TokenMix Research Lab · 2026-06-22

GLM-4.1V-Thinking Review 2026: 9B Open VLM vs Qwen 72B

GLM-4.1V-Thinking Review 2026: 9B Open VLM vs Qwen 72B

Last Updated: 2026-06-22 Author: TokenMix Research Lab Data verified: 2026-06-22 - GLM-4.1V arXiv report (2507.01006 v1), Hugging Face zai-org/GLM-4.1V-9B-Thinking card, GitHub zai-org/GLM-V, OpenRouter listing, SiliconFlow, TokenMix live model API

Zhipu released GLM-4.1V-9B-Thinking on July 1, 2025, a 9B open vision-language model that matches or beats the roughly 8x larger Qwen2.5-VL-72B on 18 of 28 benchmarks, with MIT-licensed weights (GLM-4.1V arXiv v1). It scores 80.7 on MathVista and 57.1 on MMMU-Pro, ahead of GPT-4o on both — but it outputs text only, its served context is 64K, and TokenMix does not relay it, offering GLM-5V-Turbo and Qwen3-VL as managed vision options instead (TokenMix models).

This review focuses on the GLM-4.1V tier specifically and cites the original v1 paper, because the arXiv report was later revised to cover GLM-4.5V and GLM-4.6V with different benchmark counts. Each claim is tagged Confirmed, Likely, or vendor-reported; benchmarks come from the v1 report and the Hugging Face model card, pricing from OpenRouter and SiliconFlow.

Table of Contents

Quick Verdict

GLM-4.1V-9B-Thinking is the strongest small open vision model of its generation: a 9B VLM with "thinking" reasoning that punches far above its weight class, free under MIT. The catches are text-only output, a 64K context, and benchmark numbers that need reading against the right paper version.

Claim Status Source
Released July 1, 2025 Confirmed arXiv v1
~9B params (10B with vision encoder) Confirmed HF model card
Weights MIT, code Apache-2.0 Confirmed HF card, GitHub
Base is GLM-4-9B-0414, vision encoder AIMv2-Huge Confirmed HF model card
Matches/beats Qwen2.5-VL-72B on 18 of 28 benchmarks Confirmed (v1 paper) arXiv v1
MathVista 80.7, MMMU-Pro 57.1 Confirmed arXiv v1
64K served context (32K training window) Likely HF model card
Output is text only (multimodal input) Confirmed GitHub
OpenRouter price ~$0.035 in / $0.138 out per 1M Likely (re-verify) OpenRouter
TokenMix serves GLM-4.1V-Thinking False TokenMix lists GLM-5V-Turbo and Qwen3-VL, not 4.1V (TokenMix models)

The short answer: GLM-4.1V-9B-Thinking is the open VLM to run when you want frontier-adjacent vision reasoning on modest hardware for free. For a managed endpoint, route to a hosted vision model instead.

What GLM-4.1V-Thinking Is

GLM-4.1V-9B-Thinking is a small open vision-language model from Zhipu that adds explicit chain-of-thought reasoning to multimodal tasks. It pairs the GLM-4-9B-0414 language backbone with an AIMv2-Huge vision encoder and was trained with Reinforcement Learning with Curriculum Sampling (RLCS) to reason over images, video, documents, and GUI screens (GLM-4.1V arXiv v1).

There is a version trap to flag up front. The arXiv paper (2507.01006) was later revised through v6 in January 2026 to fold in the bigger GLM-4.5V and GLM-4.6V models, changing the benchmark counts to "42 benchmarks" and "29 vs Qwen2.5-VL-72B." For a GLM-4.1V-specific review, the correct figures are the v1 paper's 18-of-28 and 23-of-28, which this article uses. For the family context, see the GLM models roundup and the broader vision API comparison.

Specifications

The defining spec is the size-to-capability ratio: a ~9B model with a vision encoder that competes with 72B VLMs. Every figure is from the model card or v1 paper.

Field Value Status
Release date 2025-07-01 Confirmed
Parameters 9B backbone (10B with vision encoder) Confirmed
Language base GLM-4-9B-0414 Confirmed
Vision encoder AIMv2-Huge Confirmed
Context window 64K (32K training window) Likely
Input modalities Image (to 4K, any aspect ratio), video, document, GUI Confirmed
Output Text only, bilingual EN/ZH Confirmed
Weights license MIT Confirmed
Code license Apache-2.0 Confirmed

Two clarifications buyers ask about. The parameter count appears as both 9B and 10B because 9B is the language backbone and ~10B counts the vision encoder. The context shows as 64K on the card and 66K on OpenRouter, while the v1 paper mentions a 32K training window — so treat 64K as the served ceiling and 32K as a training detail.

Benchmarks

On the v1 paper's numbers, GLM-4.1V-9B-Thinking genuinely competes with models eight times its size, leading on math-visual and chart reasoning. The table below pits it against Qwen2.5-VL-72B, Qwen2.5-VL-7B, and GPT-4o.

