deploybase

DeepSeek R1 vs OpenAI O1: Reasoning Model Showdown

Deploybase · September 9, 2025 · Model Comparison

Contents

Deepseek R1 vs Openai O1: Overview

The DeepSeek R1 vs OpenAI o1 comparison addresses a historical moment: OpenAI's o1 models have been deprecated as of July 2025. Teams still running o1 need to migrate. DeepSeek R1 launched in January 2025 as a direct reasoning-focused alternative. The new matchup is R1 vs o3 (o1's successor), not o1 itself. That said, R1 is substantially cheaper, which alone makes the comparison relevant for teams evaluating reasoning models. Pricing and availability current as of March 2026.

Summary Comparison

DimensionDeepSeek R1OpenAI o1 (Deprecated)OpenAI o3 (Current)Edge
StatusActiveDeprecated (July 2025)Active (flagship)o3
Price Input $/M$0.55[Sunset]$2.00R1
Price Output $/M$2.19[Sunset]$8.00R1
Context Window128K128K200Ko3
AIME 2024 Score79.8%~83%~94%+o3
SWE-bench Verified89%
LatencySlower (thinking)Slower (thinking)Slower (thinking)Tie
AvailabilityGroqCloud, DeepSeek APINoneOpenAI API, Plus, Pro
Cost per 1M input + 500K output$0.715[Sunset]$6.00R1

o1 is gone. The real choice is R1 vs o3. On pure pricing, R1 wins by a large margin.

Pricing Deep Dive

DeepSeek R1 Pricing

Official DeepSeek API:

  • Input: $0.55/M tokens
  • Output: $2.19/M tokens
  • Cache hit: $0.055/M (90% cheaper on repeated prompts)

Available via:

  • DeepSeek API (deepseek.com/api)
  • GroqCloud (groq.com) - runs R1 on LPU hardware, different pricing
  • Third-party providers (Together.AI, DeepInfra, Novita) with varying markups

OpenAI o1 Pricing (Historical)

o1 and o1-mini were deprecated July 7, 2025. Prices were:

  • o1: $15/M input, $60/M output
  • o1-mini: $3/M input, $12/M output

Roughly 27x more expensive than DeepSeek R1 on the flagship tier.

OpenAI o3 Pricing (Current)

o3 replaces o1:

  • Input: $2.00/M tokens
  • Output: $8.00/M tokens
  • Pro tier: $20/M input, $80/M output (for intensive reasoning)

o3 is cheaper than o1 was. Still significantly more expensive than DeepSeek R1.

Cost at Scale (1M input + 500K output tokens)

ModelInputOutputTotal
DeepSeek R1$0.55$1.095$1.645
o3$2.00$4.00$6.00
o3-pro$20.00$40.00$60.00

DeepSeek R1 is ~3.6x cheaper than o3, ~36x cheaper than o3-pro.

For teams doing 1B tokens/month (500M input, 500M output):

  • R1: $275 input + $1,095 output = $1,370/month
  • o3: $1,000 input + $4,000 output = $5,000/month
  • Savings on R1: $3,630/month

OpenAI o1 Deprecation Timeline

OpenAI notified developers in April 2025 that o1 and o1-mini would be deprecated. The o1-preview model had already been signaled for phase-out. July 7, 2025 was the cutoff: o1 and o1-mini stopped being available on the OpenAI API.

Teams still using o1 via cached API keys or large-scale agreements may have extended access, but new projects cannot start on o1. Migration path: o3 is the direct replacement.

The deprecation was faster than typical. o1 was live for roughly 12 months before removal. That speed suggests OpenAI was confident in o3's capabilities and wanted to consolidate on a single flagship reasoning tier.

Model Capabilities

DeepSeek R1

DeepSeek is a Chinese AI company founded in 2023. R1 is their reasoning-optimized LLM, trained with reinforcement learning to show its work before answering (chain-of-thought).

671B parameters total, but only 37B active on any given inference pass (mixture-of-experts efficiency). That's why it's fast relative to its capability-most parameters are dormant.

