Published 2026-08-10 · reference numbers, verify before budgeting
Run the same chain-of-thought workload through OpenAI's o1 and it costs 27 times what DeepSeek R1 costs. Run it through o3 instead — same provider, same reasoning tier, same "thinking before answering" behavior — and the gap drops to 3.6 times. That's not a rounding difference. It means the question "how much does a reasoning model cost over an open-weight one" doesn't have one answer. It has a different answer for every model OpenAI ships, because OpenAI's own reasoning lineup spans a 7.5x range before you've even looked outside their catalog.
Pulled straight from our pricing table (387 models tracked, last refreshed against provider docs): OpenAI's four current reasoning SKUs, plus DeepSeek's R1 and its distilled variants, per 1M tokens.
| Model | Provider | Input /1M | Output /1M | vs DeepSeek R1 (blended) |
|---|---|---|---|---|
| o1 | OpenAI | $15.00 | $60.00 | 27.4× |
| o3 | OpenAI | $2.00 | $8.00 | 3.7× |
| o4-mini / o3-mini / o1-mini | OpenAI | $1.10 | $4.40 | 2.0× |
| Grok 4 Reasoning | xAI | $3.00 | $15.00 | 6.2× |
| DeepSeek R1 | DeepSeek | $0.55 | $2.19 | 1.0× |
| DeepSeek R1 Distill Llama 70B | DeepSeek | $0.23 | $0.69 | 0.35× |
| DeepSeek R1 Distill Qwen 32B | DeepSeek | $0.18 | $0.18 | 0.11× |
| Reference pricing from our tracked table, 387 models. "vs DeepSeek R1" uses a blended 3:8 input:output token ratio matching the worked example below. Run your own ratio on the reasoning tokens cost calculator. | ||||
Notice the shape of that curve. It isn't a straight line from expensive to cheap — o1 to o3 alone is an 87% output-price cut within OpenAI's own catalog, no competitor involved. The distilled DeepSeek variants then undercut even full R1 by another 3-5x, at a real capability cost the benchmarks are honest about. Four points on one table, four completely different stories about what "the reasoning tax" means.
A support-triage agent that reasons before it answers: 1,500 input tokens of context per ticket, 4,000 output tokens once you count the hidden chain-of-thought tokens billed as output. 10,000 tickets a month is 15M input tokens and 40M output tokens, run against each model:
| Model | Input cost | Output cost | Monthly total |
|---|---|---|---|
| o1 | $225.00 | $2,400.00 | $2,625.00 |
| Grok 4 Reasoning | $45.00 | $600.00 | $645.00 |
| o3 | $30.00 | $320.00 | $350.00 |
| o4-mini | $16.50 | $176.00 | $192.50 |
| DeepSeek R1 | $8.25 | $87.60 | $95.85 |
| 10,000 tickets/mo × 1,500 in / 4,000 out tokens. Illustrative workload — price your own mix on the reasoning tokens cost calculator. | |||
$2,625 a month on o1 versus $95.85 on DeepSeek R1 for a workload that, on paper, asks the same thing of both models. Swap o1 for o3 instead and the OpenAI bill alone drops 87%, from $2,625 to $350 — before DeepSeek enters the comparison at all. Most of the "reasoning tax" horror stories floating around right now are actually o1 pricing carried over from when it was the only frontier reasoning option. It isn't anymore, even inside OpenAI's own lineup.
o1 was priced when reasoning inference was scarce and mostly unproven — it shipped near the top of OpenAI's entire catalog, reasoning or not. o3 and o4-mini came after competition (DeepSeek R1 launched in January, Grok's and Gemini's reasoning tiers followed) forced the frontier labs to defend reasoning pricing the same way they'd already been forced to defend flagship chat pricing. The mini-tier reasoning models in particular now sit close enough to non-reasoning mid-tier pricing that "should I use a reasoning model" has stopped being primarily a cost question for a lot of workloads — it's a latency and chain-of-thought-token-budget question instead.
What hasn't converged is quality per dollar at the very top. o1 and o3 aren't interchangeable — o3 is the newer, generally stronger model at a fraction of the price, which is a straightforward upgrade. DeepSeek R1 is a different tradeoff: open weights, self-hostable, and priced like it, but benchmarked meaningfully behind o3 on hard reasoning tasks in most third-party evals we've seen cited. The 27x number against o1 is real and worth knowing. It is not the same claim as "R1 replaces o3 for 27x less" — that comparison is closer to 3.7x, and it's a capability tradeoff, not a free upgrade.
Don't anchor on o1's price — it's the most expensive reasoning SKU on the market by a wide margin and increasingly not the one teams should be pricing against. Anchor on o3 or o4-mini as the realistic OpenAI baseline, then decide whether DeepSeek R1's remaining 2-4x discount is worth the self-hosting or third-party-inference overhead, and whether the distilled variants' capability drop is acceptable for your task. Price your actual token mix — reasoning workloads skew output-heavy in a way chat workloads don't, so the blended ratio matters more here than almost anywhere else in the API cost stack. Run the numbers on the reasoning tokens cost calculator or the reasoning token overrun calculator if hidden chain-of-thought tokens are the part you can't predict, and see the reasoning-token tax for how o3 compares to non-reasoning GPT-4.1 on the same prompt.
Methodology: prices from our tracked pricing table (387 models) as of August 2026, matching each provider's published per-1M-token rates. The worked example uses a constructed 1,500-in/4,000-out token ratio to illustrate scaling, not telemetry from a production system — confirm your own workload's token mix and each provider's current rate before budgeting.