Short answer: “GLM-4” can refer to several different text models, not one universal API model ID. BigModel’s documentation lists five variants—GLM-4-Plus, GLM-4-Air-250414, GLM-4-AirX, GLM-4-FlashX-250414, and GLM-4-Flash-250414. Z.AI separately documents GLM-4-32B-0414-128K. Their listed specifications and API examples belong to different provider channels, so choose an exact model and confirm its identifier, availability, price, and limits with the provider you intend to use. (docs.bigmodel.cn)
The comparison below reflects what the linked provider pages display; it is not an independent benchmark or a guarantee that prices and terms will apply to your account. The pages do not establish that similarly named models across the two channels are interchangeable.
Which GLM-4 model might fit?
BigModel’s model cards list text input and text output for all five variants, with these context limits, maximum output limits, and displayed prices. BigModel describes Plus as high-performance, Air as value-oriented, AirX as focused on fast inference, FlashX as high-speed and low-price, and Flash as its free option. Those descriptions are the provider’s positioning, not comparative test results. (docs.bigmodel.cn)
| Model listed by BigModel | Provider-stated positioning | Context | Maximum output | Displayed price |
|---|---|---|---|---|
| GLM-4-Plus | High performance | 128K | 4K tokens | ¥5 per million tokens |
| GLM-4-Air-250414 | High value | 128K | 16K tokens | ¥0.5 per million tokens |
| GLM-4-AirX | Fast inference | 8K | 4K tokens | ¥10 per million tokens |
| GLM-4-FlashX-250414 | High speed, low price | 128K | 16K tokens | ¥0.1 per million tokens |
| GLM-4-Flash-250414 | Free option | 128K | 16K tokens | Listed as free |
Price caveat: The BigModel page displays a per-million-token price but does not say whether it applies to input tokens, output tokens, or both. Its free listing does not specify conditions. Treat the figures as displayed values, not a complete bill estimate; confirm current rates and eligibility before planning costs. (docs.bigmodel.cn)
A separate Z.AI page documents GLM-4-32B-0414-128K with text input and output, a 128K context, 16K maximum output, and a listed price of $0.1 per million tokens. That price is in a different currency and API channel from the BigModel figures. The page also does not provide an input/output price breakdown, so do not assume the billing basis is the same. (docs.z.ai)
A practical shortlist by workload
- Need a long context and up to 16K output tokens? Several BigModel variants list 128K context and 16K maximum output: Air-250414, FlashX-250414, and Flash-250414. GLM-4-32B-0414-128K is another separately documented option with those limits on
Z.AI. A larger context limit does not, by itself, show that a model will reason better over your documents. (docs.bigmodel.cn) - Considering AirX for latency-sensitive traffic? BigModel positions it for fast inference and high concurrency, but its card lists 8K context and 4K maximum output—less than the listed limits for the other four BigModel variants. Check that your prompts and expected answers fit before prioritizing speed. (
docs.bigmodel.cn) - Comparing on price? Start with the displayed rates, but do not select solely on them: the captured BigModel page does not clarify input/output billing, and the
Z.AIprice is in USD on a separate channel. Measure the cost on representative requests after confirming the current rate and billing rules. (docs.bigmodel.cn) - Choosing Plus for difficult tasks? BigModel labels Plus “high performance” and publishes benchmark results for it. Those are provider-reported results, not independent evidence that Plus will outperform the alternatives on your particular workload. (
docs.bigmodel.cn)
Naming caution: BigModel’s overview describes FlashX as an enhanced version of free GLM-4-Flash, while its detailed text uses the Flash-250414 name when discussing capabilities. Do not transfer a detailed Flash claim to FlashX—or assume the names are interchangeable—without checking the exact current model documentation. (docs.bigmodel.cn)
Documented capabilities and use cases
BigModel lists streaming output, function calling, context caching, structured output, and MCP support. Its suggested use cases include translation, data classification, document information extraction, marketing copy, risk reports, and itinerary planning. The page does not map each listed capability to each variant, and these suggested applications are not performance guarantees. For tasks needing current information—such as travel planning or risk analysis—the documentation recommends combining the model with search tools. (docs.bigmodel.cn)
For GLM-4-32B-0414-128K specifically, Z.AI lists streaming, structured output, function calling, knowledge-base retrieval, and web search. It suggests uses including enterprise Q&A, text extraction and inspection, financial analysis, code generation, and job-market analysis. These are provider descriptions; the captured page does not give task-specific independent evaluations or detailed configuration instructions for every feature. (docs.z.ai)
To decide whether those features are sufficient, test the exact model you plan to deploy. For example, if the application extracts invoice fields, compare its output with labeled examples and record field-level accuracy, missing values, and invalid JSON. If it answers from a knowledge base, check whether it cites or uses the right source and how it behaves when the answer is absent. For an agent, measure successful tool calls and end-to-end task completion—not just whether the model can produce a plausible response.
