GLM 5.3 Flash vs GPT-6 Sol
GPT-6 Sol and GLM 5.3 Flash score 76.9% vs 55.8% averaged across five Vision Evals tasks (#15 and #49 of 68 models), so GPT-6 Sol leads overall.
- GPT-6 Sol wins all 5 tasks, with its widest lead on object detection (74.7% vs 33.1%).
- GLM 5.3 Flash is under a tenth of the cost: $0.0001 vs $0.0074 overall average cost per sample.
- GLM 5.3 Flash is faster: 9.3s vs 10.2s per sample.
Roboflow Vision Evals. Full breakdown below.
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GLM 5.3 Flash vs GPT-6 Sol on Vision Evals
Roboflow Vision Evals. Full breakdown below.
GLM 5.3 Flash vs GPT-6 Sol Comparison Table
Evals updated November 10, 2026Pricing updated November 11, 2026
| Property | GLM 5.3 Flash | GPT-6 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% | 76.9% |
| Avg cost / sample | $0.0001 | $0.0074 |
| Avg speed / sample | 9.29s | 10.20s |
| By task | ||
| Object Detection (low) | 33.1% | 74.7% ±0.7, Mean of 3 runs, range 74.1 to 75.5 |
| Object Detection (high) | – | 76.4% ±1.2, Mean of 3 runs, range 75.0 to 77.4 |
| Counting (low) | 55.4% | 76.6% ±2.0, Mean of 3 runs, range 74.3 to 78.4 |
| Counting (high) | – | 77.5% ±1.3, Mean of 3 runs, range 75.7 to 78.4 |
| Identification (low) | 84.4% | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 |
| Identification (high) | – | 92.7% ±1.6, Mean of 3 runs, range 90.6 to 93.8 |
| OCR (low) | 55.4% | 66.7% |
| by category |
|
|
| OCR (high) | 55.3% | 68.8% |
| by category |
|
|
| Reasoning (low) | 51.0% | 72.6% ±3.6, Mean of 3 runs, range 69.5 to 76.8 |
| Reasoning (high) | 59.6% | 77.7% ±1.0, Mean of 3 runs, range 76.8 to 78.8 |
GLM 5.3 Flash vs GPT-6 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 Sol is a proprietary multimodal reasoning model from OpenAI, released on September 22, 2026 alongside GPT-6 Luna as an efficiency-oriented tier of the GPT-6 family that began with GPT-6 Astra. OpenAI states that Sol and Luna are trained with methods similar to those used for Astra, carrying the same work on professional tasks, factuality, coding, computer use, and alignment into models that run faster. Sol accepts text and image input and returns text output, and OpenAI documents a context window of roughly one million tokens together with a knowledge cutoff of April 20, 2026.
The model targets complex coding and agentic workflows and exposes a configurable reasoning effort setting with levels of none, low, medium, high, xhigh, and max, which trades latency and token consumption against answer quality. Its vision behavior covers image understanding tasks such as visual question answering, captioning, document and chart interpretation, and text recognition.
Frequently Asked Questions
On Roboflow's Vision Evals, GPT-6 Sol performed better. It scores higher on all five vision tasks and averages 76.9% (#15 of 68) against 55.8% (#49 of 68) 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 Sol leads with 74.7% 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 $0.0074. GLM 5.3 Flash is priced at $0.04 per 1M input tokens and $0.08 per 1M output; GPT-6 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 10.2s. 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.