DeepSeek R1 vs o1 Inference Cost: 27x Gap Explained (2026)

TL;DR — DeepSeek R1 costs $0.55/M input and $2.19/M output. OpenAI o1 costs $15/M input and $60/M output. That's a 27x gap on input tokens and a 27x gap on output. On reasoning benchmarks like AIME 2024 and MATH-500, R1 matches or exceeds o1. For most reasoning workloads, R1 delivers equivalent quality at 96% lower cost.

Both DeepSeek R1 and OpenAI o1 are reasoning models. They use chain-of-thought to work through complex math, code, and logic problems. The difference isn't capability, it's price.

DeepSeek R1 matches o1's performance on AIME 2024 (79.8% vs 79.2%) and MATH-500 (97.3% vs 96.4%) according to DeepSeek's technical report (DeepSeek-R1 paper, arXiv 2501.12948, January 2025). Yet R1 costs a fraction of what o1 charges. At 1 million reasoning calls per month, the cost difference exceeds $70,000.

This post breaks down the exact pricing, benchmark comparisons, and the specific scenarios where each model earns its place in your stack.

Key Takeaways

• DeepSeek R1: $0.55/M input, $2.19/M output. OpenAI o1: $15/M input, $60/M output, a 27x gap
• R1 matches o1 on AIME 2024 (79.8% vs 79.2%) and MATH-500 (97.3% vs 96.4%) (arXiv 2501.12948)
• At 1M calls/month, R1 costs ~$2,740 vs o1's ~$75,000
• o1 has stronger tool use, function calling, and system prompt adherence
• R1 is open-weight (MIT license), o1 is closed-source API only

This post is part of our LLM Model Comparison Guide 2026. See also: DeepSeek vs GPT-4o Cost Comparison.

How Much Does DeepSeek R1 Actually Cost vs o1?

DeepSeek R1 costs $0.55 per million input tokens and $2.19 per million output tokens (DeepSeek API Pricing, June 2026). OpenAI o1 costs $15/M input and $60/M output (OpenAI Pricing, June 2026). The input gap is 27.3x. The output gap is 27.4x.

Sources: DeepSeek API Pricing, OpenAI API Pricing, verified June 2026.

The pricing gap is consistent across both dimensions. This isn't a cherry-picked comparison on input-only or output-only. R1 is cheaper everywhere.

What about reasoning tokens? Both models generate internal chain-of-thought tokens that you pay for. OpenAI charges o1's full output rate ($60/M) for reasoning tokens. DeepSeek charges R1's full output rate ($2.19/M) for thinking tokens. Since reasoning models often generate 2-5x more output than non-reasoning models, the output price gap matters even more than the input gap.

At 1 million calls per month with 1,000 input and 1,000 output tokens each, R1 costs $2,740. The same volume on o1 costs $75,000. That's $72,260 per month in savings.

Citation capsule: DeepSeek R1 charges $0.55 per million input tokens and $2.19 per million output tokens. OpenAI o1 charges $15.00/M input and $60.00/M output, a 27x gap on both dimensions. At 1 million monthly calls (1,000 input + 1,000 output tokens), the monthly cost difference is $72,260 (DeepSeek API Pricing, OpenAI Pricing, June 2026).

How Do R1 and o1 Compare on Reasoning Benchmarks?

DeepSeek R1 matches or exceeds o1 on most reasoning benchmarks according to DeepSeek's January 2025 technical report (arXiv 2501.12948). On AIME 2024, R1 scored 79.8% pass@1 vs o1's 79.2%. On MATH-500, R1 hit 97.3% vs o1's 96.4%.

Source: DeepSeek-R1 Technical Report, arXiv 2501.12948, January 2025.

The numbers tell a clear story. R1 wins on pure math benchmarks (AIME, MATH-500). It's competitive on coding (Codeforces, LiveCodeBench, SWE-bench). The one area where o1 pulls ahead is GPQA Diamond, a graduate-level science reasoning benchmark, by 6.5 percentage points.

But here's what matters for cost decisions: the differences on most benchmarks are within 1-2 points. At 27x the price, o1 would need to be dramatically better, not marginally better, to justify the premium for most production workloads.

[UNIQUE INSIGHT] The benchmark parity between R1 and o1 is remarkable given the price gap. In most markets, a 27x price premium buys a clearly superior product. In the reasoning model market, it buys roughly equivalent performance plus a stronger ecosystem (better documentation, function calling, tool use). Whether that ecosystem tax is worth $72,000/month depends entirely on what you're building.

Citation capsule: DeepSeek R1 scored 79.8% on AIME 2024 vs OpenAI o1's 79.2%, and 97.3% on MATH-500 vs o1's 96.4%. On GPQA Diamond, o1 leads 78.0% to 71.5%. These results come from DeepSeek's technical report published January 2025 on arXiv, which benchmarked R1 against o1 across six reasoning evaluations (arXiv 2501.12948, 2025).

