Claude Sonnet 4.6 vs GPT-5

Choose Claude Sonnet 4.6 if you want…

• ✓Larger context window (1M)

Choose GPT-5 if you want…

• ✓Higher intelligence (Artificial Analysis index 45)
• ✓Faster output (~100.1 tokens/sec)
• ✓Lower price ($3.44 / 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.

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.