GLM-5 9B vs Kimi K2.7-Code
GLM-5 9B (2026) and Kimi K2.7-Code (2026) compare a standalone API model against a coding-specialized model. GLM-5 9B ships a 262k-token context window, while Kimi K2.7-Code ships a 262k-token context window. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: GLM-5 9B is standalone API model, while Kimi K2.7-Code is coding-specialized model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | GLM-5 9B | Kimi K2.7-Code |
|---|---|---|
| Product type | Standalone API model | Coding-specialized model |
| Best for | reasoning-heavy apps and tool-calling agents | custom coding agents, code generation, and tool loops |
| Decision fit | RAG, Agents, and Long context | Coding, RAG, and Agents |
| Context window | 262k | 262k |
| Cheapest output | - | $4/1M tokens |
| Provider routes | 0 tracked | 1 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- Local decision data tags GLM-5 9B for RAG, Agents, and Long context.
- Kimi K2.7-Code has broader tracked provider coverage for fallback and procurement flexibility.
- Kimi K2.7-Code uniquely exposes Vision, Multimodal, and Structured outputs in local model data.
- Local decision data tags Kimi K2.7-Code 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
Kimi K2.7-Code
$1,760
Cheapest tracked route/tier: Moonshot AI Kimi
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 Kimi K2.7-Code; plan for SDK, billing, or endpoint changes.
- Kimi K2.7-Code adds Vision, Multimodal, and Structured outputs in local capability data.
- No overlapping tracked provider route is sourced for Kimi K2.7-Code 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 | Kimi K2.7-Code |
|---|---|---|
| Input price | - | $0.95/1M tokens |
| Output price | - | $4/1M tokens |
| Providers | - |
Capabilities
| Capability | GLM-5 9B | Kimi K2.7-Code |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark scores are currently available for this pair.
Deep dive
The capability footprint differs most on vision: Kimi K2.7-Code, multimodal input: Kimi K2.7-Code, and structured outputs: Kimi K2.7-Code. 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 Kimi K2.7-Code has $0.95/1M input tokens. Provider availability is 0 tracked routes versus 1. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GLM-5 9B when provider fit are central to the workload. Choose Kimi K2.7-Code 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 K2.7-Code?
GLM-5 9B supports 262k tokens, while Kimi K2.7-Code supports 262k 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 K2.7-Code open source?
GLM-5 9B is listed under MIT. Kimi K2.7-Code 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 vision, GLM-5 9B or Kimi K2.7-Code?
Kimi K2.7-Code 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 Kimi K2.7-Code?
Kimi K2.7-Code 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 Kimi K2.7-Code?
Both GLM-5 9B and Kimi K2.7-Code 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 Kimi K2.7-Code?
GLM-5 9B is available on the tracked providers still being sourced. Kimi K2.7-Code is available on Moonshot AI Kimi. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-15. Data sourced from public model cards and provider documentation.