LLM Reference

GPT-5.2 vs Qwen3-235B-A22B

GPT-5.2 (2025) and Qwen3-235B-A22B (2025) are frontier reasoning models from OpenAI and Alibaba. GPT-5.2 ships a 400K-token context window, while Qwen3-235B-A22B ships a 128K-token context window. On pricing, Qwen3-235B-A22B costs $0.4/1M 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.

Qwen3-235B-A22B is ~338% cheaper at $0.4/1M; pay for GPT-5.2 only for coding workflow support.

Decision scorecard

Local evidence first
SignalGPT-5.2Qwen3-235B-A22B
Decision fitCoding, RAG, and AgentsCoding, RAG, and Long context
Context window400K128K
Cheapest output$14/1M tokens$1.2/1M tokens
Provider routes2 tracked4 tracked
Shared benchmarks0 rows0 rows

Decision tradeoffs

Choose GPT-5.2 when...
  • GPT-5.2 has the larger context window for long prompts, retrieval packs, or transcript analysis.
  • GPT-5.2 uniquely exposes Vision, Multimodal, and Reasoning in local model data.
  • Local decision data tags GPT-5.2 for Coding, RAG, and Agents.
Choose Qwen3-235B-A22B when...
  • Qwen3-235B-A22B has the lower cheapest tracked output price at $1.2/1M tokens.
  • Qwen3-235B-A22B has broader tracked provider coverage for fallback and procurement flexibility.
  • Local decision data tags Qwen3-235B-A22B for Coding, RAG, and Long context.

Monthly cost at traffic

Estimate token spend from the cheapest tracked input and output prices on this page.

Lower estimate Qwen3-235B-A22B

GPT-5.2

$4,900

Cheapest tracked route: Replicate API

Qwen3-235B-A22B

$620

Cheapest tracked route: AWS Bedrock

Estimated monthly gap: $4,280. Batch, cache, and negotiated pricing are excluded from this local estimate.

Switch friction

GPT-5.2 -> Qwen3-235B-A22B
  • Provider overlap exists on OpenRouter; start route-level A/B tests there.
  • Qwen3-235B-A22B is $12.80/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
  • Check replacement coverage for Vision, Multimodal, and Reasoning before moving production traffic.
Qwen3-235B-A22B -> GPT-5.2
  • Provider overlap exists on OpenRouter; start route-level A/B tests there.
  • GPT-5.2 is $12.80/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
  • GPT-5.2 adds Vision, Multimodal, and Reasoning in local capability data.

Specs

Specification
Released2025-12-112025-04-29
Context window400K128K
Parameters235B
Architecturedecoder onlydecoder only
LicenseProprietaryApache 2.0
Knowledge cutoff2025-08-

Pricing and availability

Pricing attributeGPT-5.2Qwen3-235B-A22B
Input price$1.75/1M tokens$0.4/1M tokens
Output price$14/1M tokens$1.2/1M tokens
Providers

Capabilities

CapabilityGPT-5.2Qwen3-235B-A22B
VisionYesNo
MultimodalYesNo
ReasoningYesNo
Function callingYesNo
Tool useYesNo
Structured outputsYesYes
Code executionYesNo

Benchmarks

No shared benchmark rows are currently sourced for this pair.

Deep dive

The capability footprint differs most on vision: GPT-5.2, multimodal input: GPT-5.2, reasoning mode: GPT-5.2, function calling: GPT-5.2, tool use: GPT-5.2, and code execution: GPT-5.2. Both models share 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-5.2 lists $1.75/1M input and $14/1M output tokens, while Qwen3-235B-A22B lists $0.4/1M input and $1.2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3-235B-A22B lower by about $4.79 per million blended tokens. Availability is 2 providers versus 4, so concentration risk also matters.

Choose GPT-5.2 when coding workflow support and larger context windows are central to the workload. Choose Qwen3-235B-A22B when provider fit, lower input-token cost, and broader provider choice 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-5.2 or Qwen3-235B-A22B?

GPT-5.2 supports 400K tokens, while Qwen3-235B-A22B supports 128K 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-5.2 or Qwen3-235B-A22B?

Qwen3-235B-A22B is cheaper on tracked token pricing. GPT-5.2 costs $1.75/1M input and $14/1M output tokens. Qwen3-235B-A22B costs $0.4/1M input and $1.2/1M output tokens. Provider discounts or batch pricing can still change the final bill.

Is GPT-5.2 or Qwen3-235B-A22B open source?

GPT-5.2 is listed under Proprietary. Qwen3-235B-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 vision, GPT-5.2 or Qwen3-235B-A22B?

GPT-5.2 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-5.2 or Qwen3-235B-A22B?

GPT-5.2 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-5.2 and Qwen3-235B-A22B?

GPT-5.2 is available on Replicate API and OpenRouter. Qwen3-235B-A22B is available on Fireworks AI, AWS Bedrock, OpenRouter, and Venice AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.

Continue comparing

Last reviewed: 2026-05-20. Data sourced from public model cards and provider documentation.