GPT-4o vs Mistral Medium 3.5

Choose GPT-4o if you want…

• ✓Faster output (~212.1 tokens/sec)

Choose Mistral Medium 3.5 if you want…

• ✓Higher intelligence (Artificial Analysis index 30)
• ✓Lower price ($3 / 1M blended)
• ✓Larger context window (256K)

GPT-4o

Speed is GPT-4o's defining trait. Where comparable models average 61 tokens per second, GPT-4o delivers nearly 200 — and its native audio pipeline hits 320ms response latency, making it the practical choice for voice interfaces and real-time chat. It also collapses text, image, and audio processing into a single unified model rather than routing across separate systems, which produces more coherent multimodal reasoning without the awkward handoffs. Users feel this difference acutely. When OpenAI tried to retire GPT-4o in early 2026, the backlash was fierce enough to reverse the decision — petitions, mass unsubscribe threats, and user surveys suggesting 95% found no adequate replacement. That kind of loyalty comes from how the model feels in practice: snappy, versatile, fluent across 50+ languages, and capable of web search that reasoning-focused models like o1 lack. The honest caveat: GPT-4o trades raw reasoning depth for speed. It scores below average on Artificial Analysis's Intelligence Index and struggles with complex multi-step logic. For hard reasoning or large-document tasks, newer models outclass it. For fast, general-purpose, multimodal work, few match it.

Mistral Medium 3.5

Mistral Medium 3.5 is Mistral's multimodal flagship on just4o.chat, served through the Vercel AI Gateway as mistral/mistral-medium-3.5. It combines frontier text reasoning with native image understanding in a single model, so you can hand it a screenshot, chart, or document image alongside your prompt and get a grounded answer back. A 256k-token context window handles long inputs comfortably, and function calling makes it suitable for agentic and tool-driven work. It is a Premium model on just4o.chat, billed at 3 premium requests per send before length multipliers, reflecting its higher provider output price of $7.50 per million tokens ($1.50 per million input). The honest trade-offs: no native web search, and as a vision-capable generalist rather than a dedicated reasoning model, it does not expose a step-by-step thinking budget the way Magistral does. For multimodal analysis, visual document work, and high-quality general assistance where image input matters, it is the model to reach for.