Z.ai: GLM 4.5V
GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding,...
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Verdict
Best for
- Budget-conscious image analysis at scale
- Prototyping multimodal applications quickly
- Document OCR with moderate accuracy needs
- Screenshot annotation and UI testing
- Cost-sensitive vision pipelines
Strengths
The pricing structure makes GLM 4.5V one of the cheapest vision-capable models available, enabling high-throughput image analysis without breaking budgets. The 65K context window handles multi-page documents or batches of screenshots in a single call. Z.ai's infrastructure supports both text and image inputs natively, so you avoid the complexity of stitching together separate models for multimodal workflows.
Trade-offs
No public benchmarks means you're flying blind on accuracy relative to GPT-4o, Claude 3.5 Sonnet, or Gemini 1.5 Flash. Early-stage models from smaller vendors often lag on nuanced vision tasks like spatial reasoning, fine-grained object detection, or complex chart interpretation. The 65K window is smaller than Gemini's 1M or Claude's 200K, limiting use cases that require massive context. Expect to run your own evals before production deployment.
Specifications
- Provider
- z-ai
- Category
- llm
- Context length
- 65,536 tokens
- Max output
- 16,384 tokens
- Modalities
- text, image
- License
- proprietary
- Released
- 2025-08-11
Pricing
- Input
- $0.60/Mtok
- Output
- $1.80/Mtok
- Model ID
z-ai/glm-4.5v
Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.
Team cost calculator
5 seats · 80 msgs/day
Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.
Providers
| Provider | Context | Input | Output | P50 latency | Throughput | 30d uptime |
|---|---|---|---|---|---|---|
| z-ai | 66k | $0.60/Mtok | $1.80/Mtok | — | — | — |
Performance
Benchmarks
Works well with
Top MCPs
Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.
How Switchy teams use it
Starter prompts
Extract Invoice Line Items
Extract all line items from this invoice image into a JSON array. For each item, include description, quantity, unit price, and total. Return only valid JSON with no explanation.Open in a Space →
Annotate UI Screenshot
List all clickable UI elements visible in this screenshot. For each, provide the element type (button, link, input field) and the visible label text. Format as a numbered list.Open in a Space →
Summarize Multi-Page PDF
These images show pages from a technical report. Write a 150-word executive summary covering the main findings, methodology, and recommendations. Focus on actionable insights.Open in a Space →
Compare Product Photos
Compare these two product photos. Describe any differences in lighting, composition, background, and product presentation. Note which image is more likely to drive conversions and why.Open in a Space →
Generate Alt Text for Accessibility
Write concise alt text for this image suitable for screen readers. Describe the key visual elements, context, and any text visible in the image. Keep it under 125 characters.Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
Analyze this product mockup image and suggest three specific improvements to the visual hierarchy that would increase conversion rates for an e-commerce listing.
Looking at your mockup, the product gets lost against the busy background. First, increase negative space around the hero image — aim for 40% margins to let the product breathe. Second, your CTA button uses #4A90E2 which has insufficient contrast (WCAG AA fails at 3.2:1). Switch to #2563EB for 4.8:1 contrast. Third, the price typography is undersized at what appears to be 14px. Bump to 24px semibold and position it above the fold, aligned left with the product title. These changes should reduce cognitive load and guide the eye through a clear F-pattern scan path.
Demonstrates GLM 4.5V's multimodal analysis combining visual understanding with domain expertise in UX and accessibility. The 65k context window supports detailed image reasoning. However, at $1.80/Mtok output, this 120-word response costs ~$0.00022 — fine for occasional visual analysis, but expensive for high-volume image processing workflows compared to vision-specialized models.
I have a 40-page technical specification document. Extract all API endpoints, their methods, required parameters, and response codes into a structured markdown table.
Here's the extracted API surface: | Endpoint | Method | Required Params | Response Codes | |----------|--------|-----------------|----------------| | `/api/v2/users` | POST | `email`, `password`, `role` | 201, 400, 409 | | `/api/v2/users/{id}` | GET | `id` (path) | 200, 404 | | `/api/v2/sessions` | POST | `email`, `password` | 200, 401 | | `/api/v2/sessions` | DELETE | `token` (header) | 204, 401 | | `/api/v2/projects` | GET | `page`, `limit` (optional) | 200 | Found 23 total endpoints across 6 resource groups. Three endpoints lack documented error responses — flagged for spec review.
Showcases structured extraction from long documents within the 65k token window — a 40-page spec typically fits in 30-50k tokens. The model correctly formats tabular data and adds editorial notes about spec gaps. Trade-off: at $0.60/Mtok input, processing a 40k-token document costs ~$0.024 per analysis, which adds up for batch document processing jobs.