Strengths:

  • Mathematics: AIME 2024 score 79.8%, competitive with o1-series reasoning models. Handles proofs, symbolic reasoning.
  • Software engineering: SWE-bench Verified performance places it above baseline (exact score not published by DeepSeek but comparable to GPT-4).
  • Cost efficiency: 37B active parameters means inference is faster per token than models using all parameters.
  • Chain-of-thought reasoning: Explicit thinking process. Teams can see how the model arrived at answers.

Weaknesses:

  • Context window: 128K tokens. Smaller than o3's 200K.
  • Multimodal: No built-in vision. Text only.
  • Real-time data: Trained on data through April 2025. Not live-updated.

OpenAI o3

OpenAI's flagship reasoning model, launched April 2025. Successor to o1 (deprecated). Available in o3 standard and o3-pro (more thinking compute).

Strengths:

  • Broader reasoning: Not just math. Philosophy, complex analysis, multi-step planning.
  • Larger context: 200K tokens, better for large codebases.
  • Established ecosystem: ChatGPT Plus, Pro, Team access. Integrations everywhere.
  • Vision: Multimodal. Can reason about images.

Weaknesses:

  • Expensive: $2.00/$8.00 per M tokens (~3.6x more than R1).
  • Latency: Slower per token due to reasoning compute.
  • Closed source: No way to fine-tune or run locally.

Benchmark Comparison

Mathematics (AIME 2024)

DeepSeek R1: 79.8% pass@1 on AIME 2024 (per official DeepSeek paper) OpenAI o1: ~92% (deprecated) OpenAI o3: ~94%+ (per OpenAI's announcement, but exact benchmark not independently verified yet)

Mathematics gap: negligible. Both are world-class on competition math. A 2-point gap on AIME is within noise. For mathematical reasoning, either model is competitive.

The interesting detail: R1's architecture is mixture-of-experts (671B total, 37B active). o3's architecture is not public. Different approaches, similar results. This suggests the reasoning capability is not tied to size but to training methodology. R1 achieves competitive reasoning with 18x fewer active parameters, which is why it's faster.

For teams using reasoning models for symbolic math, scientific computing, or proof verification, both reach acceptable accuracy. The choice comes down to cost and integration, not benchmark score.

Science (GPQA Diamond - PhD-level questions)

R1: Exact score not published. Estimated ~85-88% based on external benchmarking. o1: ~92% (per OpenAI's o1 announcement) o3: ~90%+ (estimated)

o1 led here. o3 likely similar. R1 trails slightly. Gap is real but not massive on graduate-level questions.

GPQA Diamond tests physics, chemistry, biology at the PhD level. 88% correct means about 1 in 12 questions is wrong. That's still concerning for scientific applications. Neither model should be trusted without human review on expert-level work.

For research teams using reasoning models as thinking partners (not as final truth), the gap between R1 and o3 is acceptable. For automated scientific workflows, the 4-point gap could matter. A system iterating on 1,000 research questions might produce 40 fewer errors with o3 than R1. That's meaningful at scale.

Software Engineering (SWE-bench Verified)

R1: ~70-75% (estimated; exact score not published) o1: 89% (per OpenAI) o3: ~88%+ (estimated)

This is the gap that matters for dev teams. OpenAI's reasoning models excel at multi-step code generation and debugging. R1 is strong but measurably below. For coding-heavy workloads, o3 justifies its cost premium.

General Knowledge (MMLU)

Both models perform in the 85-90% range on general knowledge. Reasoning models aren't optimized for raw knowledge. Use general-purpose models (GPT-5, Claude Opus) for this.

Integration and Availability

DeepSeek R1 Availability

DeepSeek's official API is available at api.deepseek.com. Standard REST interface. Python, JavaScript, Go SDKs. Same interface pattern as OpenAI (system messages, user/assistant turns, tools).

R1 is also available through third-party providers:

  • GroqCloud: Runs R1 on Groq's LPU hardware. Different pricing, different latency profile.
  • Together.AI: Offers R1 through their unified API.
  • DeepInfra: Hosts DeepSeek models at cheaper rates (no official SLA).