Benchmark GLM-4.1V-9B Qwen2.5-VL-72B Qwen2.5-VL-7B GPT-4o
MMMU (val) 68.0 70.2 58.6 69.1
MMMU-Pro 57.1 51.1 38.3 54.6
MathVista 80.7 74.8 68.2 64.0
MMStar 72.9 70.8 63.9 66.2
MUIRBENCH 74.7 62.9 53.2 69.7
ChartMuseum 48.8 39.6 27.2 42.7
VideoMMMU 61.0 60.2 47.4 61.2
AI2D 87.9 87.6 83.8 84.8
OCRBench 84.2 85.1 84.5 81.1

Source: the GLM-4.1V arXiv v1 report. The headline holds up: a 9B model beating Qwen2.5-VL-72B outright on MathVista (80.7 vs 74.8), MMMU-Pro (57.1 vs 51.1), MMStar, MUIRBENCH, and ChartMuseum is a real result, and the paper claims it leads the larger model on 18 of 28 benchmarks. Where it slips is raw MMMU and OCRBench, where the 72B model holds a small edge. These are vendor-published comparisons, so confirm on your own tasks.

Pricing and Access

GLM-4.1V-9B-Thinking is free to self-host and very cheap to rent, since it is positioned as an open release rather than a first-party paid API. The OpenRouter rate should be re-checked, as the live page renders dynamically.

Access Input / 1M Output / 1M Note
Hugging Face weights $0 API $0 API MIT, plus GGUF/AWQ/GPTQ quants
OpenRouter ~$0.035 ~$0.138 Routed via indie providers, re-verify
SiliconFlow hosted hosted China-region hosting
First-party Zhipu paid API not found not found 4.1V is the open tier

The OpenRouter price of roughly $0.035 input and $0.138 output per 1M tokens came from a search snippet rather than a clean fetch, so verify it live before budgeting. There is no first-party paid Zhipu endpoint for the 4.1V tier that surfaced in this review — it is the open/free release, with hosting handled by third parties and quantized builds for local runners.

Cost vs Managed Vision APIs

For teams that want a managed endpoint instead of self-hosting, the relevant comparison is GLM-4.1V on OpenRouter versus hosted vision models, including ones on TokenMix. A small vision workload of 5M input and 1M output tokens shows the spread.

Model Input / 1M Output / 1M 5M in + 1M out Hosting
GLM-4.1V-9B (OpenRouter) $0.035 $0.138 ~$0.31 self/indie
Qwen3 VL Flash (TokenMix) $0.02 $0.20 ~$0.30 managed
Qwen3 VL Plus (TokenMix) $0.13 $1.33 ~$1.98 managed
GLM-5V-Turbo (TokenMix) $0.66 $2.89 ~$6.19 managed
Qwen2.5 VL 72B (TokenMix) $1.56 $4.68 ~$12.48 managed

A small vision job runs about $0.31 on GLM-4.1V via OpenRouter, essentially tied with the cheapest managed option, Qwen3 VL Flash at roughly $0.30 on TokenMix. The trade is operational: GLM-4.1V via OpenRouter or self-host gives you the specific 4.1V model and MIT weights; a managed route like Qwen3 VL Flash or GLM-5V-Turbo gives you a single OpenAI-compatible endpoint and no infrastructure. Model your own mix with the LLM API cost calculator.

How to Run GLM-4.1V-Thinking

Because the weights are MIT and the model is only ~10B, GLM-4.1V-9B-Thinking runs on a single capable GPU, which is the main reason to choose it over a closed VLM. There are three practical paths.

Path What you get Best for Caveat
Hugging Face weights Full model + Base variant Self-host, research Needs a vision-capable serving stack
Quantized (GGUF/AWQ/GPTQ) Smaller local builds Single-GPU / laptop Quantization quality trade
OpenRouter / SiliconFlow Hosted endpoint Quick test, no infra Re-verify price, indie providers

The model takes images at arbitrary aspect ratio up to 4K resolution, plus video, documents, and GUI screenshots, and emits a reasoning trace before its answer thanks to the "thinking" training. For agent and document workflows that need visual grounding on a budget, that combination of small size, open license, and explicit reasoning is hard to match. If you want the managed-relay pattern instead, the AI API gateway guide covers routing vision models through one endpoint.

Where GLM-4.1V-Thinking Loses

GLM-4.1V-Thinking loses on output modality, context length, and benchmark-version clarity. These are scope limits, not quality flaws.

Weak spot Evidence Pick instead
Text-only output No image/audio generation A generative multimodal model
64K context Card/OpenRouter figure Long-context VLM if needed
Benchmark version confusion v1 vs v6 paper differs Cite v1 for 4.1V specifically
Vendor-published comparisons No third-party replication here Run your own vision eval
Self-host needs a GPU ~10B vision model Managed Qwen3-VL / GLM-5V-Turbo
Small vs newest GLM VLMs GLM-4.5V/4.6V are larger Newer GLM vision for max quality

The pattern is consistent with a small open model: outstanding capability per parameter, with the ceilings you would expect from a 9B VLM. Where you need image generation, very long context, or guaranteed managed uptime, a larger or hosted model fits better. Where you want frontier-adjacent vision reasoning on cheap hardware, GLM-4.1V is a standout.

