GLM 5.1 vs GPT-5

Choose GLM 5.1 if you want…

• ✓Higher intelligence (Artificial Analysis index 51)
• ✓Lower price ($2.15 / 1M blended)

Choose GPT-5 if you want…

• ✓Faster output (~100.1 tokens/sec)
• ✓Larger context window (400K)

GLM 5.1

GLM-5.1 from Z.ai is built for one thing above all else: software engineering that runs on its own. A 754-billion parameter Mixture-of-Experts model, it tops the SWE-Bench Pro leaderboard at 58.4%, edging out both GPT-5.4 and Claude Opus 4.6 on real-world coding tasks. What sets it apart in practice is stamina — it can pursue a single engineering goal autonomously for up to eight hours, sustaining hundreds of iterations and thousands of tool calls without human intervention. Users consistently praise this long-horizon execution for agent-based workflows where other models stall. It also delivers fast responses, with a time-to-first-token of 1.33 seconds against a class median of 2.37 seconds. The honest trade-off: GLM-5.1 accepts text only, with no image input, making it a poor fit for visual debugging or UI-centric tasks. It also tends toward verbosity in practice, which can inflate token costs. For teams building autonomous coding pipelines, though, it earns its place at the top of the leaderboard.

GPT-5

GPT-5 is OpenAI's unified flagship — a single model that scales its reasoning effort up or down to match the task, rather than making you pick a separate variant. The result shows up most clearly in hard technical work: a perfect AIME 2025 score with tools, 74.9% on SWE-bench Verified, and a 1.6% error rate on medical benchmark HealthBench make it one of the more capable models available for code, math, and domain-specific research. Its 400,000-token context window handles large codebases and lengthy documents without truncation. Users consistently praise the step-up in accuracy and the meaningful reduction in hallucinations over GPT-4o. The honest caveat: GPT-5 trades warmth for precision. Early adopters widely noted that responses are shorter, cooler, and noticeably less conversational than its predecessor — a real shift if personality and back-and-forth rapport matter to your workflow. Latency is also substantial; extended reasoning produces a time-to-first-token around 68 seconds, which rules it out for anything requiring snappy replies.