TokenMix Research Lab · 2026-04-25

glm-4.1v-9b-thinking & glm-4.5-flash: Zhipu Model Roundup (2026)

glm-4.1v-9b-thinking & glm-4.5-flash: Zhipu Model Roundup (2026)

Last Updated: 2026-04-25
Author: TokenMix Research Lab

Zhipu's GLM family competes across vision-language and fast-tier segments with a mix of open-weight and hosted offerings. GLM-4.1V-9B-Thinking is a 9B vision-language reasoning model that matches or surpasses the much larger Qwen-2.5-VL-72B on 18 benchmark tasks — remarkable efficiency. GLM-4.5-Flash sits in Zhipu's cost-optimized tier. This guide covers both, plus the newer GLM-4.5V (106B) and GLM-5.1 (current flagship at $0.45/$1.80 per MTok), giving a complete picture of Zhipu's 2026 offering. All data verified against Zhipu's open platform and Hugging Face releases as of April 2026.

Table of Contents

Zhipu GLM Family Overview

Zhipu AI (智谱AI) is one of China's top LLM labs, spun out of Tsinghua University. Their GLM family covers:

Strategic positioning: Zhipu competes with Qwen, DeepSeek, Moonshot (Kimi) in the Chinese open-source ecosystem while pushing into vision-reasoning specifically.

GLM-4.1V-9B-Thinking Deep Dive

A 9-billion-parameter vision-language model that introduces a reasoning paradigm via RLCS (Reinforcement Learning with Curriculum Sampling).

Key attributes:

Attribute Value
Creator Zhipu AI (zai-org)
Base model GLM-4-9B-0414
Parameters 9B
Specialty Vision-language reasoning
Context 32K-128K typical
License Open-source
Benchmark highlight Strongest 10B-level VLM; matches/surpasses Qwen-2.5-VL-72B on 18 tasks

Why it matters: demonstrates that careful RL training on a 9B foundation can match larger VLMs. For teams wanting vision capability on consumer hardware, GLM-4.1V-9B-Thinking is a strong candidate.

Typical use cases:

GLM-4.5 Series (4.5V, 4.5-Flash)

The GLM-4.5 generation builds on lessons from 4.1V with improved scale and capability.

GLM-4.5V (106B):

GLM-4.5-Flash:

GLM-4.6V (106B-A12B):

GLM-5.1 (Current Flagship)

Zhipu's current frontier model (as of April 2026):

GLM-5.1 is notable for leading SWE-Bench Pro, a harder coding benchmark where even GPT-5.5 (58.6%) and Claude Opus 4.7 (64.3%) trail. The 70% score surprised many in the industry — proving open-source can lead on specific benchmarks even against frontier closed models.

For code generation workloads where SWE-Bench Pro-style benchmarks matter, GLM-5.1 is the leader. For general tasks, it's strong but competitive with peers.

Pricing Summary

Current pricing landscape across GLM tiers (April 2026):

Model Input / MTok Output / MTok Notes
GLM-4.6V-Flash (9B) FREE FREE Open weights, commercial
GLM-4.6V (106B-A12B) ~$0.14 ~$0.42 Vision-language
GLM-4.5-Flash Lower tier Lower tier Cost-optimized
GLM-4.1V-9B-Thinking Open-weight (self-host) Open-weight Vision reasoning
GLM-5.1 $0.45 $1.80 Flagship, SWE-Bench Pro leader

For exact current pricing, check bigmodel.cn or your aggregator provider.

Supported LLM Providers and Model Routing

GLM models are accessible via:

Through TokenMix.ai, GLM models (including GLM-5.1 the flagship, GLM-4.5V, GLM-4.1V-Thinking) are accessible alongside Kimi K2.6, DeepSeek V4-Pro, qwen3-next-80b, QwQ-32B, Claude Opus 4.7, GPT-5.5, and 300+ other models through a single OpenAI-compatible API key. Useful for Chinese model comparison workflows.

Basic usage:

from openai import OpenAI

client = OpenAI(
    api_key="your-tokenmix-key",
    base_url="https://api.tokenmix.ai/v1",
)

# Flagship reasoning + coding
response = client.chat.completions.create(
    model="glm-5.1",
    messages=[{"role": "user", "content": "Complex coding task"}],
)

# Vision-language
vision_response = client.chat.completions.create(
    model="glm-4.5v",
    messages=[{"role": "user", "content": [
        {"type": "text", "text": "Describe this chart"},
        {"type": "image_url", "image_url": {"url": "..."}},
    ]}],
)