Debug this Python function. It's supposed to merge two sorted lists but returns incorrect results for lists with duplicate values: `def merge(a, b): result = []; i = j = 0; ...`
The bug is in your duplicate handling. When `a[i] == b[j]`, your current code only appends from `a` and increments `i`, leaving duplicates from `b` behind. Fix: ```python while i < len(a) and j < len(b): if a[i] <= b[j]: # changed from < result.append(a[i]) i += 1 else: result.append(b[j]) j += 1 ``` The `<=` ensures both lists' duplicates get processed. Test case: `merge([1,3,3], [2,3,4])` should return `[1,2,3,3,3,4]`, not `[1,2,3,4]`.
Shows GLM 4.5V handling code debugging with explanation and test cases. The model identifies the logical error and provides a minimal fix. However, without public benchmark scores for HumanEval or MBPP, it's unclear how this model ranks against code-specialized alternatives like Codestral or GPT-4 for complex refactoring tasks.
Use-case deep-dives
When GLM 4.5V makes sense for image-heavy support workflows
A 4-person customer success team handling 200+ product screenshots daily needs to triage bug reports and route them to engineering. GLM 4.5V's $0.60/$1.80 per Mtok pricing undercuts most vision models by 40-60%, making it viable for high-volume image analysis where GPT-4V or Claude would blow the budget. The 65k context window handles multi-image threads without truncation. The trade-off: no public benchmarks means you're flying blind on accuracy until you test it on your own data. If your workflow tolerates a 10-15% miss rate and you can build a feedback loop to catch errors, the cost savings justify the risk. Run a 2-week pilot on 500 real tickets before committing.
GLM 4.5V for extracting tables from scanned invoices at scale
An accounting firm processes 800 vendor invoices monthly, each 2-4 pages of mixed text and tables. GLM 4.5V's 65k token window fits most multi-page PDFs in a single call, and the $0.60 input rate keeps batch processing under $50/month at that volume. Vision models excel at OCR-free extraction from scanned documents, and the price point makes this model competitive with dedicated document AI services. The unknown: without MMMU or DocVQA scores, you can't predict accuracy on complex table layouts or handwritten notes. Test it on 50 representative invoices first. If extraction accuracy hits 90%+ and your team can manually review edge cases, this is the cheapest vision option for document workflows under 2,000 pages/month.
When to pick GLM 4.5V for early-stage multimodal experiments
A 3-person startup is building a home repair assistant that analyzes photos of broken fixtures and suggests fixes. They're pre-revenue and need to validate the concept with 50 beta users before raising a seed round. GLM 4.5V's pricing lets them run 10,000 image queries for under $20, versus $80-120 on established models. The 65k context supports multi-turn conversations with image history. The risk: no benchmark data means the model might hallucinate repair steps or misidentify materials, and you won't know until users complain. For a prototype where you're testing product-market fit and can tolerate rough edges, the 4x cost savings buys you more iteration cycles. Switch to a benchmarked model once you have paying customers and liability concerns.
Frequently asked
Is GLM 4.5V good for general text tasks?
GLM 4.5V handles standard text generation, Q&A, and summarization competently with its 65k context window. Without public benchmarks, it's hard to rank against GPT-4 or Claude, but the multimodal support (text + image) makes it versatile for mixed-content workflows. If you need proven performance metrics, wait for benchmark data or test on your specific use case first.
Is GLM 4.5V cheaper than GPT-4o or Claude Sonnet?
Yes. At $0.60 input / $1.80 output per Mtok, GLM 4.5V undercuts GPT-4o ($2.50/$10.00) and Claude Sonnet 3.5 ($3.00/$15.00) by roughly 75-85%. For high-volume text processing where cost matters more than bleeding-edge quality, this pricing is competitive. Just confirm output quality meets your bar before committing to production scale.
Can GLM 4.5V handle 60k+ token documents in one prompt?
The 65k context window technically fits a 60k document, but real-world performance depends on how the model maintains coherence across that span. Without published long-context benchmarks (like RULER or Needle-in-Haystack scores), assume degradation past 40-50k tokens. Test your actual document types before relying on the full window for critical retrieval or reasoning tasks.
How does GLM 4.5V compare to GLM 4 or earlier versions?
Z.ai hasn't published direct comparisons, and no third-party benchmarks exist yet for GLM 4.5V. The version bump suggests improvements in reasoning or multimodal handling, but without MMLU, HumanEval, or vision benchmark deltas, you're flying blind. If you're already using GLM 4, run A/B tests on your workload before migrating.
Should I use GLM 4.5V for production chatbots?
Only if cost is the primary constraint and you can tolerate unknown latency and quality variance. The lack of public benchmarks means no safety, instruction-following, or refusal-rate data. For customer-facing chat, stick with GPT-4o or Claude until GLM 4.5V publishes performance metrics. For internal tools where you can iterate quickly, it's worth a pilot.