GLM-5 vs Qwen3-9B
GLM-5 (2026) and Qwen3-9B (2026) are frontier reasoning models from Zhipu AI and Alibaba. GLM-5 ships a 200k-token context window, while Qwen3-9B ships a 256K-token context window. On pricing, Qwen3-9B costs $0.04/1M input tokens versus $0.72/1M 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.
Qwen3-9B is ~1700% cheaper at $0.04/1M; pay for GLM-5 only for reasoning depth.
Specs
Pricing and availability
| GLM-5 | Qwen3-9B | |
|---|---|---|
| Input price | $0.72/1M tokens | $0.04/1M tokens |
| Output price | $2.3/1M tokens | $0.2/1M tokens |
| Providers |
Capabilities
| GLM-5 | Qwen3-9B | |
|---|---|---|
| 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 reasoning mode: GLM-5, function calling: GLM-5, and tool use: GLM-5. 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, GLM-5 lists $0.72/1M input and $2.3/1M output tokens, while Qwen3-9B lists $0.04/1M input and $0.2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3-9B lower by about $1.11 per million blended tokens. Availability is 5 providers versus 1, so concentration risk also matters.
Choose GLM-5 when reasoning depth and broader provider choice are central to the workload. Choose Qwen3-9B when long-context analysis, larger context windows, 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. 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 or Qwen3-9B?
Qwen3-9B supports 256K tokens, while GLM-5 supports 200k 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, GLM-5 or Qwen3-9B?
Qwen3-9B is cheaper on tracked token pricing. GLM-5 costs $0.72/1M input and $2.3/1M output tokens. Qwen3-9B costs $0.04/1M input and $0.2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5 or Qwen3-9B open source?
GLM-5 is listed under MIT. Qwen3-9B 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 or Qwen3-9B?
GLM-5 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 or Qwen3-9B?
GLM-5 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.
Where can I run GLM-5 and Qwen3-9B?
GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Qwen3-9B is available on DeepInfra. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.