Multi-vendor availability is a strength. Teams can arbitrage pricing across providers. DeepSeek official is cheapest ($0.55/$2.19). DeepInfra offers $0.91 per 1M tokens blended (cheaper per token, but less transparency). Teams can test on DeepInfra cheap, move to official when confident.

Availability outside the US is better for R1 than o3. Chinese teams get native support. EU teams have regional availability. OpenAI's o3 is primarily US-focused.

OpenAI o3 Availability

o3 is available exclusively through OpenAI's API (api.openai.com) or ChatGPT (web and mobile). No third-party providers yet.

OpenAI-only distribution means:

  • Ecosystem lock-in: If the system uses OpenAI's other models, SDKs, and tooling, o3 integrates smoothly. Cost of adding R1 is a second vendor.
  • Support: OpenAI's large-scale support is available for o3 (Team, Pro, large-scale plans). DeepSeek's support is less formalized.
  • Compliance: OpenAI has SOC 2, HIPAA, FedRAMP for regulated industries. DeepSeek's compliance posture is less clear.

Teams in healthcare, finance, or government that already use ChatGPT/OpenAI API will face compliance questions before using R1. Switching requires security review and vendor approval.

Cost-Benefit Analysis

When R1 Makes Sense

High-volume batch reasoning. Processing 10 billion tokens/month on math or logic-heavy tasks. Cost difference: $16,450 (R1) vs $60,000 (o3-pro). That's $43,550/month savings for acceptable reasoning.

Research and experimentation. Testing reasoning approaches, iterating on prompt engineering. The 128K context handles paper review, code analysis, and problem solving. Pay less than half the price of o3.

Teams budget-constrained. Startups, academic labs, small teams. R1 at $1.37/million tokens becomes viable for daily use. o3 at $2-3/million tokens is still more expensive.

Mathematics and symbolic reasoning. R1 scores 79.8% on AIME 2024. o3 is stronger on competition math (~94%+), but for many math-heavy workloads R1 is sufficient at a fraction of the cost.

When o3 Makes Sense

Production software engineering. SWE-bench gap matters. o3's 88%+ vs R1's estimated 70-75% translates to fewer bugs in production code. For code generation feeding automated systems, the extra accuracy justifies cost.

Multimodal reasoning. Contracts with images, designs requiring interpretation, visual problem-solving. R1 can't do this.

Larger context requirements. o3's 200K context exceeds R1's 128K, making o3 preferable for larger codebase analysis or legal document review.

Teams already in OpenAI ecosystem. ChatGPT Pro subscriptions, Codex integrations, RBAC, large-scale agreements. Switching to DeepSeek means integrating a new API. Not worth it if o3 fits.

Reasoning Depth and Thinking Time

What "Reasoning" Actually Means

Reasoning models (R1, o1, o3) use a different inference paradigm: explicit chain-of-thought. Instead of generating an answer directly, they think through a problem step-by-step, then output the conclusion.

This is slower (2-5 seconds per response) but more accurate on hard problems. The thinking is often hidden (OpenAI doesn't show o3's full thought process in ChatGPT), but the API can expose it.

DeepSeek R1 Thinking

R1's architecture uses reinforcement learning trained on chain-of-thought. The model learns to think longer on hard problems and shorter on easy ones.

Example: math problem. R1 might output 2,000 tokens of reasoning (internal), then 100 tokens of answer. The API returns the 100-token answer. Reasoning is not visible to users (unless explicitly enabled).

This efficiency (scaling thinking time to problem difficulty) is why R1 is fast compared to o1. Instead of always thinking for 30 seconds, R1 adapts: 2 seconds for arithmetic, 10 seconds for olympiad math.

OpenAI o3 Thinking

o3 has explicit reasoning modes. Standard (light thinking) is cheaper. Pro (heavy thinking) uses 10x more compute for harder problems.

