GLM 4.7 Flash
Z.ai's lightweight 30B MoE text model built for fast coding, tool use, and agentic tasks.
Get API key- Provider
- Z.ai
- Price
- $0.13 in · $0.50 out / 1M
- Context window
- 128K tokens
- Released
- January 19, 2026
- License
- MIT
What is GLM 4.7 Flash?
GLM 4.7 Flash is a lightweight, open-weights text model from Z.ai. Released in January 2026, it is a 30B-parameter mixture-of-experts model with 3B active parameters per token, built for fast coding, tool use, and agentic tasks. It runs under an MIT license and offers strong benchmark performance for its size class.
Use it privately on Venice
On Venice, GLM 4.7 Flash runs under a private, zero-retention tier — your prompts are not stored or used for training. You get the same open-weights model with tool use, reasoning, and web search capabilities, served with fp8 quantization for efficient inference without Big-Tech surveillance.
What can it do?
- •Strong coding and agentic performance for its size — scores 59.2 on SWE-bench Verified and 91.6 on AIME 2025 per the official model card.
- •Supports tool use, reasoning, and web search, plus structured JSON output for building automated workflows.
- •Open-weights MIT license lets you inspect, modify, or self-host the model outside of Venice.
- •Efficient fp8 quantization on Venice keeps inference fast and affordable without sacrificing core capabilities.
- •Offers thinking modes for complex multi-step tasks, improving reliability on debugging and agentic execution.
- •Text-only — no vision or image input support.
- •Not fully uncensored — safety alignment is present, so it may refuse certain sensitive or restricted prompts.
- •Smaller active parameter count (3B per token) can limit depth on the most complex reasoning tasks compared to full-scale frontier models.
- •Venice hosts the fp8 quantized version; self-hosters seeking maximum fidelity may want to run the original full-precision weights locally.
GLM 4.7 Flash capabilities
- Tool use / function calling
- Vision (image input)
- Reasoning
- Web search
- Code-optimized
- Structured output (JSON schema)
- Audio input
- Video input
- Multiple image inputs
- Log probabilities
How to use it via API
Venice exposes an OpenAI-compatible API. Swap your base URL and call zai-org-glm-4.7-flash.
curl https://api.venice.ai/api/v1/chat/completions \
-H "Authorization: Bearer $VENICE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "zai-org-glm-4.7-flash",
"messages": [{ "role": "user", "content": "Explain quantum tunneling simply." }]
}'Specifications
Pricing
Billed per token on Venice: $0.13 per 1M input tokens and $0.50 per 1M output tokens.
New Venice accounts include a free daily allowance and 500 welcome credits — no credit card required.
GLM 4.7 Flash vs alternatives
| Model | Context window | Open weights | Price (Venice) |
|---|---|---|---|
| GLM 4.7 Flash | 128K tokens | Yes | $0.13 in · $0.50 out / 1M |
| DeepSeek V3.2 | 160K tokens | Yes | $0.33 in · $0.48 out / 1M |
| Google Gemma 4 31B Instruct | 256K tokens | Yes | $0.12 in · $0.36 out / 1M |
| Kimi K2.6 | 256K tokens | Yes | $0.75 in · $3.50 out / 1M |
Lightweight MoE with tool use, reasoning, and web search. Best price-to-performance for high-volume coding.
What is it good for?
- •High-throughput coding assistants and autocomplete where low latency matters.
- •Agentic workflows that need function calling, web search, and structured JSON output.
- •Lightweight self-hosted deployment for privacy-sensitive teams (open weights, MIT license).
- •Multi-turn debugging and reasoning tasks using the model’s thinking mode.
- •Cost-effective text generation for startups and indie developers.
Prompting tips
- •Enable thinking mode for complex debugging or multi-step agent tasks — it improves accuracy at the cost of slightly higher latency.
- •Use structured output (JSON schema) when integrating with tools or APIs to get predictable, parseable responses.
- •For coding, specify the language and framework explicitly; the model is tuned for precise code generation.
- •Keep context within Venice’s 128K token window for best results, even though the native model supports 200K.
Version history
Base model in the GLM-4.7 series.
CurrentCurrent lightweight, speed-optimized variant.
Frequently asked questions
GLM 4.7 Flash is a lightweight, open-weights text model from Z.ai (Zhipu AI). Released in January 2026, it is a 30B-parameter MoE model with 3B active parameters per token, designed for fast coding, tool use, reasoning, and web search.
Venice bills per token at $0.13 per 1M input tokens and $0.50 per 1M output tokens. There is no subscription required; you pay only for what you use.
Yes. The model weights are released under an MIT license and available on Hugging Face, so you can self-host or modify it. On Venice, it runs privately with zero retention.
Yes. On Venice it supports function calling, reasoning, web search, and structured JSON output, making it suitable for agentic workflows and automated pipelines.
GLM 4.7 Flash is cheaper on input and offers strong coding benchmarks for its size, while DeepSeek V3.2 provides a larger 160K context window and very similar output pricing. Choose Flash for cost efficiency; choose DeepSeek V3.2 if you need the extra context.
No. While it is open-weights and private on Venice, it is not marketed as uncensored and retains standard safety alignment. It may refuse certain sensitive requests.
Venice hosts GLM 4.7 Flash with a 128K token context window and up to 16,384 tokens max output per response.
Yes. The model supports thinking modes for multi-step reasoning and complex agentic tasks, which improves accuracy on debugging and advanced coding problems.
Related models
Run GLM 4.7 Flash privately.
No prompt logging. No data used for training. Free to start — no credit card.
