GLM-5 9B vs o3
GLM-5 9B (2026) and o3 (2025) are frontier-tier reasoning models from Zhipu AI and OpenAI. GLM-5 9B ships a 262k-token context window, while o3 ships a 200k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.
GLM-5 9B is safer overall; choose o3 when coding workflow support matters.
Decision scorecard
Local evidence first| Signal | GLM-5 9B | o3 |
|---|---|---|
| Best for | reasoning-heavy apps and tool-calling agents | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | RAG, Agents, and Long context | Coding, RAG, and Agents |
| Context window | 262k | 200k |
| Cheapest output | - | $8/1M tokens |
| Provider routes | 0 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GLM-5 9B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags GLM-5 9B for RAG, Agents, and Long context.
- o3 has broader tracked provider coverage for fallback and procurement flexibility.
- o3 uniquely exposes Vision, Multimodal, and Structured outputs in local model data.
- Local decision data tags o3 for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5 9B
Unavailable
No complete token price in local provider data
o3
$3,600
Cheapest tracked route/tier: OpenAI API
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 9B and o3; plan for SDK, billing, or endpoint changes.
- o3 adds Vision, Multimodal, and Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for o3 and GLM-5 9B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Vision, Multimodal, and Structured outputs before moving production traffic.
Specs
Pricing and availability
| Pricing attribute | GLM-5 9B | o3 |
|---|---|---|
| Input price | - | $2/1M tokens |
| Output price | - | $8/1M tokens |
| Providers | - |
Capabilities
| Capability | GLM-5 9B | o3 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | No | Yes |
| Code execution | No | Yes |
| 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: o3, multimodal input: o3, structured outputs: o3, and code execution: o3. Both models share reasoning mode, 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 9B has no token price sourced yet and o3 has $2/1M input tokens. Provider availability is 0 tracked routes versus 3. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GLM-5 9B when long-context analysis and larger context windows are central to the workload. Choose o3 when coding workflow support 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 has a larger context window, GLM-5 9B or o3?
GLM-5 9B supports 262k tokens, while o3 supports 200k 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 9B or o3 open source?
GLM-5 9B is listed under MIT. o3 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-5 9B or o3?
o3 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-5 9B or o3?
o3 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 reasoning mode, GLM-5 9B or o3?
Both GLM-5 9B 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.
Where can I run GLM-5 9B and o3?
GLM-5 9B is available on the tracked providers still being sourced. o3 is available on OpenAI API, OpenRouter, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-08. Data sourced from public model cards and provider documentation.