GLM-4 32B vs Qwen3.5-9B
GLM-4 32B (2025) and Qwen3.5-9B (2026) are general-purpose language models from Tsinghua Knowledge Engineering Group (THUDM) and Alibaba. GLM-4 32B ships a not-yet-sourced context window, while Qwen3.5-9B ships a 262K-token context window. On pricing, GLM-4 32B costs $0.1/1M input tokens versus $0.1/1M 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.
Qwen3.5-9B is safer overall; choose GLM-4 32B when provider fit matters.
Decision scorecard
Local evidence first| Signal | GLM-4 32B | Qwen3.5-9B |
|---|---|---|
| Decision fit | Classification and JSON / Tool use | RAG, Agents, and Long context |
| Context window | — | 262K |
| Cheapest output | $0.1/1M tokens | $0.15/1M tokens |
| Provider routes | 1 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GLM-4 32B has the lower cheapest tracked output price at $0.1/1M tokens.
- Local decision data tags GLM-4 32B for Classification and JSON / Tool use.
- Qwen3.5-9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-9B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen3.5-9B uniquely exposes Vision, Multimodal, and Function calling in local model data.
- Local decision data tags Qwen3.5-9B for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
GLM-4 32B
$105
Cheapest tracked route: OpenRouter
Qwen3.5-9B
$118
Cheapest tracked route: Together AI
Estimated monthly gap: $12.50. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Qwen3.5-9B is $0.05/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Qwen3.5-9B adds Vision, Multimodal, and Function calling in local capability data.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- GLM-4 32B is $0.05/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Function calling before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-03-05 | 2026-03-02 |
| Context window | — | 262K |
| Parameters | 32B | 9B |
| Architecture | - | decoder only |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | GLM-4 32B | Qwen3.5-9B |
|---|---|---|
| Input price | $0.1/1M tokens | $0.1/1M tokens |
| Output price | $0.1/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | GLM-4 32B | Qwen3.5-9B |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: Qwen3.5-9B, multimodal input: Qwen3.5-9B, function calling: Qwen3.5-9B, and tool use: Qwen3.5-9B. 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-4 32B lists $0.1/1M input and $0.1/1M output tokens, while Qwen3.5-9B lists $0.1/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GLM-4 32B lower by about $0.01 per million blended tokens. Availability is 1 providers versus 3, so concentration risk also matters.
Choose GLM-4 32B when provider fit are central to the workload. Choose Qwen3.5-9B when vision-heavy evaluation 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 is cheaper, GLM-4 32B or Qwen3.5-9B?
GLM-4 32B is cheaper on tracked token pricing. GLM-4 32B costs $0.1/1M input and $0.1/1M output tokens. Qwen3.5-9B costs $0.1/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-4 32B or Qwen3.5-9B open source?
GLM-4 32B is listed under Apache 2.0. Qwen3.5-9B 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 vision, GLM-4 32B or Qwen3.5-9B?
Qwen3.5-9B 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, GLM-4 32B or Qwen3.5-9B?
Qwen3.5-9B 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.
Which is better for function calling, GLM-4 32B or Qwen3.5-9B?
Qwen3.5-9B 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.
Where can I run GLM-4 32B and Qwen3.5-9B?
GLM-4 32B is available on OpenRouter. Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. 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: 2026-05-14. Data sourced from public model cards and provider documentation.