GLM-5.2 API
Price: $1.000(~ 68 credits) per 1M input tokens
Highest stability with guaranteed 99.9% uptime. Recommended for production environments.
Use the same API endpoint for all versions. Only the model parameter differs.
GLM-5.2 API
Use Z.ai GLM-5.2 when your agent needs to reason across repositories, tools, and long engineering context. EvoLink gives teams one OpenAI-compatible API route, model ID `glm-5.2`, visible token pricing, and a gateway path that fits existing SDKs and coding-agent stacks.
Access and workflow fit
Best fit
Coding agents
Model ID
glm-5.2
Access
OpenAI-compatible
Context
1M window
Input
$1.00/1M
Built-in
Thinking + tools + caching
Where GLM-5.2 fits in production engineering workflows
Long-horizon coding agents
Use GLM-5.2 for agents that need to inspect repository context, explain architectural choices, plan multi-file changes, or review pull requests. EvoLink keeps the integration path OpenAI-compatible, so existing coding CLIs, editor tools, and agent frameworks can usually reuse the same client pattern.
Tool-using engineering assistants
Route tool-using assistants through GLM-5.2 when they need to combine reasoning, function calling, retrieval, tests, or internal APIs. EvoLink keeps those calls under one key and one usage surface, which makes agent experiments easier to move toward production.
Long-context repo and document analysis
Use the large context window for codebases, specifications, reports, and knowledge bases that are painful to split too aggressively. Stable repository prefixes, system prompts, and project context can also be designed around prompt caching for recurring agent workloads.
Why access GLM-5.2 through EvoLink
The model story is long-horizon coding and engineering agents. The EvoLink story is practical access: one key, OpenAI-compatible routing, model ID clarity, pricing visibility, and a gateway layer that avoids another vendor-specific integration.
Fit GLM-5.2 into existing agent stacks
Call GLM-5.2 through an OpenAI-compatible route with one EvoLink key. Existing OpenAI SDK code, coding CLIs, and agent frameworks can usually be adapted by changing the base URL and setting `model` to `glm-5.2`.
Price the agent workload before it scales
Long-running agents can spend heavily on input, output, and repeated context. EvoLink exposes live token pricing for GLM-5.2, including input, output, and cache-read usage, so teams can budget against actual route behavior instead of a vague model description.
Keep route choice and usage visible
GLM-5.2 can be evaluated beside other EvoLink models without rebuilding client integrations. That matters when coding-agent workloads need fallback options, cost checks, and routing decisions over time.
Model comparison
GLM-5.2 vs GPT-5.5 vs Claude Opus 4.8
Use this as a practical coding-agent shortlist. Benchmark all three on the same repo Q&A, multi-file refactor, PR review, and tool-calling traces before changing production routes.
| Model | Best fit | Test against GLM-5.2 | Routing role |
|---|---|---|---|
| GLM-5.2 | OpenAI-compatible coding agents, 1M-context repo work, and cost-aware engineering tasks. | Full-repo Q&A, long context retention, tool loops, prompt caching, and cost per successful task. | Candidate default or cost-aware route for coding-agent workloads. |
| GPT-5.5 | OpenAI flagship reasoning and coding workflows with strong SDK and tool ecosystem fit. | Hard debugging, architecture review, existing GPT workflows, and premium escalation cases. | Premium GPT benchmark or escalation route when failure is expensive. |
| Claude Opus 4.8 | Complex reasoning, long-horizon agentic coding, and high-autonomy engineering work. | Multi-file refactors, PR review quality, tool-use recovery, and long-running agent sessions. | Premium Claude benchmark for the hardest coding-agent traces. |
The product page should not declare a universal winner. The useful decision is which route wins on your own engineering traces.
Read the full comparison guideHow to route GLM-5.2 through EvoLink
Start with the access facts that usually break integrations: the exact model ID is `glm-5.2`, the route is OpenAI-compatible, and the live pricing table on this page is the pricing source of truth.
Step 1 — Point your client at EvoLink
Create an EvoLink API key, use Bearer auth, and configure your OpenAI-compatible client or agent framework to use the EvoLink base URL.
Step 2 — Use the exact model ID
Set `model` to `glm-5.2` in the request body. The page slug is `/glm-5-2`, but production requests should use the dotted model ID.
Step 3 — Add tools and context deliberately
Start with a plain chat call, then add repo context, tool schemas, and agent loops in stages. Track input, output, cache-read usage, latency, and retries before moving high-volume coding-agent traffic.
GLM-5.2 model comparison and routing fit
Use this checklist to decide when GLM-5.2 should be the primary coding-agent route, when to compare it against premium coding models, and when to keep cheaper fallbacks in the same gateway.
Choose GLM-5.2 when repo context is the bottleneck
GLM-5.2 is positioned for coding agents and complex engineering workflows where the model must hold long context, plan across multiple steps, and reason about code or tools beyond a single prompt.
Compare it with premium coding models
Evaluate GLM-5.2 beside GPT, Claude, Gemini, or other coding-capable routes for your own PR review, repo Q&A, and agent tasks. EvoLink keeps the client path, usage view, and pricing comparison in one place.
Keep cheaper fallbacks for simple work
Do not route every request to a long-context reasoning model. Simple classification, formatting, and short edits can use lower-cost routes while GLM-5.2 handles harder coding-agent or long-context jobs.
~1M context window
The large context window is useful for repository summaries, specs, logs, or long documents. Use it intentionally: full-context prompts are powerful, but token cost still scales with usage.
Tool calling and OpenAI-compatible access
Use structured function calling for assistants that need retrieval tools, internal APIs, test runners, or workflow actions. The OpenAI-compatible path keeps setup familiar for SDKs, CLIs, and agent frameworks.
Prompt caching and visible token pricing
Stable system prompts, repository summaries, and repeated prefixes can be designed around cache-read pricing when the workload qualifies. The live table shows input, output, and cache-read prices before scaling traffic.
GLM-5.2 API FAQs
Everything you need to know about the product and billing.