GLM-5V-Turbo vs Qwen2.5-72B-Instruct
GLM-5V-Turbo (2026) and Qwen2.5-72B-Instruct (2024) are frontier reasoning models from Zhipu AI and Alibaba. GLM-5V-Turbo ships a 200k-token context window, while Qwen2.5-72B-Instruct ships a 128k-token context window. On pricing, Qwen2.5-72B-Instruct costs $0.18/1M input tokens versus $1.20/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Qwen2.5-72B-Instruct is ~567% cheaper at $0.18/1M; pay for GLM-5V-Turbo only for reasoning depth.
Decision scorecard
Local evidence first| Signal | GLM-5V-Turbo | Qwen2.5-72B-Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | provider-routed production |
| Decision fit | RAG, Agents, and Long context | Coding, RAG, and Long context |
| Context window | 200k | 128k |
| Cheapest output | $4/1M tokens | $0.54/1M tokens |
| Provider routes | 2 tracked | 7 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 Vision, Multimodal, and Reasoning in local model data.
- Local decision data tags GLM-5V-Turbo for RAG, Agents, and Long context.
- Qwen2.5-72B-Instruct has the lower cheapest tracked output price at $0.54/1M tokens.
- Qwen2.5-72B-Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Qwen2.5-72B-Instruct for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5V-Turbo
$1,960
Cheapest tracked route/tier: OpenRouter
Qwen2.5-72B-Instruct
$279
Cheapest tracked route/tier: Chutes AI
Estimated monthly gap: $1,681. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Qwen2.5-72B-Instruct is $3.46/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision, Multimodal, and Reasoning before moving production traffic.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- GLM-5V-Turbo is $3.46/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GLM-5V-Turbo adds Vision, Multimodal, and Reasoning in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-01 | 2024-06-07 |
| Context window | 200k | 128k |
| Parameters | 744B total, 40B active | 72.7B |
| Architecture | mixture of experts | decoder only |
| License | MIT(OSI) | Apache 2.0(OSI) |
| Openness | Open source | Open source |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | 2025-11 | - |
Pricing and availability
| Pricing attribute | GLM-5V-Turbo | Qwen2.5-72B-Instruct |
|---|---|---|
| Input price | $1.20/1M tokens | $0.18/1M tokens |
| Output price | $4/1M tokens | $0.54/1M tokens |
| Providers |
Capabilities
| Capability | GLM-5V-Turbo | Qwen2.5-72B-Instruct |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on vision: GLM-5V-Turbo, multimodal input: GLM-5V-Turbo, reasoning mode: GLM-5V-Turbo, function calling: GLM-5V-Turbo, and tool use: GLM-5V-Turbo. 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-5V-Turbo lists $1.20/1M input and $4/1M output tokens on the cheapest tracked provider, while Qwen2.5-72B-Instruct lists $0.18/1M input and $0.54/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen2.5-72B-Instruct lower by about $1.75 per million blended tokens. Availability is 2 providers versus 7, so concentration risk also matters.
Choose GLM-5V-Turbo when reasoning depth and larger context windows are central to the workload. Choose Qwen2.5-72B-Instruct when provider fit, lower input-token cost, 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.
FAQ
Which has a larger context window, GLM-5V-Turbo or Qwen2.5-72B-Instruct?
GLM-5V-Turbo supports 200k tokens, while Qwen2.5-72B-Instruct 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 Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct is cheaper on tracked token pricing. GLM-5V-Turbo costs $1.20/1M input and $4/1M output tokens. Qwen2.5-72B-Instruct costs $0.18/1M input and $0.54/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5V-Turbo or Qwen2.5-72B-Instruct open source?
GLM-5V-Turbo is listed under MIT. Qwen2.5-72B-Instruct 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-5V-Turbo or Qwen2.5-72B-Instruct?
GLM-5V-Turbo 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-5V-Turbo or Qwen2.5-72B-Instruct?
GLM-5V-Turbo 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.
Where can I run GLM-5V-Turbo and Qwen2.5-72B-Instruct?
GLM-5V-Turbo is available on OpenRouter and Vercel AI Gateway. Qwen2.5-72B-Instruct is available on DeepInfra, OpenRouter, Fireworks AI, Novita AI, and Chutes AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.