GPT-5.3-Codex vs Qwen2-VL-72B-Instruct
GPT-5.3-Codex (2026) and Qwen2-VL-72B-Instruct (2025) are agentic coding models from OpenAI and Alibaba. GPT-5.3-Codex ships a 400K-token context window, while Qwen2-VL-72B-Instruct ships a 32K-token context window. On pricing, Qwen2-VL-72B-Instruct costs $0.9/1M 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.
Qwen2-VL-72B-Instruct is ~94% cheaper at $0.9/1M; pay for GPT-5.3-Codex only for coding workflow support.
Decision scorecard
Local evidence first| Signal | GPT-5.3-Codex | Qwen2-VL-72B-Instruct |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | Vision |
| Context window | 400K | 32K |
| Cheapest output | $14/1M tokens | $0.9/1M tokens |
| Provider routes | 2 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GPT-5.3-Codex has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GPT-5.3-Codex has broader tracked provider coverage for fallback and procurement flexibility.
- GPT-5.3-Codex uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags GPT-5.3-Codex for Coding, RAG, and Agents.
- Qwen2-VL-72B-Instruct has the lower cheapest tracked output price at $0.9/1M tokens.
- Qwen2-VL-72B-Instruct uniquely exposes Multimodal in local model data.
- Local decision data tags Qwen2-VL-72B-Instruct for Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
GPT-5.3-Codex
$4,900
Cheapest tracked route: OpenRouter
Qwen2-VL-72B-Instruct
$945
Cheapest tracked route: Fireworks AI
Estimated monthly gap: $3,955. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for GPT-5.3-Codex and Qwen2-VL-72B-Instruct; plan for SDK, billing, or endpoint changes.
- Qwen2-VL-72B-Instruct is $13.10/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Qwen2-VL-72B-Instruct adds Multimodal in local capability data.
- No overlapping tracked provider route is sourced for Qwen2-VL-72B-Instruct and GPT-5.3-Codex; plan for SDK, billing, or endpoint changes.
- GPT-5.3-Codex is $13.10/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Multimodal before moving production traffic.
- GPT-5.3-Codex adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-05 | 2025-01-01 |
| Context window | 400K | 32K |
| Parameters | — | 72B |
| Architecture | decoder only | decoder only |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | 2025-08 | - |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex | Qwen2-VL-72B-Instruct |
|---|---|---|
| Input price | $1.75/1M tokens | $0.9/1M tokens |
| Output price | $14/1M tokens | $0.9/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.3-Codex | Qwen2-VL-72B-Instruct |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | No |
| Code execution | Yes | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on multimodal input: Qwen2-VL-72B-Instruct, reasoning mode: GPT-5.3-Codex, function calling: GPT-5.3-Codex, tool use: GPT-5.3-Codex, structured outputs: GPT-5.3-Codex, and code execution: GPT-5.3-Codex. Both models share vision, 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 Qwen2-VL-72B-Instruct lists $0.9/1M input and $0.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2-VL-72B-Instruct lower by about $4.52 per million blended tokens. Availability is 2 providers versus 1, so concentration risk also matters.
Choose GPT-5.3-Codex when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Qwen2-VL-72B-Instruct 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. 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.
FAQ
Which has a larger context window, GPT-5.3-Codex or Qwen2-VL-72B-Instruct?
GPT-5.3-Codex supports 400K tokens, while Qwen2-VL-72B-Instruct supports 32K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, GPT-5.3-Codex or Qwen2-VL-72B-Instruct?
Qwen2-VL-72B-Instruct is cheaper on tracked token pricing. GPT-5.3-Codex costs $1.75/1M input and $14/1M output tokens. Qwen2-VL-72B-Instruct costs $0.9/1M input and $0.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GPT-5.3-Codex or Qwen2-VL-72B-Instruct open source?
GPT-5.3-Codex is listed under Proprietary. Qwen2-VL-72B-Instruct is listed under Apache 2.0. 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 Qwen2-VL-72B-Instruct?
Both GPT-5.3-Codex and Qwen2-VL-72B-Instruct expose vision. 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 multimodal input, GPT-5.3-Codex or Qwen2-VL-72B-Instruct?
Qwen2-VL-72B-Instruct 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.
Where can I run GPT-5.3-Codex and Qwen2-VL-72B-Instruct?
GPT-5.3-Codex is available on OpenRouter and OpenAI API. Qwen2-VL-72B-Instruct is available on Fireworks AI. 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: 2026-05-11. Data sourced from public model cards and provider documentation.