GPT-5.3-Codex or Qwen2.5-72B?

GPT-5.3-Codex has the clearer documented structured outputs signal in this comparison. If structured outputs is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.

Where can I run GPT-5.3-Codex and Qwen2.5-72B?

GPT-5.3-Codex is available on OpenRouter. Qwen2.5-72B is available on the tracked providers still being sourced. 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.