GLM 5V Turbo vs GPT-5.4 Mini
Compare GLM 5V Turbo and GPT-5.4 Mini side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.
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Models in this comparison
GLM 5V Turbo vs GPT-5.4 Mini on Vision Evals
GLM 5V Turbo scores higher on 3 of the five Vision Evals tasks.
The widest gap is Object Detection, where GLM 5V Turbo leads 56.5% to 15.8%.
Overall, GLM 5V Turbo averages 54.6% (#47 of 61) against 53.2% (#49 of 61) for GPT-5.4 Mini.
GPT-5.4 Mini is both cheaper ($0.0034 vs $0.0037 per sample) and faster (5.5s vs 5.9s per sample).
GLM 5V Turbo vs GPT-5.4 Mini Comparison Table
Evals updated October 8, 2026Pricing updated October 10, 2026
| Property | GLM 5V Turbo | GPT-5.4 Mini |
|---|---|---|
| Organization | Z.ai | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Mar 2026 |
| Context Window | 200K | 400K |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $1.20 | $0.750 |
| Output $/1M | $4.00 | $4.50 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 54.6% | 53.2% |
| Avg cost / sample | $0.0037 | $0.0034 |
| Avg speed / sample | 5.89s | 5.51s |
| By task | ||
| Object Detection (low) | 56.5% | 15.8% ±0.4, Mean of 3 runs, range 15.3 to 16.1 |
| Object Detection (high) | – | 16.6% ±0.8, Mean of 3 runs, range 15.8 to 17.4 |
| Counting (low) | 48.6% | 58.6% ±2.0, Mean of 3 runs, range 56.8 to 60.8 |
| Counting (high) | – | 64.9% ±2.0, Mean of 3 runs, range 63.5 to 67.6 |
| Identification (low) | 84.4% | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | – | 82.3% ±3.1, Mean of 3 runs, range 78.1 to 84.4 |
| OCR (low) | 51.5% | 51.1% |
| by category |
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| OCR (high) | 56.0% | 55.8% |
| by category |
|
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| Reasoning (low) | 31.8% | 57.0% ±3.3, Mean of 3 runs, range 54.3 to 60.9 |
| Reasoning (high) | 49.7% | 64.0% ±1.3, Mean of 3 runs, range 62.9 to 65.6 |
GLM 5V Turbo vs GPT-5.4 Mini: Overview
GLM-5V-Turbo is a native multimodal model from Z.ai that extends the GLM family with joint image, video, and text input aimed at vision-centered coding and agent workflows. The model reads screenshots, design drafts, document layouts, and interface captures and generates runnable code from them, covering tasks such as turning a visual design into a working front end, diagnosing rendering and layout defects from screen captures, and operating graphical user interfaces during long-horizon agent runs. It accepts roughly 200,000 input tokens and can emit up to 131,072 output tokens in a single response, which supports sessions that hold specifications, source files, logs, and visual references at the same time.
Training includes a joint reinforcement learning stage spanning more than 30 tasks simultaneously, an approach Z.ai describes as a way to counter the trade-off in which improving visual recognition degrades programming ability and the reverse. Reported evaluations cover pure-text coding on the backend, frontend, and repository exploration tracks of CC-Bench-V2, together with agent execution suites such as PinchBench, ClawEval, and ZClawBench, indicating that text coding behavior is retained after visual input is added.
GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.
Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.
Frequently Asked Questions
On Roboflow's Vision Evals, GLM 5V Turbo performed better. It scores higher on 3 of the five vision tasks and averages 54.6% (#47 of 61) against 53.2% (#49 of 61) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Object Detection benchmark at low effort, GLM 5V Turbo leads with 56.5% against 15.8%. This is the widest gap between the two models across the benchmark's tasks.
GPT-5.4 Mini is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0034 per sample against $0.0037. GLM 5V Turbo is priced at $1.20 per 1M input tokens and $4.00 per 1M output; GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.5s per inference against 5.9s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.