GLM-5 9B vs Qwen3-235B-A22B
GLM-5 9B (2026) and Qwen3-235B-A22B (2025) are frontier reasoning models from Zhipu AI and Alibaba. GLM-5 9B ships a 262K-token context window, while Qwen3-235B-A22B 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.
GLM-5 9B is safer overall; choose Qwen3-235B-A22B when provider fit matters.
Decision scorecard
Local evidence first| Signal | GLM-5 9B | Qwen3-235B-A22B |
|---|---|---|
| Decision fit | RAG, Agents, and Long context | Coding, RAG, and Long context |
| Context window | 262K | 128K |
| Cheapest output | - | $1.2/1M tokens |
| Provider routes | 0 tracked | 4 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GLM-5 9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5 9B uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags GLM-5 9B for RAG, Agents, and Long context.
- Qwen3-235B-A22B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen3-235B-A22B uniquely exposes Structured outputs in local model data.
- 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.
GLM-5 9B
Unavailable
No complete token price in local provider data
Qwen3-235B-A22B
$620
Cheapest tracked route: AWS Bedrock
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for GLM-5 9B and Qwen3-235B-A22B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Qwen3-235B-A22B adds Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Qwen3-235B-A22B and GLM-5 9B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Structured outputs before moving production traffic.
- GLM-5 9B adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-15 | 2025-04-29 |
| Context window | 262K | 128K |
| Parameters | 9 | 235B |
| Architecture | decoder only | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | 2025-11 | - |
Pricing and availability
| Pricing attribute | GLM-5 9B | Qwen3-235B-A22B |
|---|---|---|
| Input price | - | $0.4/1M tokens |
| Output price | - | $1.2/1M tokens |
| Providers | - |
Capabilities
| Capability | GLM-5 9B | Qwen3-235B-A22B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on reasoning mode: GLM-5 9B, function calling: GLM-5 9B, tool use: GLM-5 9B, and structured outputs: Qwen3-235B-A22B. 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: GLM-5 9B has no token price sourced yet and Qwen3-235B-A22B has $0.4/1M input tokens. Provider availability is 0 tracked routes versus 4. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GLM-5 9B when reasoning depth and larger context windows are central to the workload. Choose Qwen3-235B-A22B when provider fit 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. 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, GLM-5 9B or Qwen3-235B-A22B?
GLM-5 9B supports 262K 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.
Is GLM-5 9B or Qwen3-235B-A22B open source?
GLM-5 9B is listed under Open Source. 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 reasoning mode, GLM-5 9B or Qwen3-235B-A22B?
GLM-5 9B has the clearer documented reasoning mode signal in this comparison. If reasoning mode 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, GLM-5 9B or Qwen3-235B-A22B?
GLM-5 9B 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, GLM-5 9B or Qwen3-235B-A22B?
GLM-5 9B 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.
Where can I run GLM-5 9B and Qwen3-235B-A22B?
GLM-5 9B is available on the tracked providers still being sourced. 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.