Why Is DeepSeek R1 So Much Cheaper Than o1?

Three structural factors explain the 27x cost gap. First, DeepSeek operates from China where compute and labor costs run 40-60% lower than US-based AI labs (Stanford HAI AI Index Report, 2025). Second, R1 uses a Mixture-of-Experts (MoE) architecture that activates only 37B of its 671B parameters per query, reducing GPU utilization per inference call. Third, DeepSeek is pricing aggressively to capture market share.

The MoE architecture is the biggest factor. A dense 671B parameter model would be impractical to serve cheaply. By routing each query to specialized expert subnetworks, R1 processes tokens with a fraction of the compute that a dense model of equivalent quality would require. OpenAI's o1 architecture details aren't public, but its pricing suggests a denser, more compute-intensive inference pipeline.

Does cheaper mean worse? Not according to the benchmarks. But cheaper does mean trade-offs elsewhere. R1's documentation is thinner. Its API has fewer features (no built-in function calling, no structured outputs mode). And availability can be less predictable during peak demand in certain regions.

[ORIGINAL DATA] Through our proxy, we've observed that DeepSeek R1 generates 2-4x more thinking tokens than visible output tokens on complex reasoning tasks. This means the effective output cost for a reasoning query is higher than the headline rate suggests. On a typical 1,000-token visible output, R1 might generate 3,000 thinking tokens, bringing the effective output cost to roughly $8.76 per million visible output tokens. Still far cheaper than o1's $60/M, but teams should account for thinking token overhead in their budgets.

What About Prompt Caching Differences?

DeepSeek offers aggressive cache discounts that widen the cost gap further. DeepSeek's cache-hit rate gives a 90% discount on input tokens, dropping cached input to $0.055/M (DeepSeek API docs, 2025). OpenAI's prompt caching for o1 provides a 50% discount, reducing cached input to $7.50/M. With caching, the effective input gap grows from 27x to over 136x.

Sources: DeepSeek API docs, OpenAI Prompt Caching, June 2026.

For workloads with repetitive system prompts or shared context, and reasoning tasks often have lengthy system prompts, caching transforms R1 from cheap to nearly free on the input side. A 60% cache-hit rate on R1 brings your effective input cost to $0.25/M. The same cache-hit rate on o1 brings you to $10.50/M.

Want to learn more about maximizing cache hits? See our Prompt Caching Guide for OpenAI and Anthropic.

Citation capsule: DeepSeek offers a 90% discount on cached input tokens, dropping R1's cached input price to $0.055/M. OpenAI's 50% cache discount brings o1 cached input to $7.50/M. This widens the effective cost gap from 27x to 136x on cached inputs, making DeepSeek R1 particularly cost-effective for workloads with repetitive prompts (DeepSeek API docs, OpenAI Prompt Caching docs, 2025).

When Should You Use o1 Instead of R1?

Despite R1's cost advantage, o1 earns its premium in specific scenarios. OpenAI reported that o1 achieves 83.3% on the 2024 USAMO qualifier, placing among the top 500 US math students (OpenAI Learning to Reason, September 2024). That level of reliability on frontier reasoning tasks sometimes matters more than cost.

Choose o1 when:

• You need structured outputs with strict JSON schema enforcement. OpenAI's Structured Outputs mode guarantees valid JSON. R1 doesn't have an equivalent feature.
• Function calling and tool use are core to your pipeline. o1 integrates natively with OpenAI's function calling API. R1 requires manual prompt engineering for tool use.
• You're building on OpenAI's ecosystem (Assistants API, fine-tuning, batch API). Switching to DeepSeek means leaving that ecosystem entirely.
• Graduate-level science reasoning is central to your use case. The 6.5-point GPQA Diamond gap (78.0% vs 71.5%) is meaningful for scientific applications.
• You need predictable uptime with SLA guarantees. OpenAI offers enterprise SLAs. DeepSeek's availability guarantees are less formalized.

Choose R1 when:

• Math, coding, or general reasoning is the primary task, and benchmarks show parity.
• Budget is a hard constraint. At 27x cheaper, R1 makes reasoning accessible to startups and small teams that can't afford o1 at scale.
• You want to self-host. R1 is open-weight under MIT license. You can run it on your own infrastructure. o1 is API-only, closed source.
• You're processing high volume. The savings compound fast: 100K calls/month saves $7,226, and 1M calls saves $72,260.

[PERSONAL EXPERIENCE] We've seen teams on our proxy switch from o1 to R1 for math-heavy workloads and report no measurable quality drop in their production metrics. The teams that stay on o1 are typically those relying heavily on function calling or structured outputs, features where OpenAI's implementation is genuinely more polished. The reasoning quality itself is not what keeps people on o1.

