DeepSeek V4 Pro vs DeepSeek R1: Should You Still Use R1?

DeepSeek-R1, released January 20, 2025, was the model that put DeepSeek on the map: an open-source reasoning model whose chain-of-thought output shocked the industry at a fraction of the closed-competitor price^[2]. A 0528 checkpoint followed in May 2025^[2].

R1's formula was simple: give the model long chain-of-thought by default and let it think its way through math, logic, and coding problems. It was brilliant and slow — every answer came with a long reasoning trace.

R1 also proved a business model: open weights could match closed reasoning models, and the community could distill the reasoning traces into smaller models. That playbook directly shaped what came after — the V4 family's open weights and visible thinking traces are R1's DNA^[2].

On July 24, 2026, DeepSeek retired the legacy aliases deepseek-chat and deepseek-reasoner^[1][2]. Code that still calls deepseek-reasoner must migrate to deepseek-v4-pro or deepseek-v4-flash — the reasoning experience now lives inside the V4 family^[1].

The retirement is the cleanest statement of the new strategy: reasoning is no longer a separate product line. V4 Pro folds deep reasoning into a general model with an effort dial^[1][3].

# July 24, 2026: legacy aliases stopped resolving[1]
model="deepseek-reasoner"   # ✗ 400 now
model="deepseek-v4-pro"     # ✓ current flagship
model="deepseek-v4-flash"   # ✓ fast lane

# reasoning is now a switch, not a separate model:
reasoning_effort="low" | "high" | "max"

R1 was a dedicated reasoning model: 671B total / 37B active, always-on long chain-of-thought. V4 Pro is a general-purpose 1.6T/49B MoE with reasoning-effort control — you decide how much thinking a request deserves, from low (fast chat) to max (deep proof)^[1][3].

The architecture story is the strategy story: R1 hard-wired thinking; V4 Pro makes thinking a tunable cost. For a simple query you pay Flash-class latency on Pro with reasoning_effort=low; for a hard proof you spend max and get the deep trace back in reasoning_content^[1][3].

Should anyone still use R1 today? For new projects, no — the alias is retired and the reasoning capability moved into the V4 family^[1]. For self-hosted workloads still running R1 weights, the case is marginal: V4 Pro's weights are MIT-licensed and far stronger on coding, while R1 retains one niche — very small self-hosted deployments where 671B is already your ceiling.

R1 earned its place in history, but the July alias retirement closed the book. V4 Pro with reasoning_effort=max is the modern successor — same open-weight ethos, same visible reasoning, dramatically better agentic coding, and one model instead of two product lines^[1][2][4].

If you are still running R1 in production, the migration checklist is short: switch the model string to deepseek-v4-pro (or deepseek-v4-flash for speed), keep your tool-call and JSON-output code paths (both work), re-validate prompts that relied on R1's always-on deep reasoning — many of those can now drop to reasoning_effort=high and save latency — and compare the output quality on your hardest 50 prompts before deleting the R1 pipeline^[1][3].

• New API work → deepseek-v4-pro (or flash) — the reasoning dial covers R1's use case^[1].
• Chain-of-thought visibility → keep reasoning_content; it's still returned in thinking mode^[3].
• Self-hosted legacy R1 → fine to keep, but plan migration to V4 Pro weights for coding tasks.
• Budget → Pro at $0.66/$1.98 per 1M off-peak (after 8/16) beats R1-era pricing per token and delivers far more capability per token^[5].