When to Use Which GLM

Your need Pick
Best SWE-Bench Pro coding GLM-5.1
Frontier reasoning at cheap tier GLM-5.1 ($0.45 input)
Vision-language with reasoning GLM-4.5V (106B) or GLM-4.6V
Open-weight vision-language on single GPU GLM-4.1V-9B-Thinking
Free for commercial vision use GLM-4.6V-Flash (9B)
Fast cost-optimized general chat GLM-4.5-Flash or Zhipu's latest flash tier
Self-hosted vision on consumer hardware GLM-4.1V-9B-Thinking (4-bit on RTX 4090)

vs Qwen-VL, Kimi, DeepSeek VL

Chinese vision-language landscape:

Model Parameters Vision Context Open-weight Pricing
GLM-4.1V-9B-Thinking 9B 32K-128K Yes Self-host / low
GLM-4.5V 106B — Yes ~$0.14 / ~$0.42
GLM-4.6V-Flash (9B) 9B — Yes, free FREE
Qwen3-VL-72B 72B 128K Yes Low
QVQ Max (Alibaba) — — Partial TBD
DeepSeek does not have VL as of April 2026 — — — —

For text reasoning:

Model SWE-Bench Pro Input / MTok
GLM-5.1 70% $0.45
Claude Opus 4.7 64.3% $5.00
GPT-5.5 58.6% $5.00
Kimi K2.6 58.6% $0.60
DeepSeek V4-Pro ~55% $1.74

GLM-5.1 genuinely leads on this specific benchmark at a fraction of frontier pricing.

Known Limitations

1. Ecosystem less global than OpenAI/Anthropic. Zhipu is primarily Chinese-market focused. English documentation thinner.

2. API latency from outside China. Route through aggregators or multi-region providers for better latency.

3. Open-weight GLM-4.1V is vision-only reasoning. For pure text reasoning at small scale, QwQ-32B or ERNIE-4.5-21B-Thinking may fit better.

4. Commercial licensing nuances. Check specific license terms for your use case — not all GLM variants have identical licensing.

5. SWE-Bench Pro leadership is specific. GLM-5.1 leads SWE-Bench Pro but not all coding benchmarks. Verify on your workload.

6. Version confusion. GLM-4.1V vs 4.5V vs 4.6V vs 5.1 naming is confusing. Pay attention to which tier fits your needs.

FAQ

What's the difference between GLM-4.1V and GLM-4.5V?

GLM-4.1V is 9B parameters (smaller, runnable on single GPU). GLM-4.5V is 106B (larger, better performance on benchmarks). Both are vision-language; 4.5V is the scale-up.

Is GLM-4.6V-Flash really free?

Yes, Zhipu offers GLM-4.6V-Flash (9B) free for API calls, with open weights available. Commercial use allowed. Unusual generosity — useful for development and small production.

How does GLM-5.1 lead SWE-Bench Pro with just $0.45 input pricing?

Zhipu has invested heavily in coding-focused training. SWE-Bench Pro specifically measures harder, less-saturated problems where RL-trained models tend to excel. GLM-5.1 optimization happened to target this benchmark category well.

Can I self-host GLM-5.1?

GLM-5.1 weights availability depends on specific release terms. GLM-4.5V and smaller open-weight variants are downloadable. Check bigmodel.cn for current status.

Is Zhipu's English documentation good enough?

Adequate but less comprehensive than OpenAI or Anthropic. Community resources (DEV.to articles, Hugging Face discussions) fill gaps.

How does GLM-4.1V-9B-Thinking compare to Qwen-2.5-VL-7B?

Similar size, different approaches. GLM-4.1V uses reasoning paradigm; Qwen-VL is more general-purpose. GLM-4.1V claims benchmark leadership in its size class.

What's the best GLM for coding specifically?

GLM-5.1 — 70% SWE-Bench Pro leads the industry.

Where can I compare GLM-5.1 against Claude Opus 4.7 for coding?

TokenMix.ai provides unified access to GLM-5.1, Claude Opus 4.7, GPT-5.5, DeepSeek V4-Pro, and 300+ other models — run the same coding challenges, measure cost-per-task and quality across providers.

Does GLM support MCP?

Via standard OpenAI-compatible tool calling interfaces. Not native MCP protocol but MCP-compatible when accessed via OpenAI-compatible endpoints.

Will Zhipu release GLM-6?

Active development; no announced timeline. Monitor Zhipu's official channels for next-generation announcements.

Related Articles

Author: TokenMix Research Lab | Last Updated: April 25, 2026 | Data Sources: Zhipu AI Open Platform pricing, zai-org GLM-V GitHub, GLM-4.1V-9B-Thinking Hugging Face, GLM-4.5 GitHub, GLM-4.1V/4.5V research paper arXiv, TokenMix.ai GLM models access