Side-by-side sourced facts
Both columns quote the same reviewed Epoch AI catalog artifact. The LMArena row quotes the separately pinned snapshot published July 12, 2026 and appears only for an exact source model match. Missing values stay missing.
| Field |  DeepSeek-V4-Pro |  Pixtral Large |
|---|---|---|
| Developer | DeepSeek | Mistral |
| Released | April 24, 2026 | November 18, 2024 |
| Domains | Language | Multimodal, Language, Vision |
| Tasks | Language modeling/generation, Question answering | Vision-language generation, Visual question answering, Mathematical reasoning, Character recognition (OCR), Language modeling/generation, Question answering |
| Access | Open weights (unrestricted) | Open weights (non-commercial) |
| Weights | Open weights | Open weights |
| LMArena rating | 1457.1 (95% interval 1452.6 to 1461.6, 41,800 battles) | No exact model match in this snapshot |
| Context window | Not reported by this source | Not reported by this source |
| Price | Not reported by this source | Not reported by this source |
Common questions
Is DeepSeek-V4-Pro better than Pixtral Large?
No published ranking covers both models in Model Gauntlet's approved sources: the LMArena snapshot published July 12, 2026 has no exact match for Pixtral Large. The Epoch catalog documents what each model is for; it does not measure which is better.
Which is newer, DeepSeek-V4-Pro or Pixtral Large?
According to Epoch AI's model catalog (retrieved July 14, 2026), DeepSeek-V4-Pro is newer: it was released April 24, 2026, while Pixtral Large was released November 18, 2024.
Can you self-host DeepSeek-V4-Pro or Pixtral Large?
Both, according to the Epoch records: DeepSeek-V4-Pro ships unrestricted open weights and Pixtral Large ships open weights limited to non-commercial use. Read each license before deploying.
Evidence boundary
This page declares no winner beyond what a named source's numbers say. Where the sources do not overlap, the page says so plainly. Task quality, price, context window, latency, and reliability are not reported by these sources, so no claim about them appears here. Both models still require hands-on evaluation on your own workload.