GLM 4.7 Flash APIOPEN WEIGHTS203K context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is GLM 4.7 Flash?

As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning,...

GLM 4.7 Flash on the release timeline

GLM 4.7 Flash pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, GLM 4.7 Flash is cheaper than 78% of the 309 models in the catalog. It is served by 4 providers; the best combined rate at our last snapshot was ≈ $0.06 / $0.40 per 1M via DeepInfra, with upstream quantizations bf16, fp8.

What GLM 4.7 Flash costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

GLM 4.7 Flash vs alternatives

Specs

How to call GLM 4.7 Flash

Copyfrom openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
    model="z-ai/glm-4.7-flash",
    messages=[{"role": "user", "content": "Hello"}],
    # supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"z-ai/glm-4.7-flash","messages":[{"role":"user","content":"Hello"}]}'

Copyimport OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
  model: "z-ai/glm-4.7-flash",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes GLM 4.7 Flash and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for GLM 4.7 Flash

• State the goal, not every step. GLM 4.7 Flash reasons internally — give it the objective, constraints and success criteria and let it plan the approach; over-scripting each step tends to lower quality. Turn effort up for hard problems.
• Huge 203K context — but anchor the ask. You can paste whole documents or codebases; models attend most to the start and end, so put the key instruction at the top and restate it after long inputs.
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let GLM 4.7 Flash emit tool calls, execute them, and feed results back for the next turn.
• Constrain JSON with a schema. Use response_format / structured outputs rather than "reply in JSON" to get valid, parseable objects every time.
• Cheap enough to batch. Great for high-volume classification, extraction and drafting — template the prompt, batch requests, and validate outputs programmatically.

Tips are derived from GLM 4.7 Flash's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for GLM 4.7 Flash

Popular open-source projects for running and building with GLM 4.7 Flash — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.

Migrating to GLM 4.7 Flash

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → z-ai/glm-4.7-flash. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

GLM 4.7 Flash ships open weights (zai-org/GLM-4.7-Flash, ~2.5M downloads and 1.8K likes in the last 30 days), with community quantizations (bf16, fp8) for smaller GPUs, so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 4 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.

Rate limits & reliability

Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff; because GLM 4.7 Flash is served by 4 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.

Related reading

Frequently asked questions