If you are choosing between DeepSeek R1 vs OpenAI o1 for a reasoning workload in 2026, the trade-off is no longer “open weights versus better numbers.” It is “open weights at a fraction of the price versus a model OpenAI itself now treats as legacy.” R1 was released in January 2025 with MIT-licensed weights and benchmark scores within a point or two of o1-1217 on AIME 2024, MATH-500 and SWE-Bench Verified. o1’s API list price never moved off $15 input / $60 output per million tokens, and OpenAI’s own newer o-series models have since superseded it. This comparison walks through benchmarks, pricing, deployment options and the decision rule we use in production.

Verdict: who wins, and for whom

For almost any new project in 2026, DeepSeek R1 is the better pick over OpenAI o1. R1 matches o1-1217 on the headline reasoning benchmarks, ships under an MIT license that lets you self-host, and runs at roughly 4 % of o1’s API cost. The only honest reason to still prefer o1 today is an existing OpenAI contract or a regulated environment that mandates US-hosted inference.

That said, neither model is the current state of the art. OpenAI has moved its reasoning lineup on to o3 and o4-mini; DeepSeek’s own current generation is V4 (released April 24, 2026). If you are starting fresh and want frontier reasoning, look at those instead. The R1-vs-o1 comparison still matters because both models remain widely deployed, both have stable, documented behaviour, and R1’s open weights make it the default reference point for self-hosted reasoning.

At a glance: DeepSeek R1 vs OpenAI o1

Numbers from the DeepSeek-R1 technical report (January 2025). Pricing for o1 from OpenAI’s API pricing page; we cross-checked against secondary sources confirming o1 at $15/$60 per million tokens, with newer o-series models priced substantially lower. R1’s $0.55 / $2.19 figures are the rates DeepSeek published when R1 was current; today’s DeepSeek API pricing centres on V4-Flash and V4-Pro tiers (see the pricing section below).

Reasoning and math

This is the cleanest head-to-head, because both models were built specifically for chain-of-thought reasoning and DeepSeek’s technical report includes a direct table against o1-1217.

DeepSeek-R1 achieves a score of 79.8 % Pass@1 on AIME 2024, slightly surpassing OpenAI-o1-1217. On MATH-500, it attains a score of 97.3 %, performing on par with OpenAI-o1-1217 and significantly outperforming other models. On GPQA Diamond — the graduate-level science benchmark — R1 lands at 71.5 %, a small step behind o1.

The headline is that R1 trades blows with o1 on math and STEM reasoning rather than dominating it. The two models converge on a similar performance band; the differentiators are price, transparency and licensing, not raw capability. If you want a deeper look at this side, our DeepSeek R1 review walks through the same benchmarks with longer commentary.

Coding

On software-engineering tasks, R1 and o1 are essentially tied. DeepSeek-R1 performs strongly on SWE-bench Verified with a score of 49.2 %, slightly ahead of OpenAI o1-1217’s 48.9 %. This positions DeepSeek-R1 as a strong contender in specialised reasoning tasks like software verification. On Codeforces — the competitive-programming proxy — DeepSeek-R1 achieved a 2,029 rating, better than 96.3 % of human programmers, against o1-1217’s 96.6 percentile.

In day-to-day use, both models are slow per request because they emit long internal reasoning traces before the final answer. For interactive coding (autocomplete, inline edits), neither is a sensible default — pair a reasoner with a faster general model. We cover that pattern in DeepSeek for coding and compare against IDE-native tools in DeepSeek Coder vs Copilot.

What R1 is bad at

The R1 paper is unusually candid here. The capabilities of DeepSeek-R1 fall short of DeepSeek-V3 in tasks such as function calling, multi-turn, complex role-playing, and JSON output. If your application leans on tool calling or strict JSON shapes, neither R1 nor o1 is the right primary model — pick a non-reasoning chat model and route only hard reasoning sub-tasks to a reasoner.

Writing and general chat

For prose, summarisation and ordinary Q&A, both reasoning models are overkill. They burn extra tokens “thinking” about straightforward prompts, which makes them slower and more expensive than a non-reasoning model would be for the same output quality.

