GLM-5V-Turbo vs Qwen3-Max
GLM-5V-Turbo (2026) and Qwen3-Max (2026) are frontier reasoning models from Zhipu AI and Alibaba. GLM-5V-Turbo ships a 200k-token context window, while Qwen3-Max ships a 128K-token context window. On pricing, Qwen3-Max costs $0.78/1M input tokens versus $1.2/1M 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-Max is ~54% cheaper at $0.78/1M; pay for GLM-5V-Turbo only for reasoning depth.
Decision scorecard
Local evidence first| Signal | GLM-5V-Turbo | Qwen3-Max |
|---|---|---|
| Decision fit | RAG, Agents, and Long context | Coding, RAG, and Agents |
| Context window | 200k | 128K |
| Cheapest output | $4/1M tokens | $3.9/1M tokens |
| Provider routes | 1 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GLM-5V-Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5V-Turbo uniquely exposes Reasoning in local model data.
- Local decision data tags GLM-5V-Turbo for RAG, Agents, and Long context.
- Qwen3-Max has the lower cheapest tracked output price at $3.9/1M tokens.
- Local decision data tags Qwen3-Max for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
GLM-5V-Turbo
$1,960
Cheapest tracked route: OpenRouter
Qwen3-Max
$1,599
Cheapest tracked route: OpenRouter
Estimated monthly gap: $361. 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-Max is $0.1/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning before moving production traffic.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- GLM-5V-Turbo is $0.1/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GLM-5V-Turbo adds Reasoning in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-01 | 2026-01-15 |
| Context window | 200k | 128K |
| Parameters | 744B total, 40B active | — |
| Architecture | mixture of experts | decoder only |
| License | Proprietary | Proprietary |
| Knowledge cutoff | 2025-11 | 2025-12 |
Pricing and availability
| Pricing attribute | GLM-5V-Turbo | Qwen3-Max |
|---|---|---|
| Input price | $1.2/1M tokens | $0.78/1M tokens |
| Output price | $4/1M tokens | $3.9/1M tokens |
| Providers |
Capabilities
| Capability | GLM-5V-Turbo | Qwen3-Max |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | Yes |
| Tool use | Yes | 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 reasoning mode: GLM-5V-Turbo. Both models share vision, multimodal input, 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.
For cost, GLM-5V-Turbo lists $1.2/1M input and $4/1M output tokens, while Qwen3-Max lists $0.78/1M input and $3.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3-Max lower by about $0.32 per million blended tokens. Availability is 1 providers versus 1, so concentration risk also matters.
Choose GLM-5V-Turbo when reasoning depth and larger context windows are central to the workload. Choose Qwen3-Max when vision-heavy evaluation 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-5V-Turbo or Qwen3-Max?
GLM-5V-Turbo supports 200k tokens, while Qwen3-Max 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, GLM-5V-Turbo or Qwen3-Max?
Qwen3-Max is cheaper on tracked token pricing. GLM-5V-Turbo costs $1.2/1M input and $4/1M output tokens. Qwen3-Max costs $0.78/1M input and $3.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5V-Turbo or Qwen3-Max open source?
GLM-5V-Turbo is listed under Proprietary. Qwen3-Max is listed under Proprietary. 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-5V-Turbo or Qwen3-Max?
Both GLM-5V-Turbo and Qwen3-Max expose vision. 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 multimodal input, GLM-5V-Turbo or Qwen3-Max?
Both GLM-5V-Turbo and Qwen3-Max expose multimodal input. 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-5V-Turbo and Qwen3-Max?
GLM-5V-Turbo is available on OpenRouter. Qwen3-Max is available on OpenRouter. 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.