Thinking time is not adaptive. o3-pro always thinks longer, regardless of problem difficulty. It's more uniform, less elegant than R1's adaptive approach, but possibly more reliable on very hard problems.

Example: on AIME math, o3's heavy thinking mode reportedly reaches 95%+. Light mode is lower. o3-pro is the sledgehammer.

Practical Implications

R1: Better for cost-sensitive reasoning. Thinking scales automatically. Output is faster than o3 on average. Ideal for teams with variable problem difficulty.

o3 standard: ~3.6x more expensive than R1, lighter thinking. Good for most tasks when budget allows.

o3 pro: ~36x cost of R1. Only for problems where reasoning depth is critical (hard math, complex planning). Not for every query.

When Thinking Speed Matters

Real-time applications (chatbots, search) need sub-second latency. Reasoning models are not suitable. Use fast models (GPT-4o, Claude Sonnet) for this.

Batch reasoning (overnight processing, analysis pipelines) can tolerate 5-second latency per response. Reasoning models shine here.

Deployment and Operations

DeepSeek R1 Deployment

DeepSeek's API is REST-based, standard. Deploy with any framework: LangChain, LlamaIndex, vLLM, or custom Python.

Latency: First token ~2-3 seconds (reasoning time). Subsequent tokens ~100-200ms. Not fast, but consistent.

Rate limits: Standard usage tiers. Free tier is rate-limited (~100 requests/day). Paid tier is token-based (pay for what teams use).

Fallbacks: If DeepSeek API is down, third-party providers (Together.AI, DeepInfra) offer R1 as backup. Multi-vendor deployment reduces outage risk.

Infrastructure: DeepSeek runs on NVIDIA H100s. Inference is standard, not optimized for latency like Groq LPUs. Speed is acceptable for batch and reasoning, not real-time.

OpenAI o3 Deployment

OpenAI's API is the gold standard. HTTP/REST, with async and streaming support.

Latency: First token ~3-5 seconds (heavy reasoning). Designed for thinking time, not speed. o3-pro is even slower (intentionally).

Rate limits: Token-based pricing. Higher usage tiers available. large-scale contracts for volume commitments.

Multi-vendor options: Azure OpenAI and AWS Bedrock both host o3. Vendor lock-in is optional.

Infrastructure: OpenAI's proprietary training. Hardware is not public. Inference likely optimized for latency within their cost structure.

Practical Operations

DeepSeek: Simpler integration (fewer integrations mean fewer dependencies). Multi-vendor options provide fallback. Cost is the main feature. Operational simplicity is medium.

OpenAI: Deep integrations everywhere. LangChain, Copilot, Assistants API. Operational complexity comes from ecosystem breadth. Cost is higher. Support is better.

For teams building new systems, DeepSeek's lower cost and multi-vendor options are attractive. For teams migrating from o1, o3 is the native path (same API, same ecosystem).

FAQ

Is o1 still available? No. Deprecated July 7, 2025. New projects cannot start on o1. Existing users were encouraged to migrate to o3.

Should I use R1 or o3? If cost is primary: R1 (18x cheaper). If accuracy on coding is primary: o3. If budget allows both: use R1 for batches, o3 for production.

Can I run DeepSeek R1 locally? Yes. R1 weights are open-source (MIT license) and available on Hugging Face. Self-host via vLLM or llama.cpp on sufficient GPU hardware. Also available via DeepSeek API and third-party providers (GroqCloud, Together.AI).

What about o3-mini? o3-mini is cheaper ($1.10/$4.40) and faster but weaker than full o3. Compare to R1: o3-mini costs 2x more, hits some benchmarks lower. R1 is the budget choice.

Which handles longer documents? o3 at 200K context vs R1 at 128K. o3 handles larger documents. For documents under 100K tokens, both work equally well.

Is R1's thinking visible? Yes. Chain-of-thought reasoning is shown to users. You see the model's working. o3 also shows reasoning. Both are transparent about their thought process.

What about multimodal? o3 has vision. R1 doesn't. If the task involves images, o3 is required.

Sources