GLM-4.7-Flash vs Qwen3-Coder-30B: Which One Fits Your Coding Workflow Better?

If you’re choosing a coding-focused LLM for production, you’re usually balancing three realities:

• Code quality on real engineering tasks
• Speed & latency for an interactive developer experience
• Cost at scale (especially when context gets long)

In this post, we compare GLM-4.7-Flash and Qwen3-Coder-30B through that lens—using benchmark + speed/latency files (placeholders included below), and Novita AI’s official pricing for cost.

Basic Introduction

Item	GLM-4.7-Flash	Qwen3-Coder (30B-A3B)
Publisher	Z.ai (GLM Series)	Alibaba (Qwen Series)
Release	Jan 2026	July 2025
Architecture	MoE: ~30B total parameters / ~3B active per token	MoE: ~30B total parameters / ~3B active per token (A3B)
Input / Output	Text → Text	Text → Text
Context Length	200K (128K output)	262K native (up to 1M w/ YaRN)
Reasoning Mode	Supports thinking modes	Non-thinking only
Novita Model ID	zai-org/glm-4.7-flash	qwen/qwen3-coder-30b-a3b-instruct

High-level takeaway:GLM-4.7-Flash is optimized for fast, controllable execution in production and interactive workflows, while Qwen3-Coder-30B leans into stronger deep reasoning signals on several “hard” evaluations—at the cost of higher latency in interactive settings.

Benchmark Comparison

The benchmark story is essentially a tradeoff between execution-oriented coding and depth-oriented reasoning.

Capability Dimension	Included Benchmarks	GLM-4.7-Flash	Qwen3-Coder
Coding / Terminal / Tool Use	Terminal-Bench Hard; τ²-Bench Telecom; SciCode	40.70%	26.00%
Long-Context Reasoning	AA-LCR	15.00%	29.00%
Knowledge Accuracy	AA-Omniscience Accuracy	12.00%	15.00%
Non-Hallucination (Reliability)	AA-Omniscience Non-Hallucination Rate	6.00%	21.00%
General Reasoning & Knowledge	Humanity’s Last Exam	4.90%	4.00%
Scientific Reasoning	GPQA Diamond	45.00%	52.00%
Overall Judgment / Evaluation	GDPval-AA	18.00%	14.00%

• GLM-4.7-Flash performs better in the most “engineering-like” bucket—Coding / Terminal / Tool Use—scoring 40.7% vs 26.0%. That combination (Terminal-Bench Hard + τ²-Bench Telecom + SciCode) maps well to real workflows where the model must write code, interact with tools, interpret outputs, and keep moving. It also shows a stronger signal on overall judgment via GDPval-AA (18.0% vs 14.0%), plus a small edge on general reasoning & knowledge (Humanity’s Last Exam: 4.9% vs 4.0%).
• Qwen3-Coder-30B shines when tasks are long and reliability-sensitive. It leads Long-Context Reasoning (29.0% vs 15.0%), which matters when you’re feeding large repo context or long specs and need the model to stay coherent. It also has a major advantage on non-hallucination / reliability (21.0% vs 6.0%) and a modest lead in knowledge accuracy (15.0% vs 12.0%), making it a better fit when confident mistakes are costly. It’s also stronger on scientific reasoning (GPQA Diamond: 52.0% vs 45.0%), which can matter for more research-heavy or mathematically complex coding tasks.

You can choose GLM-4.7-Flash for tool-heavy coding execution and practical decision-making; choose Qwen3-Coder-30B for long-context depth and higher reliability.

Speed & Latency Comparison

For coding assistants, “fast enough” isn’t just about raw throughput—it’s about how quickly the model starts responding (TTFT) and how long a typical turn takes end-to-end.

Metric	GLM-4.7-Flash	Qwen3-Coder-30B	Better (direction)
Latency (TTFT: Time to First Answer Token)	0.9 s	1.5 s	Lower is better → GLM-4.7-Flash
End-to-End Response Time (500 output tokens)	5.6 s	6.3 s	Lower is better → GLM-4.7-Flash
Output Speed (tokens/sec)	106 tok/s	104 tok/s	Higher is better → GLM-4.7-Flash

Interpretation

• Snappier “first response” in chat/IDE: GLM-4.7-Flash reaches the first answer token in 0.9s vs 1.5s, making it noticeably more responsive for interactive coding chats, IDE copilots, and rapid debugging loops.
• Faster turn completion for common coding prompts: For a 500-token response, GLM-4.7-Flash finishes in 5.6s vs 6.3s—a consistent edge when users iterate quickly across many turns.
• Similar decoding throughput: Output speed is close (106 vs 104 tok/s), so the key UX advantage is mostly latency + end-to-end time, not raw tokens/sec.

