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.
Specifications
Strengths
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)
How to prompt GLM-4.7 Flash
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)