Z.ai: GLM 5V Turbo
GLM-5V-Turbo is Z.ai’s first native multimodal agent foundation model, built for vision-based coding and agent-driven tasks. It natively handles image, video, and text inputs, excels at long-horizon planning, complex coding,...
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Verdict
Best for
- Long-context multimodal document analysis
- Video content summarization and extraction
- Cost-sensitive vision tasks with text
- Processing large PDFs with embedded images
- Exploratory work on emerging Chinese models
Strengths
The 202K context window is roughly 2.5× larger than GPT-4o's 128K, giving you room to process entire reports, transcripts, or video frames in a single call. Pricing undercuts OpenAI's multimodal tier by about 40% on input tokens. Native video support means you can pass video files directly rather than extracting frames manually, streamlining workflows for content moderation or media analysis.
Trade-offs
No public benchmarks means you're flying blind on instruction-following quality, reasoning depth, and vision accuracy relative to GPT-4o or Gemini 1.5 Pro. Z.ai is a newer provider with less ecosystem maturity—expect fewer integrations and less community troubleshooting. The model likely skews toward Chinese-language training data, which may hurt performance on English-only or Western-context tasks compared to Anthropic or OpenAI models.
Specifications
- Provider
- z-ai
- Category
- llm
- Context length
- 202,752 tokens
- Max output
- 131,072 tokens
- Modalities
- image, text, video
- License
- proprietary
- Released
- 2026-04-01
Pricing
- Input
- $1.20/Mtok
- Output
- $4.00/Mtok
- Model ID
z-ai/glm-5v-turbo
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 | 203k | $1.20/Mtok | $4.00/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
Summarize Long Video
Watch this video and provide a structured summary with timestamps for each major topic discussed. Include any on-screen text or visual elements that support the narrative.Open in a Space →
Analyze PDF with Charts
Review this PDF and extract all quantitative findings. For each chart or table, describe what it shows and how it supports the document's conclusions.Open in a Space →
Compare Product Screenshots
Compare these five product screenshots. Identify differences in layout, feature placement, and visual hierarchy. Recommend which approach best serves user goals.Open in a Space →
Extract Meeting Notes from Recording
Generate meeting notes from this video recording. Include action items, decisions made, and any slides or documents shown on screen.Open in a Space →
Audit Long-Form Content
Audit this 50-page document for internal consistency. Flag any contradictions between text claims and supporting images, charts, or tables.Open in a Space →
Example outputs
Illustrative - representative of the model's voice and quality, not literal recordings.
Analyze this product dashboard screenshot and suggest three UX improvements that would reduce user drop-off during onboarding.
Looking at your dashboard, I can identify three friction points. First, the primary CTA ('Get Started') competes visually with secondary actions—consider elevating it with stronger contrast and positioning. Second, the progress indicator at top shows 5 steps but doesn't communicate time investment, which creates uncertainty. Adding estimated completion time ('~3 minutes remaining') reduces abandonment. Third, the left sidebar exposes advanced features before core setup is complete, creating cognitive overload. Collapse or hide these until the user completes their first workflow. These changes address the typical 40-60% drop-off window in SaaS onboarding.
This example demonstrates the model's multimodal analysis capability—processing visual UI elements alongside product strategy context. The 202k token window supports including multiple screenshots plus design system documentation in one prompt. However, without public benchmarks, we can't compare its visual reasoning accuracy against GPT-4V or Claude 3.5 Sonnet on standardized tasks.
Watch this 90-second product demo video and write a technical FAQ covering the three questions enterprise buyers will ask first.
**FAQ: Enterprise Deployment** **Q: What's your data residency story?** A: We support single-tenant deployment in AWS (us-east-1, eu-west-1, ap-southeast-1) and Azure (EastUS, WestEurope). Data never leaves your selected region. On-premise deployment available for Financial Services and Healthcare verticals. **Q: How does SSO integration work?** A: Native SAML 2.0 and OIDC support. We've pre-built connectors for Okta, Azure AD, and Google Workspace. Custom IdP integration typically takes 2-3 days with our solutions team. **Q: What's the user provisioning model?** A: SCIM 2.0 for automated provisioning/deprovisioning. Supports group-based access control and just-in-time provisioning for zero-touch onboarding.
Showcases video comprehension combined with domain knowledge synthesis—the model extracts technical details from a demo and maps them to buyer concerns. The large context window means you can include full product specs, competitor comparison docs, and the video transcript in one prompt. Trade-off: at $4/Mtok output, generating long-form content from video analysis costs more than text-only models.
Here's a 50-page technical specification for our API. Generate a migration guide for developers moving from v2 to v3, focusing on breaking changes.
