Claude Sonnet 4.6 vs GLM 5.1

Choose Claude Sonnet 4.6 if you want…

• ✓Larger context window (1M)

Choose GLM 5.1 if you want…

• ✓Higher intelligence (Artificial Analysis index 51)
• ✓Faster output (~80.7 tokens/sec)
• ✓Lower price ($2.15 / 1M blended)

Claude Sonnet 4.6

Sonnet 4.6 sits at the sweet spot where coding and agentic work get done without paying Opus prices. On SWE-bench Verified it scores 79.6% — within one point of Opus 4.6 (80.8%) — at roughly a third of the cost, which is why developers running automated pipelines tend to reach for it first. The self-correction training is the headline improvement: when a tool call fails, the model recognizes and recovers rather than cycling through the same error. Users also praise the 1M-token context window for swallowing entire codebases or large document sets in a single pass. The honest caveat is that this context window has edges — retrieval quality degrades on adversarial tests beyond about 700K tokens, so vector-based RAG is still the safer bet for critical long-context searches. Speed is also a known tension: at 44 tokens per second, it runs slower than the median for its tier, which can feel noticeable in real-time applications. Still, for teams that need high-quality code generation, browser automation, and multi-step agentic workflows without Opus-level spend, Sonnet 4.6 is the practical default.

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.