If you want a single model for mixed workloads, V4-Flash or GPT-5 Mini are better defaults than either reasoner. R1 and o1 earn their keep specifically on hard math, hard coding and step-by-step logical analysis. For a broader head-to-head across all task types, see our DeepSeek vs ChatGPT comparison.

Pricing: a 27× gap on input, ~27× on output

OpenAI’s o1 API rates have not moved since launch. OpenAI o1 costs $15 per million input tokens and $60 per million output tokens, while DeepSeek Reasoner, based on the R1 model, was priced at $0.55 per million input and $2.19 per million output tokens. That is the comparison contemporary with the R1 release.

Two important caveats for 2026:

• OpenAI itself recommends moving off o1. Industry write-ups now consistently advise replacing it: “o1 at $15/$60 is expensive. o3 ($2/$8) is 7.5× cheaper on input and o4-mini ($1.10/$4.40) is 13.6× cheaper. Both outperform o1 on most benchmarks.” If you are still on o1 specifically, your real comparison today is R1 versus o3 or o4-mini.
• DeepSeek’s reasoner pricing has also moved. The legacy deepseek-reasoner model ID currently routes to deepseek-v4-flash in thinking mode, and will be retired on 2026-07-24 at 15:59 UTC. Today’s DeepSeek thinking-mode rates follow V4-Flash: $0.028 cache hit / $0.14 cache miss / $0.28 output per 1M tokens, or V4-Pro at $0.145 / $1.74 / $3.48.

Worked example: 1M reasoner calls per month

Assume 2,000 input tokens (cached system prompt), 200 input tokens (user message, uncached), and 1,500 output tokens per call. We multiply by 1,000,000 calls. R1’s legacy rate had no cache tier, so we lump all 2,200 input tokens into one bucket.

OpenAI o1 (no caching applied for fairness):

Input  : 2,200 × 1,000,000 = 2.2B tokens × $15/M  = $33,000.00
Output : 1,500 × 1,000,000 = 1.5B tokens × $60/M  = $90,000.00
                                                    ----------
Total                                              = $123,000.00

DeepSeek R1 (legacy rate):

Input  : 2,200 × 1,000,000 = 2.2B tokens × $0.55/M = $1,210.00
Output : 1,500 × 1,000,000 = 1.5B tokens × $2.19/M = $3,285.00
                                                     ---------
Total                                              = $4,495.00

DeepSeek V4-Flash, thinking mode (today’s equivalent):

Input cache hit  : 2,000 × 1,000,000 = 2.0B × $0.028/M = $56.00
Input cache miss :   200 × 1,000,000 = 0.2B × $0.14/M  = $28.00
Output           : 1,500 × 1,000,000 = 1.5B × $0.28/M  = $420.00
                                                         -------
Total                                                  = $504.00

The legacy R1 rate was already roughly 27× cheaper than o1 on like-for-like rates. Migrating to V4-Flash drops the bill by another order of magnitude. Use the DeepSeek pricing calculator to plug in your own token mix.

Deployment, privacy and ecosystem

This is where the two products differ most sharply.

• R1 is open-weight and MIT-licensed. The repository and weights are licensed under the MIT License; the R1 series supports commercial use, modifications and derivative works, including distillation for training other LLMs. You can self-host on your own GPUs, run distilled variants on a single workstation, or use any of the providers serving R1.
• o1 is API-only and US-hosted. No weights, no self-hosting, no on-prem option. For some regulated workloads that is a feature; for others it is a hard blocker.
• R1’s data path goes to DeepSeek servers when you use the hosted API. If you cannot send data to China-based servers, self-host R1 — that is the entire point of the open-weight release. We cover the trade-offs in DeepSeek’s privacy posture.
• Ecosystem. OpenAI ships a broader product surface — consumer ChatGPT, business and enterprise plans, agents, and a wide range of integrations. DeepSeek is more model-and-API-centric, with a minimal chat UI that exists mainly to let people try the underlying models. If you want app-level features (file search, image generation, voice), that is OpenAI’s ground; if you want a model you control, that is DeepSeek’s.

