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GLM-4.7 Flash

Zhipu AI · glm family · Official Docs

GLM-4.7 Flash is an exceptional value proposition: near-free pricing with strong coding benchmarks that rival much larger models. The 200K context / 128K output combination is rare and valuable for long-form generation tasks. The MLA architecture is a Chinese innovation distinct from standard GQA/MQA. Key differentiator from Western models: deeply optimized for Chinese + English bilingual use cases. The multiple thinking mode configurations (interleaved, retention-based, round-level) offer granular control not found in most models. Watch for the quality cliff on complex multi-step reasoning — the 3B active parameters limit depth despite strong benchmarks on individual tasks.

#14
Rank
83
Quality Score
900ms
Avg Response
+13%
Adaptation Gain

Specifications

200K
Context Window
128K
Max Output
$0.06 / $0.4
Per 1M tokens (in/out)
GLM-4.7-Flash is marketed as free with no rate limits on Zhipu's platform. Third-party providers charge $0.06/$0.40. Full GLM-4.7 starts at $10/month for premium tier. (source: pricepertoken.com, docs.z.ai)

Key Capabilities

  • ✓200K token context window — one of the largest among open models (source: docs.z.ai, Official Documentation)
  • ✓128K max output tokens — exceptional generation length (source: docs.z.ai, Official Documentation)
  • ✓30B-A3B MoE architecture with ~3.6B active parameters per token (source: Hugging Face, Model Card)
  • ✓Multi-head Latent Attention (MLA) architecture for efficiency (source: Pandaily, Zhipu announcement)
  • ✓Strong coding benchmarks: SWE-bench Verified 73.8%, LiveCodeBench V6 84.9 SOTA (source: docs.z.ai, Official Documentation)
  • ✓Multiple thinking modes: enabled/disabled, interleaved, retention-based, round-level (source: docs.z.ai, Official Documentation)
  • ✓Function calling and tool invocation capabilities (source: docs.z.ai, Official Documentation)
  • ✓Structured output support including JSON formats (source: docs.z.ai, Official Documentation)
  • ✓Context caching for efficient long conversations (source: docs.z.ai, Official Documentation)
  • ✓Bilingual English/Chinese with strong performance in both (source: Hugging Face, Model Card)

Known Limitations

  • ⚠Text-only input/output — no multimodal/vision support in Flash variant (source: docs.z.ai, Official Documentation)
  • ⚠Drops reasoning chains under stress with complex multi-step workflows (source: WaveSpeedAI comparison)
  • ⚠Quality tapers with very long prompts or dense instructions (source: WaveSpeedAI comparison)
  • ⚠Not a flagship replacement for largest closed models on complex math/niche reasoning (source: WaveSpeedAI comparison)
  • ⚠Multi-file coordinated edits more prone to errors vs full GLM-4.7 (source: WaveSpeedAI comparison)
  • ⚠Agent workflows may stop mid-process requiring new session continuation (source: DataCamp tutorial)
  • ⚠Smaller active parameter count (3B) limits depth of reasoning vs dense models (source: Architecture analysis)

Prompt Patterns

Preferred Instruction Format

Standard OpenAI-compatible chat format with system/user/assistant roles. Supports multiple thinking mode configurations via API parameters.

Recommended Practices

  • Enable thinking mode for complex tasks; disable for simple queries to reduce latency (source: docs.z.ai, Official Documentation)
  • Use retention-based reasoning for long-term multi-turn conversations to improve cache efficiency (source: docs.z.ai, Official Documentation)
  • Leverage 'think before acting' mechanism in coding frameworks like Claude Code, Cline, Roo Code (source: docs.z.ai, Official Documentation)
  • Use task delivery workflow organizing development from requirements to implementation (source: docs.z.ai, Official Documentation)
  • Deploy with vLLM, SGLang, or Transformers for local inference (source: Hugging Face, Model Card)

Anti-Patterns to Avoid

  • Do NOT use extremely long dense instructions in single prompt — quality degrades, use chunking instead (source: WaveSpeedAI comparison)
  • Do NOT rely on Flash for multi-file coordinated edits requiring tight consistency — use full GLM-4.7 instead (source: WaveSpeedAI comparison)
  • Do NOT assume stable agent execution for very long-running workflows — model may stop mid-process (source: DataCamp tutorial)

Sources

What Refrase Does

Here is exactly how Refrase optimizes prompts for GLM-4.7 Flash, rule by rule:

Nested object fix

Refrase restructures prompts to explicitly describe nested JSON object and array schemas, working around GLM's tendency to flatten or mishandle deeply nested structures.

English enforcement

Refrase adds explicit 'respond in English' instructions to prevent the model from switching to other languages, which some multilingual models do by default.

Before / After

See how Refrase transforms a generic prompt for GLM-4.7 Flash.

Original

Extract the key information from this document. Be accurate.

Adapted for GLM-4.7 Flash

Extract the key information from this document.
Return a JSON object where key_points is an array of objects, each with "topic" (string) and "details" (string).
Respond in English.

Try It

Your prompt134 chars
Optimized for glm-47-flash

Click "Refrase It" or select a model to see the optimized prompt.