AI model comparison
GLM 5V Turbo vs GLM 4.7
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
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.
- Input / 1M
- $1.20
- Output / 1M
- $4
- Context
- 203K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
GLM 4.7
1.9x cheaper for a typical chat
Higher intelligence score
GLM 5V Turbo
23.5 vs 22.2 on Artificial Analysis
Larger context window
GLM 4.7
205K vs 203K tokens
Newer release
GLM 5V Turbo
Released April 1, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Coding index
Agentic index
Pricing
GLM 5V Turbo vs GLM 4.7 API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | ||
|---|---|---|
| Input tokensPer 1M tokens | $1.20 | $0.60 |
| Output tokensPer 1M tokens | $4 | $2.20 |
| Cached input (read)Per 1M tokens | $0.24 | $0.11 |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Coding task
30K in, 4K out
Long document summary
150K in, 2K out
Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | ||
|---|---|---|
| Context window | 203K tokens | 205K tokens |
| Max output | 131K tokens | 131K tokens |
| Input types | Image, Text, Video | Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | April 1, 2026 | December 22, 2025 |
Features
Supported features
API features available for each model.
| Metric | ||
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Not supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Temperature | Supported | Supported |
| Stop sequences | Not supported | Supported |
| Deterministic seed | Not supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
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- GLM 4.7 vs GLM 5.1
- GLM 4.7 vs Gemini 3.5 Flash Lite
- GLM 4.7 vs Kimi K2.5
- GLM 4.7 vs Nemotron 3 Ultra
- GLM 4.7 vs Qwen3.5-27B
- GLM 4.7 vs Kimi K2 Thinking
- GLM 4.7 vs Qwen3.5 397B A17B
- GLM 4.7 vs GPT-5 Mini
- GLM 4.7 vs Qwen3.6 27B
- GLM 4.7 vs GPT-5.1-Codex-Mini
- GLM 4.7 vs GLM 4.6
Our take
GLM 4.7 is 1.9x cheaper for a typical chat. GLM 5V Turbo scores higher on the Artificial Analysis Intelligence Index (23.5 vs 22.2). GLM 4.7 has the larger context window (205K vs 203K tokens). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is GLM 5V Turbo or GLM 4.7 cheaper?
GLM 4.7 is cheaper for a typical chat (2K input tokens and 500 output tokens). GLM 4.7 costs $0.60 per 1M input tokens and $2.20 per 1M output tokens, while GLM 5V Turbo costs $1.20 per 1M input tokens and $4 per 1M output tokens.
Which has a bigger context window, GLM 5V Turbo or GLM 4.7?
GLM 4.7 supports up to 205K tokens of context, compared with 203K for GLM 5V Turbo.
Is GLM 5V Turbo smarter than GLM 4.7?
On the Artificial Analysis Intelligence Index, GLM 5V Turbo scores 23.5 and GLM 4.7 scores 22.2, putting GLM 5V Turbo ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use GLM 5V Turbo and GLM 4.7 at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see GLM 5V Turbo and GLM 4.7 answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to GLM 5V Turbo and GLM 4.7 at the same time. Compare the answers side by side and keep the best one.