DeepSeek V4 Pro vs Mistral Medium 3.5

Choose DeepSeek V4 Pro if you want…

• ✓Higher intelligence (Artificial Analysis index 44)
• ✓Lower price ($0.54 / 1M blended)
• ✓Larger context window (1.0M)

Choose Mistral Medium 3.5 if you want…

• ✓Faster output (~101.8 tokens/sec)

DeepSeek V4 Pro

DeepSeek V4 Pro makes a compelling case that frontier-class coding performance and a one-million-token context window do not have to cost frontier-class money. The official 0813 release supersedes the preview, adding a DSpark speculative-decoding module and stronger agentic performance in production. On just4o.chat it runs via Fireworks serverless as deepseek-v4-pro-0813. Users consistently praise its agentic coding ability, noting it competes with or beats larger closed models on multi-step coding tasks, and its hybrid attention architecture handles full-codebase analysis without collapsing under the token budget. The MIT license is a genuine differentiator: weights are freely available for self-hosting, fine-tuning, and commercial integration. The honest caveat: V4 Pro is verbose. It can generate four to five times more output tokens than comparable models on the same prompt, which erodes the per-token savings and makes cost estimation harder than it first appears.

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.