DataLLM LabDeepSeek R1 vs OpenAI o1: The Reasoning Showdown & 2026 Successors
DeepSeek R1 vs OpenAI o1 was the matchup that reset expectations for reasoning models: R1 matched o1 on many reasoning benchmarks while being open-weights and roughly 25-30x cheaper. This guide recaps the benchmarks, models the price gap that made headlines, explains what each represented and why it mattered — and, importantly, points you to the 2026 successors you should actually deploy today.
The short answer
R1 matched o1 on much of reasoning, open and ~25-30x cheaper — but both are now prior-gen. The R1-vs-o1 moment proved open reasoning could rival the frontier at a fraction of the cost. For a build today, use their successors (DeepSeek V3.2/V4, GPT-5 with effort), not R1 or o1 directly.
Side by side
| DeepSeek R1 | OpenAI o1 | |
|---|---|---|
| Released | Jan 2025 | Late 2024 |
| Reasoning benchmarks | Competitive with o1 | Frontier at launch |
| Launch price (in/out) | ~$0.55 / $2.19 | ~$15 / $60 |
| Open weights | Yes (MIT) | No (API-only) |
| Distilled small models | Yes (run locally) | No |
| Status in 2026 | Folded into V3.2 / V4 | Superseded by GPT-5 |
The price gap
The number that made headlines: R1 delivered o1-class reasoning at roughly 1/27th the output price.
The cost gap, modeled
At launch prices, the difference compounds into staggering monthly numbers — with the 2026 successors shown for context:
| Monthly workload | OpenAI o1 | DeepSeek R1 | GPT-5.4 (now) | DeepSeek V3.2 (now) |
|---|---|---|---|---|
| Support chatbot | $1,320 | $48.3 | $280 | $13.3 |
| RAG / knowledge base | $4,200 | $154 | $800 | $52.8 |
| Coding agent | $2,700 | $98.8 | $575 | $26.9 |
| Batch extraction | $2,730 | $100 | $495 | $37.2 |
| Content generation | $2,700 | $98.6 | $650 | $18.2 |
A RAG workload on o1 would have cost about $4,200/month versus $154 for R1 — and today's DeepSeek V3.2 does it for ~$53. The trend the R1 moment kicked off — capable reasoning getting an order of magnitude cheaper — has only continued.
How they compared
R1 was competitive with o1 on the reasoning benchmarks that defined the category — close on competition math (AIME), strong on MATH, and capable on coding-reasoning tasks. o1 kept an edge on some of the very hardest problems, but the gap was narrow enough that for most real workloads R1 was a genuine substitute. The distilled R1 variants — smaller models trained on R1's reasoning traces — extended that capability down to sizes you could run on a single GPU, which broadened the impact well beyond the flagship.
Why it mattered
R1 showed that strong chain-of-thought reasoning wasn't an exclusively closed, expensive capability. By open-sourcing competitive reasoning — including distilled smaller models you could run locally — it pressured every provider on both price and openness. The effects are still visible in 2026's cheap, capable reasoning models, and in the shift toward reasoning being an adjustable setting rather than a separate premium product.
What to use in 2026
- Instead of R1 — DeepSeek V3.2 or V4, which carry R1's reasoning forward at even lower cost (and stay open-weights).
- Instead of o1 — GPT-5, which absorbs the o-series reasoning into one model with an adjustable effort setting.
- For comparison — Claude Opus and Gemini also offer strong reasoning; test on your own tasks.
Use today's reasoning models from one key
DeepSeek V3.2, GPT-5.4, Claude Opus 4.7 and 300+ more — one OpenAI-compatible endpoint, compare reasoning quality and cost per request.
FAQ
Was DeepSeek R1 as good as OpenAI o1?
On many reasoning/math benchmarks, competitive — close on AIME and MATH, strong on coding reasoning. o1 kept an edge on some hardest tasks, but R1's near-parity at a fraction of the price, open-weights, is what made it landmark.
How much cheaper was R1 than o1?
About 25-30x on output — R1 launched ~$0.55/$2.19 vs o1's ~$15/$60. On a RAG workload that's ~$154/mo for R1 vs ~$4,200 for o1.
Is DeepSeek R1 open source?
Yes — open weights (MIT), including distilled smaller variants. o1 was proprietary and API-only.
Should I still use R1 or o1 in 2026?
Generally no — both are prior-gen. Use their successors: DeepSeek V3.2/V4 and GPT-5 (reasoning via effort).
What replaced R1 and o1?
R1's reasoning folded into DeepSeek V3.2/V4; o1 was superseded by GPT-5 with an effort setting instead of a separate o-series.
Why was DeepSeek R1 such a big deal?
It proved competitive reasoning could be open and cheap — including locally-runnable distilled models — pressuring everyone on price and openness. The effects persist in 2026's cheap reasoning models.
Can I access reasoning models through a gateway?
Yes — DataLLM Lab reaches DeepSeek V3.2, GPT-5, and Claude Opus with one OpenAI-compatible key to compare reasoning and cost.
How do reasoning models differ from regular LLMs?
They spend extra inference compute on internal chain-of-thought before answering — better on hard math/coding/logic, at higher latency and token use. In 2026 it's increasingly an effort setting on a general model (e.g. GPT-5).