GLM-4.7 vs DeepSeek V3.2: Which Coding Model Fits Your Production Workflow?

Choosing the right AI model for production coding isn’t just about benchmark scores. As open-source models reach frontier performance, developers face a critical decision: optimize for speed and stability, or prioritize cost and deep reasoning capabilities?

GLM-4.7 and DeepSeek V3.2 represent two distinct approaches. Both are MIT-licensed MoE models with thinking capabilities, released within weeks of each other in late 2025. Their architectural differences—GLM-4.7’s “thinking before acting” versus DeepSeek’s sparse attention optimization—create fundamentally different performance profiles for production workflows. This comparison examines benchmarks, speed metrics, and community feedback to help teams make informed deployment decisions on Novita AI’s platform.

Model Overview

Feature	GLM-4.7	DeepSeek V3.2
Organization	Z.ai	DeepSeek AI
Release Date	December 22, 2025	December 1, 2025
Parameters	355B total / 32B activated	671B total / 37B activated
Architecture	MoE with Thinking Modes	MoE with Sparse Attention (DSA)
Context Window	200K input / 128K output	163.84K input / 64K output
License	MIT (Open Source)	MIT (Open Source)
Pricing on Novita AI	$0.60/M input, $2.20/M output	$0.269/M input, $0.40/M output

• GLM-4.7: Focuses on production-grade stability with a “thinking before acting” design, combining a 200K context window and very fast generation, making it well suited for low-latency, high-accuracy interactive coding workflows.
• DeepSeek V3.2: Optimized for cost efficiency via DeepSeek Sparse Attention, offering cheaper input and cheaper output while using longer thinking time to support deep reasoning and batch or asynchronous workloads.

Performance Benchmarks

Both models support thinking and non-thinking modes with different performance profiles across coding, reasoning, and agentic tasks.

Coding & Instruction Following

Benchmark	GLM-4.7 (non/thinking)	DeepSeek V3.2 (non/thinking)
SciCode	35% / 45%	39% / 39%
IFBench	55% / 68%	49% / 61%
SWE-Bench	73.8%	73.1%

In coding and instruction-following tasks, GLM-4.7 consistently outperforms DeepSeek V3.2 on IFBench and slightly on SWE-Bench, suggesting stronger adherence to complex instructions. DeepSeek V3.2 shows a modest advantage on SciCode, but overall performance remains closely matched between the two models.

Reasoning & Knowledge

Benchmark	GLM-4.7 (non/thinking)	DeepSeek V3.2 (non/thinking)
GPQA Diamond	66% / 86%	75% / 84%
AA-Omniscience Non-Hallucination	8% / 10%	7% / 18%
Humanity’s Last Exam	6.1%/ 25.1%	10.5% / 22.2%

Across reasoning and knowledge benchmarks, DeepSeek V3.2 shows stronger performance on GPQA Diamond and Humanity’s Last Exam, while GLM-4.7 holds a slight edge in non-hallucination precision under certain settings. Overall, the results suggest complementary strengths: DeepSeek leans toward higher reasoning accuracy, whereas GLM demonstrates more stable factual reliability in some cases.

Agentic & Tool Use

Benchmark	GLM-4.7 (non/thinking)	DeepSeek V3.2 (non/thinking)
τ²-Bench Telecom	94% / 96%	79% / 91%
Terminal-Bench Hard	30% / 32%	33% / 36%
GDPval-AA	35% / 35%	20% / 34%

In agentic and tool-use tasks, GLM-4.7 shows a clear advantage on τ²-Bench Telecom and GDPval-AA, indicating stronger reliability in structured tool execution. DeepSeek V3.2 performs slightly better on Terminal-Bench Hard, but overall GLM-4.7 appears more consistent across agent-oriented benchmarks.

Long Context Reasoning

Benchmark	GLM-4.7 (non/thinking)	DeepSeek V3.2 (non/thinking)
AA-LCR	36% / 64%	39% / 65%

DeepSeek V3.2 slightly outperforms GLM-4.7 on AA-LCR (39%/65% vs. 36%/64%) in non-thinking mode. The differences are small, suggesting broadly similar long-context reasoning performance.