Use Case Matrix

Point GLM-4.1V-Thinking at budget vision reasoning, documents, and GUI agents; route image generation and managed-uptime needs elsewhere.

Use case GLM-4.1V fit Better alternative Why
Visual math / chart reasoning Strong none on size MathVista 80.7, ChartMuseum 48.8
Document understanding Strong larger VLM if accuracy-critical strong OCR/doc scores
GUI / screen agents Strong specialized agent model trained for GUI grounding
Self-host vision on one GPU Strong quantized smaller VLM MIT, ~10B
Managed, no-infra vision API Medium Qwen3-VL Flash / GLM-5V-Turbo 4.1V not on managed relays like TokenMix
Very long visual context Weak long-context VLM 64K ceiling
Image / video generation Weak a generative model text-only output
Max-quality frontier vision Medium GPT/Gemini/Claude vision small open model ceiling

If your real problem is choosing and routing across many vision and text models rather than one VLM, pair this with the vision API comparison and the QVQ Plus visual reasoning review.

Final Recommendation

Run GLM-4.1V-9B-Thinking when you want frontier-adjacent vision reasoning on a single GPU for free: it leads much larger models on visual math, charts, and several multimodal benchmarks, ships under MIT, and handles images, video, documents, and GUI screens. Use the v1 paper for its specific numbers, self-host the MIT weights or rent it on OpenRouter for the exact model, and route to a managed vision API like Qwen3-VL Flash or GLM-5V-Turbo when you need a no-infrastructure endpoint instead.

FAQ

What is GLM-4.1V-Thinking?

GLM-4.1V-9B-Thinking is an open vision-language model from Zhipu, released July 1, 2025. It adds explicit chain-of-thought reasoning to multimodal tasks, pairing a GLM-4-9B language backbone with an AIMv2-Huge vision encoder, and is MIT-licensed.

Does GLM-4.1V-Thinking really beat Qwen2.5-VL-72B?

On its own v1 paper benchmarks, yes on many tests. The 9B model leads the ~72B Qwen2.5-VL on 18 of 28 benchmarks, including MathVista, MMMU-Pro, MMStar, MUIRBENCH, and ChartMuseum, though Qwen2.5-VL-72B keeps a small edge on raw MMMU and OCRBench. These are vendor-published comparisons.

Is GLM-4.1V-Thinking free?

Yes. The weights are MIT-licensed and free to download and self-host, with the repository code under Apache-2.0. On OpenRouter it lists around $0.035 per 1M input and $0.138 per 1M output, which should be re-verified live.

What can GLM-4.1V-Thinking process?

It takes images at arbitrary aspect ratio up to 4K resolution, plus video, documents, and GUI screenshots. Output is text only, in English and Chinese. It is built for visual reasoning, document understanding, and GUI agents.

What is GLM-4.1V-Thinking's context window?

64K tokens as served (66K on OpenRouter). The v1 paper mentions a 32K training window, so treat 64K as the practical ceiling and 32K as a training detail.

How do I run GLM-4.1V-Thinking?

Download the MIT weights from Hugging Face and serve them on a vision-capable stack, use a quantized GGUF/AWQ/GPTQ build for a single GPU or laptop, or call it via OpenRouter or SiliconFlow for a hosted endpoint. At ~10B it runs on modest hardware.

How much does GLM-4.1V-Thinking cost vs a managed API?

A small vision job (5M input, 1M output) costs about $0.31 on OpenRouter, roughly tied with the cheapest managed option, Qwen3 VL Flash at about $0.30. Managed routes trade a slightly different price for zero infrastructure and one OpenAI-compatible endpoint.

Does TokenMix offer GLM-4.1V-Thinking?

No. TokenMix lists GLM-5V-Turbo from the GLM vision family, plus Qwen3-VL Flash and Plus, Qwen2.5-VL-72B, and QVQ Plus. For GLM-4.1V specifically, self-host the weights or use OpenRouter; for a managed vision endpoint, the Qwen3-VL or GLM-5V-Turbo options are the closest available.

About TokenMix

TokenMix.ai is an AI API relay that routes Claude, OpenAI, Gemini, DeepSeek, Qwen, and other large language models through a single OpenAI-compatible endpoint at https://api.tokenmix.ai/v1. Current model availability and per-token rates are listed on the pricing page and the model catalog. Integration uses the standard OpenAI SDK; details in the OpenAI compatibility reference. GLM-4.1V-Thinking is not in the TokenMix catalog (GLM-5V-Turbo and Qwen3-VL are), so this review is published as independent model intelligence.

Sources

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