How to call each one

DeepSeek’s API is OpenAI-SDK-compatible. Chat requests hit POST /chat/completions, the OpenAI-compatible endpoint, with base_url="https://api.deepseek.com". The API is stateless — the client must resend the conversation history with every request, unlike the web chat which keeps session history for you. DeepSeek also exposes an Anthropic-compatible surface against the same base URL.

Minimal Python example using the V4 thinking-mode model (the modern equivalent of deepseek-reasoner):

from openai import OpenAI

client = OpenAI(
    base_url="https://api.deepseek.com",
    api_key="YOUR_KEY",
)

resp = client.chat.completions.create(
    model="deepseek-v4-flash",
    messages=[{"role": "user", "content": "Prove sqrt(2) is irrational."}],
    reasoning_effort="high",
    extra_body={"thinking": {"type": "enabled"}},
    temperature=0.0,
    max_tokens=4096,
)

print(resp.choices[0].message.reasoning_content)  # the thinking trace
print(resp.choices[0].message.content)            # the final answer

Thinking mode returns reasoning_content alongside the final content. If you are migrating from the legacy deepseek-reasoner ID, the change is one line — switch model to deepseek-v4-flash with reasoning_effort="high" before 2026-07-24 at 15:59 UTC, after which the legacy IDs stop working. Walk-through code lives in the DeepSeek API getting-started guide.

For o1, you call OpenAI’s chat/completions with model="o1". Note that o-series models don’t take temperature or top_p in the usual way; they ignore those values. As of April 2026, o-series models support function calling and structured outputs, but some parameters like temperature and top_p behave differently. Check OpenAI’s docs for the latest compatibility matrix before building reasoning model pipelines.

When to pick R1 vs o1

Pick DeepSeek R1 when

• You are price-sensitive at scale (anything above ~10M reasoner output tokens/month).
• You need self-hosting, on-prem inference, or air-gapped deployment.
• You are doing research that requires inspecting model weights or distilling smaller variants.
• You want to study or modify a reasoning model — R1’s open release made this possible at all.

Pick OpenAI o1 when

• You are already on OpenAI infrastructure and the marginal cost of switching exceeds the savings.
• Your compliance posture requires a US-hosted SaaS provider with established procurement paths.
• You need o1-pro tier reasoning that’s only accessible through ChatGPT Pro.

Even in the second case, web sources consistently push readers toward o3 or o4-mini rather than o1 itself. “Avoid o1 ($15/$60) unless you have a specific, validated need for its reasoning depth. o3 covers most reasoning use cases at 87 % less cost.”

What’s actually current in 2026

For honesty’s sake: if you start a project today, neither R1 nor o1 should be your default reasoner. DeepSeek’s R1-0528 update lifted AIME 2024 from ~79.8 % to 91.4 % and AIME 2025 from 70.0 % to 87.5 %. DeepSeek’s V4 family released April 24, 2026, supersedes R1 for thinking-mode workloads on the API, and OpenAI’s o3 and o4-mini supersede o1 in their lineup.

The R1-vs-o1 question stays useful for two reasons: many production systems still run on these two specifically, and the comparison is the cleanest illustration of how open-weight reasoning closed the gap with closed-source reasoning in a single quarter. For where DeepSeek has gone since, see DeepSeek V4.

Alternatives worth considering

• OpenAI o3 / o4-mini — current OpenAI reasoners, cheaper than o1 at higher benchmark scores.
• DeepSeek V4-Pro thinking — frontier-tier reasoning on DeepSeek; rates are $0.145 / $1.74 / $3.48 per 1M tokens (cache hit / miss / output).
• Claude reasoning modes — Anthropic’s extended-thinking feature on the Sonnet/Opus tiers.
• Self-hosted R1 distills — the 32B and 70B distill checkpoints run on a single high-memory GPU and retain a large fraction of R1’s math ability.

Browse our full AI comparison hub for matching head-to-heads, or jump to DeepSeek alternatives for reasoning for a wider survey.

Last verified: 2026-04-25. DeepSeek AI Guide is an independent resource and is not affiliated with DeepSeek or its parent company. Model IDs, pricing and API behaviour change; check the official DeepSeek documentation and pricing page before committing to a production decision.