API examples: keep the provider channels separate
The provider examples show a basic chat-completions request with Bearer-key authentication. They do not establish current access for a particular account, region, or model ID, and a successful quick-start call alone does not establish production readiness.
BigModel example: GLM-4-Plus
BigModel’s example uses the open.bigmodel.cn endpoint and the glm-4-plus model ID. This is the example for that provider channel; do not substitute the Z.AI endpoint or identifier into it. (docs.bigmodel.cn)
curl -X POST "https://open.bigmodel.cn/api/paas/v4/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-4-plus",
"messages": [
{"role": "user", "content": "Summarize this text in three bullet points."}
],
"max_tokens": 4096,
"temperature": 0.7
}'
To request streaming, BigModel’s example adds "stream": true to the request body. Its captured documentation demonstrates Plus; confirm the exact model ID before using a different variant. (docs.bigmodel.cn)
Z.AI example: GLM-4-32B-0414-128K
Z.AI documents a distinct endpoint and the model ID glm-4-32b-0414-128k. Its example sends a messages array and uses Bearer authentication. Keep this example’s endpoint, identifier, account, and pricing separate from BigModel’s. (docs.z.ai)
curl -X POST "https://api.z.ai/api/paas/v4/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "glm-4-32b-0414-128k",
"messages": [
{"role": "user", "content": "Summarize this text in three bullet points."}
]
}'
Z.AI’s streaming example adds "stream": true. Its documentation also shows Python SDK and OpenAI Python SDK examples, but those examples do not establish that every feature of those clients is supported. Check the current API reference for the exact request parameters and response behavior you need. (docs.z.ai)
What to verify before production
Before choosing a GLM-4 API for a live application, verify these items for the exact model, provider channel, and account:
- Model ID and availability: Confirm the identifier accepted by the endpoint you will call and that your account can access it.
- Price and billing basis: Check the current currency, unit, input/output billing, any free-tier conditions, and other charges relevant to your use.
- Limits and throughput: Confirm context and output limits, rate limits, concurrency, and any tier eligibility. BigModel’s page includes concurrent in-flight request figures by user tier, but does not explain tier eligibility in the captured text. The captured
Z.AImodel page does not provide production rate limits. (docs.bigmodel.cn) - Feature support: Verify that the exact model supports the required mode—such as streaming, function calling, structured output, retrieval, MCP, or web search—and learn its constraints.
- Failure handling: Test authentication failures, timeouts, rate-limit responses, retries, and partial streaming responses using the provider’s current API documentation.
- Workload fit: Run a representative evaluation set and track task quality, output length, latency, throughput, and observed cost. Provider positioning and benchmark summaries can inform what to test, but cannot replace tests on your own prompts and constraints.
- Operational terms: Review the provider’s current service, data-handling, and reliability terms for your deployment. The cited quick-start pages do not establish an SLA or prove production suitability.
Bottom line
GLM-4 is a family label, not enough information to select or call a model. First choose the specific documented variant and API channel; then compare its stated limits and features with your workload. Use the published specifications and provider positioning to form a shortlist, not to assume current pricing, guaranteed performance, or production readiness. Confirm the exact ID and terms in the relevant provider’s current documentation, then test the model under realistic conditions. (docs.bigmodel.cn)