GLM-5 Turbo vs Kimi K2.7-Code
GLM-5 Turbo (2026) and Kimi K2.7-Code (2026) compare a standalone API model against a coding-specialized model. GLM-5 Turbo ships a 200k-token context window, while Kimi K2.7-Code ships a 262k-token context window. On pricing, Kimi K2.7-Code costs $0.95/1M input tokens versus $1.20/1M for the alternative. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: GLM-5 Turbo 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 Turbo | Kimi K2.7-Code |
|---|---|---|
| Product type | Standalone API model | Coding-specialized model |
| Best for | reasoning-heavy apps, tool-calling agents, and provider-routed production | custom coding agents, code generation, and tool loops |
| Decision fit | RAG, Agents, and Long context | Coding, RAG, and Agents |
| Context window | 200k | 262k |
| Cheapest output | $4/1M tokens | $4/1M tokens |
| Provider routes | 2 tracked | 1 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- GLM-5 Turbo has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags GLM-5 Turbo for RAG, Agents, and Long context.
- Kimi K2.7-Code has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Kimi K2.7-Code uniquely exposes Vision and Multimodal 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 Turbo
$1,960
Cheapest tracked route/tier: OpenRouter
Kimi K2.7-Code
$1,760
Cheapest tracked route/tier: Moonshot AI Kimi
Estimated monthly gap: $200. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- No overlapping tracked provider route is sourced for GLM-5 Turbo and Kimi K2.7-Code; plan for SDK, billing, or endpoint changes.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Kimi K2.7-Code adds Vision and Multimodal in local capability data.
- No overlapping tracked provider route is sourced for Kimi K2.7-Code and GLM-5 Turbo; plan for SDK, billing, or endpoint changes.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-01 | 2026-06-12 |
| Context window | 200k | 262k |
| Parameters | 744B total, 40B active | 1T |
| Architecture | Mixture of Experts | Mixture of Experts |
| License | MITOSI-approved | MITOSI-approved |
| Openness | Open source | Open source |
| Commercial use | Commercial use: permitted | Commercial use: permitted |
| Knowledge cutoff | 2025-11 | - |
Pricing and availability
| Pricing attribute | GLM-5 Turbo | Kimi K2.7-Code |
|---|---|---|
| Input price | $1.20/1M tokens | $0.95/1M tokens |
| Output price | $4/1M tokens | $4/1M tokens |
| Providers |
Capabilities
| Capability | GLM-5 Turbo | Kimi K2.7-Code |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | 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 and multimodal input: Kimi K2.7-Code. Both models share reasoning mode, function calling, tool use, 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 $1.20/1M input and $4/1M output tokens on the cheapest tracked provider, while Kimi K2.7-Code lists $0.95/1M input and $4/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.7-Code lower by about $0.18 per million blended tokens. Availability is 2 providers versus 1, so concentration risk also matters.
Choose GLM-5 Turbo when provider fit and broader provider choice are central to the workload. Choose Kimi K2.7-Code when coding workflow support, larger context windows, and lower input-token cost 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 Kimi K2.7-Code?
Kimi K2.7-Code supports 262k tokens, while GLM-5 Turbo supports 200k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, GLM-5 Turbo or Kimi K2.7-Code?
Kimi K2.7-Code is cheaper on tracked token pricing. GLM-5 Turbo costs $1.20/1M input and $4/1M output tokens. Kimi K2.7-Code costs $0.95/1M input and $4/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5 Turbo or Kimi K2.7-Code open source?
GLM-5 Turbo 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 Turbo 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 Turbo 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.
Where can I run GLM-5 Turbo and Kimi K2.7-Code?
GLM-5 Turbo is available on OpenRouter and Vercel AI Gateway. 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.