Gemma 4 31B vs GLM 5V Turbo
Compare Gemma 4 31B and GLM 5V Turbo side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.
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Models in this comparison
Gemma 4 31B vs GLM 5V Turbo on Vision Evals
Gemma 4 31B scores higher on 2 of the 4 Vision Evals tasks.
The widest gap is Reasoning, where Gemma 4 31B leads 52.8% to 31.8%.
Overall, Gemma 4 31B averages 57.7% (#37 of 61) against 54.6% (#47 of 61) for GLM 5V Turbo.
Gemma 4 31B is cheaper ($0.0015 vs $0.0037 per sample), while GLM 5V Turbo is faster (5.9s vs 34.4s per sample).
Gemma 4 31B vs GLM 5V Turbo Comparison Table
Evals updated October 8, 2026Pricing updated October 10, 2026
| Property | Gemma 4 31B | GLM 5V Turbo |
|---|---|---|
| Organization | Z.ai | |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Apr 2026 |
| Context Window | 256K | 200K |
| Parameters | 31B | Unknown |
| License | Apache 2.0 | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.090 | $1.20 |
| Output $/1M | $0.340 | $4.00 |
| 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 | 57.7% 4/5 tasks | 54.6% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0015 | $0.0037 |
| Avg speed / sample | 34.36s | 5.89s |
| By task | ||
| Object Detection | 47.5% ±0.6, Mean of 3 runs, range 46.8 to 48.0 | 56.5% |
| Counting | 51.4% ±2.7, Mean of 3 runs, range 48.6 to 54.0 | 48.6% |
| Identification | 79.2% ±1.6, Mean of 3 runs, range 78.1 to 81.3 | 84.4% |
| OCR (low) | – | 51.5% |
| by category |
| |
| OCR (high) | – | 56.0% |
| by category |
| |
| Reasoning (low) | 52.8% ±1.3, Mean of 3 runs, range 51.7 to 54.3 | 31.8% |
| Reasoning (high) | – | 49.7% |
Gemma 4 31B vs GLM 5V Turbo: Overview
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.
For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.
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.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemma 4 31B performed slightly better overall. The two split the 4 vision tasks 2 to 2, but Gemma 4 31B averages 57.7% (#37 of 61) against 54.6% (#47 of 61) for GLM 5V Turbo. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Gemma 4 31B leads with 52.8% against 31.8%. This is the widest gap between the two models across the benchmark's tasks.
Gemma 4 31B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0015 per sample against $0.0037. Actual costs depend on your image sizes, prompts, and output length.
GLM 5V Turbo is faster. Across Roboflow's Vision Evals it averaged 5.9s per inference against 34.4s. 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.