GLM-5 vs Kimi K2 Thinking Turbo
GLM-5 (2026) and Kimi K2 Thinking Turbo (2025) are frontier reasoning models from Zhipu AI and Moonshot AI. GLM-5 ships a 200k-token context window, while Kimi K2 Thinking Turbo ships a 262K-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.
GLM-5 is safer overall; choose Kimi K2 Thinking Turbo when long-context analysis matters.
Decision scorecard
Local evidence first| Signal | GLM-5 | Kimi K2 Thinking Turbo |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | Long context |
| Context window | 200k | 262K |
| Cheapest output | $2.08/1M tokens | - |
| Provider routes | 5 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GLM-5 has broader tracked provider coverage for fallback and procurement flexibility.
- GLM-5 uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags GLM-5 for Coding, RAG, and Agents.
- Kimi K2 Thinking Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Kimi K2 Thinking Turbo for Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
GLM-5
$1,000
Cheapest tracked route: OpenRouter
Kimi K2 Thinking Turbo
Unavailable
No complete token price in local provider data
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 and Kimi K2 Thinking Turbo; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- No overlapping tracked provider route is sourced for Kimi K2 Thinking Turbo and GLM-5; plan for SDK, billing, or endpoint changes.
- GLM-5 adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-11 | 2025-11-06 |
| Context window | 200k | 262K |
| Parameters | 744B total, 40B active | — |
| Architecture | mixture of experts | - |
| License | MIT | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | GLM-5 | Kimi K2 Thinking Turbo |
|---|---|---|
| Input price | $0.6/1M tokens | - |
| Output price | $2.08/1M tokens | - |
| Providers | - |
Capabilities
| Capability | GLM-5 | Kimi K2 Thinking Turbo |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on reasoning mode: GLM-5, function calling: GLM-5, tool use: GLM-5, and structured outputs: GLM-5. Both models share the core language-model surface, 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 has $0.6/1M input tokens and Kimi K2 Thinking Turbo has no token price sourced yet. Provider availability is 5 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GLM-5 when reasoning depth and broader provider choice are central to the workload. Choose Kimi K2 Thinking Turbo when long-context analysis and larger context windows 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 or Kimi K2 Thinking Turbo?
Kimi K2 Thinking Turbo supports 262K tokens, while GLM-5 supports 200k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is GLM-5 or Kimi K2 Thinking Turbo open source?
GLM-5 is listed under MIT. Kimi K2 Thinking Turbo 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 reasoning mode, GLM-5 or Kimi K2 Thinking Turbo?
GLM-5 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 or Kimi K2 Thinking Turbo?
GLM-5 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.
Which is better for tool use, GLM-5 or Kimi K2 Thinking Turbo?
GLM-5 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 and Kimi K2 Thinking Turbo?
GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Kimi K2 Thinking Turbo is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.