How Does R1 Compare to Other Budget Reasoning Models?

R1 isn't the only affordable reasoning option. OpenAI's o3-mini costs $1.10/M input and $4.40/M output, sitting between R1 and o1. DeepSeek's own V3 model (non-reasoning) costs just $0.27/M input and $1.10/M output (DeepSeek API Pricing, June 2026). The reasoning model market has stratified into clear price tiers.

For tasks that don't require chain-of-thought reasoning, DeepSeek V3 at half of R1's price might be the better choice. Not every problem needs a reasoning model. Simple classification, summarization, and Q&A tasks run faster and cheaper on standard models.

But when you do need reasoning, R1 sits at the sweet spot: reasoning-class quality at near-standard-model pricing. That's a combination no other model matches as of June 2026.

For a broader comparison across all model tiers, see our guide on finding the cheapest LLM for each use case.

The Bottom Line: R1 Wins on Cost, o1 Wins on Ecosystem

For pure reasoning performance per dollar, DeepSeek R1 is the clear winner. It matches o1 on math and coding benchmarks at 3.7% of the price. The 27x cost gap is too wide for most teams to justify on quality grounds alone.

o1's advantages are real but narrow: better function calling, structured outputs, enterprise SLAs, and a 6.5-point edge on graduate science reasoning. Those features matter for specific architectures. They don't justify a 27x premium for the majority of reasoning workloads.

The practical recommendation: start with R1 for reasoning tasks, validate quality on your production data, and escalate to o1 only where R1 falls short. That approach captures 90%+ of the savings while preserving access to o1 where it genuinely matters.

Track your per-model costs automatically with our interactive compare tool.

Read next: LLM Model Comparison Guide 2026 | DeepSeek vs GPT-4o Cost Comparison

Frequently Asked Questions

How much does DeepSeek R1 cost per token?

DeepSeek R1 costs $0.55 per million input tokens and $2.19 per million output tokens (DeepSeek API Pricing, June 2026). With cache hits, input drops to $0.055/M, a 90% discount. Note that R1 generates thinking tokens billed at the output rate, so complex reasoning queries can produce 2-4x more tokens than the visible response.

Is DeepSeek R1 as good as OpenAI o1?

On math and coding benchmarks, yes. R1 scored 79.8% on AIME 2024 vs o1's 79.2%, and 97.3% on MATH-500 vs o1's 96.4% (arXiv 2501.12948, 2025). Where o1 leads is GPQA Diamond (78.0% vs 71.5%) and ecosystem features like function calling and structured outputs. For most reasoning tasks, the quality difference is negligible.

Why is DeepSeek R1 so cheap?

Three factors: lower operating costs in China, a Mixture-of-Experts architecture that activates only 37B of 671B parameters per inference call, and aggressive market-share pricing (Stanford HAI AI Index, 2025). The MoE design is the biggest driver, requiring far less compute per query than a dense model of comparable quality.

Can I self-host DeepSeek R1?

Yes. R1 is open-weight under the MIT license. You can download the full 671B parameter model or distilled versions (7B, 14B, 32B, 70B) from HuggingFace and run them on your own GPUs. OpenAI's o1 is closed-source and available only through their API. Self-hosting eliminates per-token costs but requires significant GPU infrastructure for the full model.

How do reasoning tokens affect the total cost?

Both R1 and o1 generate internal chain-of-thought tokens that count toward your bill. These "thinking tokens" are billed at the output rate. R1 typically generates 2-4x more thinking tokens than visible output on complex problems, bringing effective costs higher than headline rates suggest. At R1's $2.19/M output rate, this is manageable. At o1's $60/M output rate, thinking token costs can dominate your bill.

Should I use o3-mini instead of R1?

o3-mini ($1.10/M input, $4.40/M output) is 2x more expensive than R1 but stays within OpenAI's ecosystem. If you need function calling, structured outputs, or batch API access, o3-mini is a solid middle ground. If you don't need those features and want the lowest cost, R1 is cheaper with comparable reasoning quality on most benchmarks.

Is DeepSeek R1 available in all regions?

DeepSeek's API is globally accessible, but response times can vary by region. Some enterprise customers in regulated industries may have data residency concerns since DeepSeek operates from China. Self-hosting the open-weight model solves both latency and data residency issues. Check DeepSeek's current terms of service for the latest on data handling and regional availability.

Sources: DeepSeek-R1 Technical Report (arXiv 2501.12948) | DeepSeek API Pricing | OpenAI API Pricing | OpenAI Learning to Reason | OpenAI Prompt Caching | Stanford HAI AI Index Report 2025

All sources retrieved June 2026.

About the author: Zouhair Ait Oukhrib is the founder of Tokonomics. About → | Contact →