GLM-5.1 API 2026: 8-Hour Agentic Coding, 200K Context, and the CodeFast GLM Package
GLM-5.1 is one of 2026's most interesting Claude-alternative coding models with 200K context, 128K max output, tool calling, and long-running agentic coding positioning. While official Z.ai pricing lists $1.40/M input and $4.40/M output, the CodeFast GLM API package starts GLM experiments with a much lower entry cost: 600 TRY for 30 days with 750 daily uses.
· CodeFast Team
Why GLM-5.1 is getting attention
Z.ai positions GLM-5.1 for complex reasoning, long context, and agentic work. The official docs highlight a 200K context window, 128K max output, thinking mode, streaming, function calling, structured output, context caching, and MCP support. That combination moves the model beyond a classic chatbot and closer to a developer agent that reads code, uses tools, plans, and iterates.
- 200K context makes it easier to keep large repo slices, long logs, and multi-step technical docs inside one task.
- 128K max output helps produce larger responses such as migration plans, extensive test suggestions, or long code drafts.
- Function calling, structured output, and MCP support make it easier to connect the model to real agent pipelines.
- Z.ai's emphasis on long-running work makes GLM-5.1 a niche but valuable candidate for agentic coding scenarios that can run for up to 8 hours.
Official Z.ai pricing and the CodeFast cost difference
Z.ai's pricing page lists GLM-5.1 at $1.40 per 1M input tokens and $4.40 per 1M output tokens. That is aggressive for a capable model, but the real developer cost depends on the number of experiments, output length, retries, tool-call loops, and context size. In coding-agent work, the model does not simply answer once; it reads files, plans, retries, and can generate long outputs.
CodeFast GLM API solves a different problem: instead of tracking token billing request by request, you see a package cost and daily usage limit up front. In the public package data on June 3, 2026, GLM API is listed at 600 TRY for 30 days with 750 daily uses. Limits and duration options can change, but the idea is clear: testing GLM-5.1 in real application prototypes becomes more predictable and practical than starting with open-ended token billing.
Official Z.ai snapshot - June 3, 2026
Model: GLM-5.1
Context window: 200K tokens
Max output: 128K tokens
Capabilities: thinking, streaming, function calling, structured output, context caching, MCP
Official price: $1.40 / 1M input tokens, $4.40 / 1M output tokens
CodeFast public package snapshot - June 3, 2026
Package: GLM API
Price: 600 TRY
Duration: 30 days
Daily usage: 750 uses
Base URL: https://api.codefast.app/glm-api
Model example: glm-5.1
Official model pricing and CodeFast package limits can change over time; check current pages before purchase or production use.
How CodeFast GLM API connection works
On CodeFast, GLM API can be used through the OpenAI-compatible chat/completions format. That means existing OpenAI-compatible clients can point to the CodeFast base URL and select glm-5.1 as the model. For tools that use the Messages format, an Anthropic-compatible endpoint is also available. This flexibility matters for Cursor-like IDE tools, terminal agents, custom backend services, and test automation.
curl https://api.codefast.app/glm-api/chat/completions \
-H "Authorization: Bearer cf_live_your_api_key" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-5.1",
"messages": [
{ "role": "system", "content": "You are a careful coding agent." },
{ "role": "user", "content": "Bu repodaki ödeme akışını inceleyip riskli noktaları listele." }
]
}'
Some clients automatically append /v1 to the Base URL; CodeFast GLM documentation also lists /v1 usage as a supported option.
Where GLM-5.1 makes sense
- Large codebase analysis: evaluate many files, logs, bug reports, and technical docs inside one task context.
- Refactor and migration planning: generate dependencies, risks, test points, and sequencing as an iterative flow instead of one isolated answer.
- Coding-agent experiments: benchmark GLM-5.1 on loops that include terminal commands, tool calls, test writing, bug fixing, and retries.
- Searching for a Claude alternative: compare quality, latency, and cost on the same prompt set.
- Affordable API prototyping: test GLM through a package workflow before moving a new idea into open-ended token billing.
When direct Z.ai API access is the better fit
CodeFast is strongest when you want low entry cost, one-panel management, package limits, and fast testing. Some teams may still prefer direct Z.ai API access for enterprise contracts, special region needs, precise token accounting, provider-side beta features, or custom rate-limit agreements. The right question is not which option is always better; it is which option matches your budget, control, and speed needs.
Agent prompt design for GLM-5.1
Long context and agentic model strength are not enough by themselves. To get good results from models like GLM-5.1, define the task, allowed tools, success criteria, and stopping condition clearly. In coding-agent scenarios, the goal is not simply making the model work harder; it is making it follow the right steps and verify its work.
- Split the task into small goals: inspect, plan, implement, test, report.
- When filling context, include only relevant files and logs; 200K context is not unlimited memory.
- Summarize tool-call results; it reduces the chance of repeating earlier mistakes.
- For cost control, set retry and max-output limits according to product behavior.
Checklist before production
- Test the same prompt set with Claude, GPT, GLM, and open-model alternatives when available; do not decide from one impressive answer.
- Log error formats in tool-use flows; debug visibility is as critical as model quality in agent systems.
- Design context caching and summarization; sending the same information repeatedly in long tasks increases cost.
- Check the current price, duration, and daily usage on the CodeFast package screen before purchase.