GLM-5 Turbo vs o3
GLM-5 Turbo (2026) and o3 (2025) are frontier-tier reasoning models from Zhipu AI and OpenAI. GLM-5 Turbo ships a 200k-token context window, while o3 ships a 128K-token context window. On pricing, o3 costs $1/1M 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.
GLM-5 Turbo is safer overall; choose o3 when coding workflow support matters.
Specs
| Released | 2026-03-01 | 2025-03-31 |
| Context window | 200k | 128K |
| Parameters | 744B total, 40B active | — |
| Architecture | mixture of experts | decoder only |
| License | Proprietary | Unknown |
| Knowledge cutoff | - | - |
Pricing and availability
| GLM-5 Turbo | o3 | |
|---|---|---|
| Input price | $1.2/1M tokens | $1/1M tokens |
| Output price | $4/1M tokens | $4/1M tokens |
| Providers |
Capabilities
| GLM-5 Turbo | o3 | |
|---|---|---|
| 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 function calling: GLM-5 Turbo, tool use: GLM-5 Turbo, and code execution: o3. Both models share reasoning mode and 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 Turbo lists $1.2/1M input and $4/1M output tokens, while o3 lists $1/1M input and $4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts o3 lower by about $0.14 per million blended tokens. Availability is 1 providers versus 3, so concentration risk also matters.
Choose GLM-5 Turbo when long-context analysis and larger context windows are central to the workload. Choose o3 when coding workflow support, 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-5 Turbo or o3?
GLM-5 Turbo supports 200k tokens, while o3 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-5 Turbo or o3?
o3 is cheaper on tracked token pricing. GLM-5 Turbo costs $1.2/1M input and $4/1M output tokens. o3 costs $1/1M input and $4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5 Turbo or o3 open source?
GLM-5 Turbo is listed under Proprietary. o3 is listed under Unknown. 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 o3?
Both GLM-5 Turbo and o3 expose reasoning mode. 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 function calling, GLM-5 Turbo or o3?
GLM-5 Turbo 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 Turbo and o3?
GLM-5 Turbo is available on OpenRouter. o3 is available on OpenAI API, OpenRouter, and OpenAI Batch API. 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: 2026-04-24. Data sourced from public model cards and provider documentation.