GPT-5.3-Codex-Spark vs Qwen3.5-235B-A22B
GPT-5.3-Codex-Spark (2026) and Qwen3.5-235B-A22B (2026) are agentic coding models from OpenAI and Alibaba. GPT-5.3-Codex-Spark ships a 131K-token context window, while Qwen3.5-235B-A22B ships a 512k-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.
Qwen3.5-235B-A22B is safer overall; choose GPT-5.3-Codex-Spark when coding workflow support matters.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-12 | 2026-02-24 |
| Context window | 131K | 512k |
| Parameters | — | 235B |
| Architecture | decoder only | MoE |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | GPT-5.3-Codex-Spark | Qwen3.5-235B-A22B |
|---|---|---|
| Input price | - | - |
| Output price | - | - |
| Providers | - |
Pricing not yet sourced for either model.
Capabilities
| Capability | GPT-5.3-Codex-Spark | Qwen3.5-235B-A22B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | 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 function calling: GPT-5.3-Codex-Spark, tool use: GPT-5.3-Codex-Spark, structured outputs: GPT-5.3-Codex-Spark, and code execution: GPT-5.3-Codex-Spark. Both models share the core language-model surface, 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-Spark has no token price sourced yet and Qwen3.5-235B-A22B 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-Spark when coding workflow support and broader provider choice are central to the workload. Choose Qwen3.5-235B-A22B when long-context analysis and larger context windows 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
Which has a larger context window, GPT-5.3-Codex-Spark or Qwen3.5-235B-A22B?
Qwen3.5-235B-A22B supports 512k tokens, while GPT-5.3-Codex-Spark supports 131K 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.
Is GPT-5.3-Codex-Spark or Qwen3.5-235B-A22B open source?
GPT-5.3-Codex-Spark is listed under Proprietary. Qwen3.5-235B-A22B 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 function calling, GPT-5.3-Codex-Spark or Qwen3.5-235B-A22B?
GPT-5.3-Codex-Spark has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, GPT-5.3-Codex-Spark or Qwen3.5-235B-A22B?
GPT-5.3-Codex-Spark has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, GPT-5.3-Codex-Spark or Qwen3.5-235B-A22B?
GPT-5.3-Codex-Spark 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-Spark and Qwen3.5-235B-A22B?
GPT-5.3-Codex-Spark is available on OpenAI API. Qwen3.5-235B-A22B 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: 2026-05-10. Data sourced from public model cards and provider documentation.