**Breaking Changes: v2 → v3 Migration** **Authentication:** Bearer tokens now require `X-API-Version: 3` header. v2 tokens expire after 90 days; generate new credentials via `/auth/v3/token`. **Pagination:** Cursor-based pagination replaces offset/limit. Update calls from `?page=2&limit=50` to `?cursor=eyJ...&size=50`. Response shape changes: `data.items` becomes `data.results`. **Webhook signatures:** HMAC-SHA256 replaces SHA1. Verify using `X-Signature-V3` header. See security docs for implementation. **Rate limits:** Now per-endpoint (was global). `/search` limited to 100 req/min; `/data` to 500 req/min. 429 responses include `Retry-After` header. **Estimated migration time:** 4-8 hours for typical integration. Test in sandbox environment first.
Highlights the model's strength with long technical documents—202k tokens accommodates entire API specs, changelogs, and example codebases in context. This enables accurate cross-referencing of breaking changes across versions. The output demonstrates structured technical writing, though without code-specific benchmarks (HumanEval, MBPP), we can't quantify its accuracy on implementation details versus specialized code models.
Use-case deep-dives
When you need one model to handle PDFs, screenshots, and video clips
A 4-person consulting shop sends weekly client reports that mix slide decks, annotated screenshots, and short screen recordings. GLM 5V Turbo handles all three modalities in a single 200k+ token context window, so you can drop the full deliverable package into one prompt and ask for consistency checks, tone analysis, or executive summaries without switching tools. At $1.20 input per Mtok, a typical 80k-token review (20 slides, 15 images, 2 minutes of video transcription) costs under $0.10. The lack of public benchmarks means you're trusting Z.ai's internal evals, but if your workflow already juggles image-only and text-only models, consolidating to one multimodal call cuts API overhead and simplifies your pipeline. Worth a pilot if you're processing 50+ mixed-media packages per month.
Reviewing hour-long webinar recordings for compliance in one pass
A 10-person EdTech team records customer training sessions and needs to flag mentions of competitors, pricing leaks, or off-brand language before publishing. GLM 5V Turbo's 200k context window fits a full 60-minute video transcript plus frame samples in a single call, so you can run one moderation prompt instead of chunking the video into 10-minute segments and stitching results. At $4.00 output per Mtok, a 5k-token moderation report costs $0.02; the real cost is the input token load from video frames, which can hit 150k tokens for a dense hour. If you're moderating fewer than 20 videos per week, the per-call simplicity beats the cost of a dedicated video-analysis pipeline. Above that volume, benchmark the token-per-frame ratio against a vision-specialist model to confirm you're not overpaying for convenience.
When you need to diff three 40-page agreements in one context
A 3-person legal ops team receives vendor contracts as scanned PDFs and needs to compare terms across the current agreement, the renewal proposal, and a reference template. GLM 5V Turbo's 200k-token window holds all three documents as images (roughly 60k tokens per 40-page PDF at standard resolution), letting you run a single prompt that asks for clause-by-clause diffs, missing obligations, or pricing changes. The $1.20 input rate makes a 180k-token comparison cost $0.22, cheaper than paying a paralegal for 15 minutes of manual review. The trade-off: without public benchmarks on legal-document accuracy, you'll need to spot-check the first 10 comparisons against human review to confirm the model catches material changes. If it holds up, you've collapsed a 2-hour task into a 5-minute API call.
Frequently asked
Is GLM 5V Turbo good for multimodal tasks?
Yes, GLM 5V Turbo handles text, image, and video inputs, making it suitable for document analysis, visual Q&A, and video understanding tasks. The 202k context window lets you process long documents with embedded images or multiple video frames in a single request. Without public benchmarks, you'll need to test it against your specific use case to verify quality.
Is GLM 5V Turbo cheaper than GPT-4o or Claude Sonnet?
GLM 5V Turbo costs $1.20 input and $4.00 output per million tokens. That's significantly cheaper than GPT-4o ($2.50/$10.00) and Claude Sonnet 3.5 ($3.00/$15.00) for output-heavy workloads. If you're generating long responses or summaries from multimodal inputs, the savings add up quickly. Input costs are comparable across all three.
Can it handle 200k tokens in practice?
The 202k context window is large enough for full codebases, long research papers with images, or hour-long video transcripts. Real-world performance depends on how the model handles attention across that span—some models degrade at the edges. Test with your actual content length to confirm retrieval accuracy and response quality at maximum capacity.
How does GLM 5V Turbo compare to GPT-4 Vision?
Without published benchmarks, direct quality comparison is difficult. GLM 5V Turbo offers a larger context window (202k vs GPT-4V's 128k) and lower pricing, especially for output tokens. GPT-4 Vision has proven performance across standard vision benchmarks. If cost and context length matter more than established track record, GLM 5V Turbo is worth testing.
Should I use this for video analysis workflows?
GLM 5V Turbo's video support and 202k context make it viable for frame extraction, scene detection, or transcript-plus-visual analysis. The $4.00/Mtok output pricing keeps costs reasonable for generating detailed summaries. Test latency and accuracy against your video length and frame rate requirements—video processing can push context limits and response times quickly.