GLM-5 vs Kimi K2 Instruct
GLM-5 (2026) and Kimi K2 Instruct (2025) are frontier-tier reasoning models from Zhipu AI and Moonshot AI. GLM-5 ships a 200k-token context window, while Kimi K2 Instruct ships a 131k-token context window. On pricing, Kimi K2 Instruct costs $0.57/1M input tokens versus $0.60/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
GLM-5 is safer overall; choose Kimi K2 Instruct when provider fit matters.
Decision scorecard
Local evidence first| Signal | GLM-5 | Kimi K2 Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and provider-routed production | reasoning-heavy apps and provider-routed production |
| Decision fit | Coding, RAG, and Agents | RAG, Long context, and Classification |
| Context window | 200k | 131k |
| Cheapest output | $2.08/1M tokens | $2.30/1M tokens |
| Provider routes | 7 tracked | 5 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GLM-5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5 has the lower cheapest tracked output price at $2.08/1M tokens.
- GLM-5 has broader tracked provider coverage for fallback and procurement flexibility.
- GLM-5 uniquely exposes Function calling and Tool use in local model data.
- Local decision data tags GLM-5 for Coding, RAG, and Agents.
- Local decision data tags Kimi K2 Instruct for RAG, Long context, and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5
$1,000
Cheapest tracked route/tier: OpenRouter
Kimi K2 Instruct
$1,031
Cheapest tracked route/tier: Vercel AI Gateway
Estimated monthly gap: $31.00. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, Together AI, and NVIDIA NIM; start route-level A/B tests there.
- Kimi K2 Instruct is $0.22/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Function calling and Tool use before moving production traffic.
- Provider overlap exists on Fireworks AI, Together AI, and NVIDIA NIM; start route-level A/B tests there.
- GLM-5 is $0.22/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- GLM-5 adds Function calling and Tool use in local capability data.
Specs
Pricing and availability
| Pricing attribute | GLM-5 | Kimi K2 Instruct |
|---|---|---|
| Input price | $0.60/1M tokens | $0.57/1M tokens |
| Output price | $2.08/1M tokens | $2.30/1M tokens |
| Providers |
Capabilities
| Capability | GLM-5 | Kimi K2 Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | Yes |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| 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 function calling: GLM-5 and tool use: GLM-5. Both models share reasoning mode 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 lists $0.60/1M input and $2.08/1M output tokens on the cheapest tracked provider, while Kimi K2 Instruct lists $0.57/1M input and $2.30/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts GLM-5 lower by about $0.04 per million blended tokens. Availability is 7 providers versus 5, so concentration risk also matters.
Choose GLM-5 when long-context analysis, larger context windows, and broader provider choice are central to the workload. Choose Kimi K2 Instruct when provider fit 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 or Kimi K2 Instruct?
GLM-5 supports 200k tokens, while Kimi K2 Instruct supports 131k 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 or Kimi K2 Instruct?
GLM-5 is cheaper on tracked token pricing. GLM-5 costs $0.60/1M input and $2.08/1M output tokens. Kimi K2 Instruct costs $0.57/1M input and $2.30/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5 or Kimi K2 Instruct open source?
GLM-5 is listed under MIT. Kimi K2 Instruct 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 or Kimi K2 Instruct?
Both GLM-5 and Kimi K2 Instruct 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.
Which is better for function calling, GLM-5 or Kimi K2 Instruct?
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.
Where can I run GLM-5 and Kimi K2 Instruct?
GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Kimi K2 Instruct is available on Fireworks AI, Together AI, NVIDIA NIM, Vercel AI Gateway, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.