GLM-5 Turbo vs Qwen3-105B
GLM-5 Turbo (2026) and Qwen3-105B (2025) are frontier reasoning models from Zhipu AI and Alibaba. GLM-5 Turbo ships a 200k-token context window, while Qwen3-105B 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 Turbo is safer overall; choose Qwen3-105B when provider fit matters.
Decision scorecard
Local evidence first| Signal | GLM-5 Turbo | Qwen3-105B |
|---|---|---|
| Decision fit | RAG, Agents, and Long context | RAG, Agents, and Long context |
| Context window | 200k | 128k |
| Cheapest output | $4/1M tokens | - |
| Provider routes | 1 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GLM-5 Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5 Turbo has broader tracked provider coverage for fallback and procurement flexibility.
- GLM-5 Turbo uniquely exposes Reasoning and Structured outputs in local model data.
- Local decision data tags GLM-5 Turbo for RAG, Agents, and Long context.
- Local decision data tags Qwen3-105B 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-5 Turbo
$1,960
Cheapest tracked route: OpenRouter
Qwen3-105B
Unavailable
No complete token price in local provider data
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 Turbo and Qwen3-105B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning and Structured outputs before moving production traffic.
- No overlapping tracked provider route is sourced for Qwen3-105B and GLM-5 Turbo; plan for SDK, billing, or endpoint changes.
- GLM-5 Turbo adds Reasoning and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-01 | 2025-12-15 |
| Context window | 200k | 128k |
| Parameters | 744B total, 40B active | 105B |
| Architecture | mixture of experts | - |
| License | Proprietary | Open Source |
| Knowledge cutoff | 2025-11 | 2025-02 |
Pricing and availability
| Pricing attribute | GLM-5 Turbo | Qwen3-105B |
|---|---|---|
| Input price | $1.2/1M tokens | - |
| Output price | $4/1M tokens | - |
| Providers | - |
Capabilities
| Capability | GLM-5 Turbo | Qwen3-105B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | No |
| 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 Turbo and structured outputs: GLM-5 Turbo. Both models share function calling and tool use, 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 Turbo has $1.2/1M input tokens and Qwen3-105B 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 GLM-5 Turbo when reasoning depth, larger context windows, and broader provider choice are central to the workload. Choose Qwen3-105B when provider fit 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 Turbo or Qwen3-105B?
GLM-5 Turbo supports 200k tokens, while Qwen3-105B 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 Turbo or Qwen3-105B open source?
GLM-5 Turbo is listed under Proprietary. Qwen3-105B is listed under Open Source. 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 Turbo or Qwen3-105B?
GLM-5 Turbo 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 Turbo or Qwen3-105B?
Both GLM-5 Turbo and Qwen3-105B expose function calling. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for tool use, GLM-5 Turbo or Qwen3-105B?
Both GLM-5 Turbo and Qwen3-105B expose tool use. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run GLM-5 Turbo and Qwen3-105B?
GLM-5 Turbo is available on OpenRouter. Qwen3-105B 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: 2026-05-11. Data sourced from public model cards and provider documentation.