GLM 5.3 Flash vs GPT-6.1 Sol
GPT-6.1 Sol and GLM 5.3 Flash score 81.7% vs 55.8% averaged across five Vision Evals tasks (#9 and #50 of 69 models), so GPT-6.1 Sol leads overall.
- GPT-6.1 Sol wins all 5 tasks, with its widest lead on object detection (80.8% vs 33.1%).
- GLM 5.3 Flash is under a tenth of the cost: $0.0001 vs $0.0068 overall average cost per sample.
- GLM 5.3 Flash is faster: 9.3s vs 12.8s per sample.
Roboflow Vision Evals. Full breakdown below.
Compare GLM 5.3 Flash vs GPT-6.1 Sol live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
GLM 5.3 Flash vs GPT-6.1 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GLM 5.3 Flash vs GPT-6.1 Sol Comparison Table
Evals updated November 12, 2026Pricing updated November 13, 2026
| Property | GLM 5.3 Flash | GPT-6.1 Sol |
|---|---|---|
| Organization | Z.ai | OpenAI |
| Category | open | closed |
| Modality | multimodal | multimodal |
| Release Date | Aug 2026 | Sep 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | 320B total, 18B active | undisclosed |
| License | MIT | Proprietary |
| Commercial use | free for commercial use | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $0.040 | $2.00 |
| Output $/1M | $0.078 | $10.00 |
| Vision Tasks | ||
| Promptable Concept Segmentation | Not listed | Demo |
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 55.8% | 81.7% |
| Avg cost / sample | $0.0001 | $0.0068 |
| Avg speed / sample | 9.29s | 12.82s |
| By task | ||
| Object Detection (low) | 33.1% | 80.8% ±0.1, Mean of 3 runs, range 80.7 to 80.9 |
| Object Detection (high) | – | 81.6% ±0.4, Mean of 3 runs, range 81.1 to 82.0 |
| Counting (low) | 55.4% | 78.8% ±3.4, Mean of 3 runs, range 75.7 to 82.4 |
| Counting (high) | – | 80.2% ±3.4, Mean of 3 runs, range 77.0 to 83.8 |
| Identification (low) | 84.4% | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 55.4% | 75.8% |
| by category |
|
|
| OCR (high) | 55.3% | 75.8% |
| by category |
|
|
| Reasoning (low) | 51.0% | 83.7% ±1.3, Mean of 3 runs, range 82.1 to 84.8 |
| Reasoning (high) | 59.6% | 88.7% ±2.0, Mean of 3 runs, range 87.4 to 91.4 |
GLM 5.3 Flash vs GPT-6.1 Sol: Overview
GLM-5.3-Flash is the first natively multimodal model in Z.ai's GLM-5 series, a mixture-of-experts transformer with roughly 320 billion total parameters and 18 billion activated per token. It routes each token through 8 of 288 experts across 45 language layers that interleave KDA linear attention with sparse multi-head latent attention, and pairs them with a 24-layer vision encoder that handles image and video input. The checkpoint declares a maximum context length of 1,048,576 tokens, ships in native FP8, and includes a multi-token prediction draft layer for speculative decoding. Z.ai reports that the hybrid attention design reduces attention computation by 3.01x and KV cache size by 4.44x relative to GLM-5.3.
The model starts from a newly trained base built on a 30 trillion token multimodal pre-training corpus and adopts Manifold-Constrained Hyper-Connections to improve scaling efficiency. Vision is integrated into the coding and agent loop, so the model can inspect interfaces, rendered output, and images while operating across code, browsers, and graphical user interfaces.
GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.
OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6.1 Sol performed better. It scores higher on all five vision tasks and averages 81.7% (#9 of 69) against 55.8% (#50 of 69) for GLM 5.3 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, GPT-6.1 Sol leads with 80.8% against 33.1%. This is the widest gap between the two models across the benchmark's tasks.
GLM 5.3 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0001 per sample against $0.0068. GLM 5.3 Flash is priced at $0.04 per 1M input tokens and $0.08 per 1M output; GPT-6.1 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
GLM 5.3 Flash is faster. Across Roboflow's Vision Evals it averaged 9.3s per inference against 12.8s. 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.