Updated March 26, 2026· Based on independent benchmark data
GPT-5.3 Codex (xhigh) leads in intelligence with a score of 54.0 vs 30.1. GLM-4.7-Flash (Reasoning) is 25.0x cheaper at $0.07/1M tokens vs $1.75/1M.
| Metric | GLM-4.7-Flash (Reasoning) | GPT-5.3 Codex (xhigh) |
|---|---|---|
| Intelligence Score | 30.1 | 54.0 |
| Coding Score | 25.9 | 53.1 |
| Math Score | N/A | N/A |
| Speed (tok/s) | 86 tok/s | 72 tok/s |
| Latency (TTFT) | 0.68s | 83.91s |
| Input Price / 1M tokens | $0.07 | $1.75 |
| Output Price / 1M tokens | $0.40 | $14 |
| Context Window | N/A |
GPT-5.3 Codex (xhigh) outperforms GLM-4.7-Flash (Reasoning) on the intelligence index with a score of 54.0 compared to 30.1. For coding tasks, GPT-5.3 Codex (xhigh) has the edge with a coding score of 53.1 vs 25.9.
Both models deliver similar output speeds: GLM-4.7-Flash (Reasoning) at 86 tok/s and GPT-5.3 Codex (xhigh) at 72 tok/s. Time to first token is 0.68s for GLM-4.7-Flash (Reasoning) vs 83.91s for GPT-5.3 Codex (xhigh), which affects perceived responsiveness in interactive applications.
GLM-4.7-Flash (Reasoning) is more affordable at $0.07/1M input tokens ($0.40/1M output), while GPT-5.3 Codex (xhigh) costs $1.75/1M input ($14/1M output). That makes GPT-5.3 Codex (xhigh) 25.0x more expensive per token, which can add up significantly at scale. For a typical workload of 100 requests per day at 2,000 tokens each, GLM-4.7-Flash (Reasoning) would cost approximately $0.42/month vs $10.50/month for GPT-5.3 Codex (xhigh) in input costs alone.
Choose GLM-4.7-Flash (Reasoning) when you need lower cost. Choose GPT-5.3 Codex (xhigh) when you need higher intelligence (54.0), stronger coding performance (53.1).
GPT-5.3 Codex (xhigh) scores higher on coding benchmarks (53.1 vs 25.9), making it the better choice for programming tasks.
GLM-4.7-Flash (Reasoning) is cheaper at $0.07/1M input tokens vs $1.75/1M for GPT-5.3 Codex (xhigh).
GLM-4.7-Flash (Reasoning) is faster, producing output at 86 tok/s compared to GPT-5.3 Codex (xhigh)'s 72 tok/s.
No, GLM-4.7-Flash (Reasoning) does not support image input. Neither model supports image input.
Data last synced: March 26, 2026
| N/A |
| Max Output Tokens | N/A | N/A |
| Input Modalities | Text | Text |
| Output Modalities | Text | Text |
| Free Tier | No | No |
It depends on your priorities. GPT-5.3 Codex (xhigh) scores higher on intelligence (54.0), but GLM-4.7-Flash (Reasoning) may be better for specific use cases like budget-conscious projects or speed-critical applications.