GLM-5 9B vs Kimi K2.5
GLM-5 9B (2026) and Kimi K2.5 (2026) are agentic coding models from Zhipu AI and Moonshot AI. GLM-5 9B ships a 262K-token context window, while Kimi K2.5 ships a 256K-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.
Kimi K2.5 is safer overall; choose GLM-5 9B when reasoning depth matters.
Specs
| Released | 2026-02-15 | 2026-03-15 |
| Context window | 262K | 256K |
| Parameters | 9 | 1T (MoE, 384 experts) |
| Architecture | decoder only | mixture of experts |
| License | Open Source | MIT |
| Knowledge cutoff | - | - |
Pricing and availability
| GLM-5 9B | Kimi K2.5 | |
|---|---|---|
| Input price | - | $0.38/1M tokens |
| Output price | - | $1.72/1M tokens |
| Providers | - |
Capabilities
| GLM-5 9B | Kimi K2.5 | |
|---|---|---|
| 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 reasoning mode: GLM-5 9B, tool use: GLM-5 9B, and structured outputs: Kimi K2.5. Both models share function calling, 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 Kimi K2.5 has $0.38/1M input tokens. Provider availability is 0 tracked routes versus 7. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GLM-5 9B when reasoning depth and larger context windows are central to the workload. Choose Kimi K2.5 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 Kimi K2.5?
GLM-5 9B supports 262K tokens, while Kimi K2.5 supports 256K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is GLM-5 9B or Kimi K2.5 open source?
GLM-5 9B is listed under Open Source. Kimi K2.5 is listed under MIT. 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 9B or Kimi K2.5?
GLM-5 9B 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 9B or Kimi K2.5?
Both GLM-5 9B and Kimi K2.5 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 9B or Kimi K2.5?
GLM-5 9B has the clearer documented tool use signal in this comparison. If tool use 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 9B and Kimi K2.5?
GLM-5 9B is available on the tracked providers still being sourced. Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-27. Data sourced from public model cards and provider documentation.