Speed & Latency Analysis

Performance speed directly impacts developer productivity in production environments.

	GLM-4.7 (non/thinking)	DeepSeek V3.2 (non/thinking)
Time To First Token	0.68s / 0.78s	1.17s / 1.17s
Thinking Time	— / 14.7s	— / 61.6s
Output Speed	127-136 tok/s	31-32 tok/s

• Latency: GLM-4.7 achieves a noticeably lower time to first token than DeepSeek V3.2, enabling faster initial responses and better interactivity.
• Efficiency: In thinking mode, GLM-4.7 requires significantly less thinking time, indicating more efficient internal computation.
• Throughput: With an output speed of 127–136 tok/s, GLM-4.7 far exceeds DeepSeek V3.2’s 31–32 tok/s, making it better suited for high-throughput scenarios.

Cost Analysis on Novita AI

Cost Component	GLM-4.7	DeepSeek V3.2	Difference
Input	$0.60/M	$0.269/M	55% cheaper
Cache Read	$0.11/M	$0.1345/M	18% more expensive
Output	$2.20/M	$0.40/M	82% cheaper

Token cost comparison:

• DeepSeek V3.2 offers 55% cheaper input and 82% cheaper output processing
• For typical sessions (10K input, 5K output): GLM-4.7 costs $0.017, DeepSeek $0.00469 (72% cheaper)
• Cache read pricing is comparable, with DeepSeek slightly higher ($0.1345 vs $0.11/M)

Pricing about GLM 4.7

Pricing about DeepSeek V3.2

How to Deploy: API, SDK, and Third-Party Integrations

Option A: API

Getting Your API Key on Novita AI

• Step 1: Create or Login to Your Account: Visit https://novita.ai and sign up or log in.
• 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 shown only once.

Call Novita via endpoint

Just change:

• base_url: https://api.novita.ai/openai
• api_key: your Novita key
• model: deepseek/deepseek-v3.2 or zai-org/glm-4.7

from openai import OpenAI

client = OpenAI(
    api_key="<Your API Key>",
    base_url="https://api.novita.ai/openai"
)

response = client.chat.completions.create(
    model="deepseek/deepseek-v3.2",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello, how are you?"}
    ],
    max_tokens=262144,
    temperature=0.7
)

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

Option B: 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 deepseek/deepseek-v3.2 or zai-org/glm-4.7

Option C: Third-Party Platforms

You can also run Novita-hosted 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.

Use Case Recommendations

Choose GLM-4.7 for:

• Interactive coding/IDE assistants (fast: 0.68s first token, 127–136 tok/s generation)
• Production-critical tool use (high reliability: 94–96% on τ²-Bench)
• Frontend/UI work (often cleaner, more aesthetic UI code per community feedback)
• Reasoning with low wait (about 14.7s thinking: good balance for design, reviews, complex features)
• Large codebases (200K context; strong long-context handling, especially non-thinking)

Choose DeepSeek V3.2 for:

• Budget / high-volume workloads (~55% input and ~82% output cost savings)
• Deep reasoning & safety-minded analysis (longer 61.6s thinking; strong long-context reasoning and low hallucination)
• Asynchronous/batch tasks (slower 31–32 tok/s is fine for overnight docs, scheduled analysis, bulk test generation)
• Research/exploration phases where latency matters less than thoroughness

Conclusion

GLM-4.7 and DeepSeek V3.2 optimize for different priorities. GLM-4.7 delivers speed (127-136 tokens/s), stability, and production reliability at higher cost ($2.20/M output). DeepSeek V3.2 provides 82% cost savings and deeper reasoning capabilities (65% long-context, 18% non-hallucination) with slower output (31-32 tokens/s).

Both models are available on Novita AI with competitive pricing, OpenAI-compatible APIs, and full MIT licensing. Novita AI’s infrastructure provides reliable access to both models with caching support and flexible deployment options.

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