Cost Comparison

Cost Item (Novita Serverless)	GLM-4.7-Flash	Qwen3-Coder (30B-A3B)
Input price (per 1M tokens)	$0.07 / Mt	$0.07 / Mt
Output price (per 1M tokens)	$0.40 / Mt	$0.27 / Mt
Cache read (per 1M tokens)	$0.01 / Mt	–

On Novita Serverless, Qwen3-Coder (30B-A3B) is cheaper for output-heavy coding (lower output $/Mt), while GLM-4.7-Flash becomes more cost-efficient when cache read applies to repeated context.

Quickstart: Try Both Models Instantly on Playground

Novita AI provides an interactive Playground where you can test both models instantly—no deployment required.

How to Deploy: API, SDK, Integrations and Local Deployment

API

Get an API Key

• Step 1: Create or Login to Your Account

Visit https://novita.ai and sign up or log in to your existing account

• Step 2: Navigate to Key Management

After logging in, find “API Keys”

• Step 3: Create a New Key

Click the “Add New Key” button.

• Step 4: Save Your Key Immediately

Copy and store the key as soon as it is generated; it is usually shown only once and cannot be retrieved later. Keep the key in a secure location such as a password manager or encrypted notes

OpenAI-Compatible API (Python)

from openai import OpenAI
client = OpenAI(
    api_key="<YOUR_NOVITA_API_KEY>",
    base_url="https://api.novita.ai/openai",
)
resp = client.chat.completions.create(
    model="zai-org/glm-4.7-flash",  # or "qwen/qwen3-coder-30b-a3b-instruct"
    messages=[
        {"role": "system", "content": "You are a precise engineering assistant. Output valid JSON when asked."},
        {"role": "user", "content": "Summarize the key risks of rolling out feature flags across 20 services."},
    ],
    temperature=0.3,
    max_tokens=4096,
)

print(resp.choices[0].message.content)

SDK

If you’re building agentic workflows (routing, handoffs, tool/function calls), Novita works with OpenAI-compatible SDKs with minimal changes:

• Drop-in compatible: keep your existing client logic; just change base_url + model
• Orchestration-ready: easy to implement routing (Flash default → GLM-4.7 escalation)
• Setup: point to https://api.novita.ai/openai, set NOVITA_API_KEY, select zai-org/glm-4.7-flash / qwen/qwen3-coder-30b-a3b-instruct

Third-Party Platforms

You can also run Novita-hosted GLM models through popular ecosystems:

• Agent frameworks & app builders: Follow Novita’s step-by-step integration guides to connect with popular tooling such as Continue, AnythingLLM, LangChain, and Langflow.
• Hugging Face Hub: Novita is listed as an Inference Provider on Hugging Face, so you can run supported models through Hugging Face’s provider workflow and ecosystem.
• OpenAI-compatible API: Novita’s LLM endpoints are compatible with the OpenAI API standard, making it easy to migrate existing OpenAI-style apps and connect many OpenAI-compatible tools ( Cline, Cursor, Trae and Qwen Code) .
• Anthropic-compatible API: Novita also provides Anthropic SDK–compatible access so you can integrate Novita-backed models into Claude Code style agentic coding workflows.
• OpenCode: Novita AI is now integrated directly into OpenCode as a supported provider, so users can select Novita in OpenCode without manual configuration.

Local & Private Deployment

Because GLM-4.7-Flash and Qwen3-Coder 30B (A3B) are relatively lightweight compared to frontier-scale models, they’re practical options for teams that prefer local-style deployment—whether for privacy, compliance, or tighter control over the runtime.

If you want the benefits of local deployment without the hassle of maintaining your own GPU hardware, drivers, and CUDA stack, you can run them on Novita GPU Instances. Novita also offers a growing Templates Library to help you launch faster, including a ready-to-use GLM-4.7-Flash template.

Conclusion

Choose GLM-4.7-Flash if you need:

• fast, low-latency interaction
• strong agentic coding & tool use
• significantly lower production cost

Choose Qwen3-Coder if you need:

• deep long-context reasoning
• scientific or analytical reliability
• large-scale repository understanding

On Novita AI, both models are production-ready—but for most interactive and cost-sensitive coding workloads, GLM-4.7-Flash delivers the best overall balance.

Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing the affordable and reliable GPU cloud for building and scaling.

Frequently Asked Questions