DeepSeek V3 wins on coding (87 vs 84) and writing quality and price ($0.27 vs $0.55/1M input). For most workflows, DeepSeek V3 is the stronger default — gpt-4o-class coding quality at under $0.30/1m — the best value in the directory.
The safest DeepSeek R1 vs DeepSeek V3 default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
DeepSeek V3 is the strongest answer here for DeepSeek R1 vs DeepSeek V3 — pick it when quality of output matters more than the $0.27/1M/1M input you pay for it.
GPT-5.1-Codex-Max is the cheaper way in for DeepSeek R1 vs DeepSeek V3, at $1.25/1M/1M input against DeepSeek V3's $0.27/1M/1M.
DeepSeek R1 is the fastest of these for DeepSeek R1 vs DeepSeek V3 — worth it when latency is what the reader notices, not the last few points of reasoning depth.
DeepSeek V3 leads on coding with a score of 87 vs 84 for DeepSeek R1.
DeepSeek V3 is cheaper at $0.27/1M input tokens vs $0.55/1M for DeepSeek R1.
DeepSeek V3 is the stronger default for coding tasks.
DeepSeek V3 is the safer default: it is built for coding, reasoning, and general tasks at extreme cost efficiency, which covers most of what people bring to this comparison.
Choose DeepSeek R1 when your work is mostly math and science — that is the workload it was tuned for.
Both models serve different primary workflows — DeepSeek V3 for coding and reasoning, DeepSeek R1 for math and science — so running each where it has a clear edge often beats forcing one to do both.
Switch the scoring lens to see whether the DeepSeek R1 vs DeepSeek V3 answer changes when cost, speed, or long-document depth leads the decision.
DeepSeek / Budget / Mar 24, 2026
GPT-4o-class coding quality at under $0.30/1M — the best value in the directory.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
Your team has data sovereignty requirements or needs enterprise-grade reliability guarantees.
Where the DeepSeek R1 vs DeepSeek V3 recommendation shifts once you weigh price or latency differently.
Open-source o1-class reasoning at a fraction of the cost.
GPT-4o-class coding quality at under $0.30/1M — the best value in the directory.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| DeepSeek V3DeepSeek | $0.27/1M | $1.10/1M | $4.90 | 128k tokens | Fast | 87 | 74 | 80 |
| DeepSeek R1DeepSeek | $0.55/1M | $2.19/1M | $9.88 | 128k tokens | Deliberate | 84 | 60 | 89 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for DeepSeek R1 vs DeepSeek V3, what it is genuinely good at, and where we would steer you away from it.
Ranked first here for DeepSeek R1 vs DeepSeek V3: 87/100 on coding, with the widest margin of anything in this line-up.
Open-source frontier model from DeepSeek that matches GPT-4o class performance at a fraction of the cost — the most disruptive budget option for coding and general tasks.
Your team has data sovereignty requirements or needs enterprise-grade reliability guarantees.
The most cost-efficient model for GPT-4o-class coding quality. Hard to beat on value per token for engineering teams.
Full pricing, benchmark table and release notes on the DeepSeek V3 page.
Here for latency: it answers fastest of anything listed for DeepSeek R1 vs DeepSeek V3, at 84/100 on coding.
Open-source reasoning model that matches o1-class performance on math, science, and complex coding at a fraction of the cost — the best open alternative to proprietary reasoning models.
Speed matters — R1's deliberate reasoning makes it wrong for interactive or high-throughput use cases.
The open-source reasoning model benchmark. If you need o1-class thinking at open-source pricing, nothing else competes.
Full pricing, benchmark table and release notes on the DeepSeek R1 page.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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DeepSeek V3 wins on more of the categories we score — coding, research, reasoning — so it is the better default of the two. DeepSeek R1 is the better pick when your work is mostly math and science. Neither is universally "better": DeepSeek V3 is aimed at coding and reasoning, DeepSeek R1 at math and science.
DeepSeek V3 is cheaper at $0.27/1M input and $1.1/1M output. DeepSeek R1 costs $0.55/1M input and $2.19/1M output.
Both DeepSeek R1 and DeepSeek V3 have the same 128K context window.
DeepSeek V3 is better for coding with a score of 87 vs DeepSeek R1's 84 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
DeepSeek V3 is faster with a fast speed rating (score: 4) vs DeepSeek R1's deliberate rating (score: 1). Speed matters most for interactive and high-throughput work; for batch jobs the DeepSeek R1 latency penalty is usually invisible.
Chinese-origin model raises data sovereignty concerns for some enterprise teams. Slightly weaker on nuanced English writing tone compared to Claude and GPT. Less reliable for complex multi-step agentic workflows vs frontier models. Avoid it if your team has data sovereignty requirements or needs enterprise-grade reliability guarantees. That is the main case for looking at DeepSeek R1 instead.
Slow — deliberate reasoning takes significantly longer than standard models. Overkill for routine tasks where a faster model gets the same result. Same data sovereignty concerns as DeepSeek V3 for regulated industries. Avoid it if speed matters — R1's deliberate reasoning makes it wrong for interactive or high-throughput use cases. Against DeepSeek V3 specifically, the gap shows up most on coding (87 vs 84).
Take a moderate workload of 10M input and 2M output tokens a month. DeepSeek R1 runs $9.88 (at $0.55/1M in and $2.19/1M out); DeepSeek V3 runs $4.90 (at $0.27/1M in and $1.1/1M out). That is a $4.98/month difference — DeepSeek V3 is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is DeepSeek V3 for coding and reasoning, with DeepSeek R1 handling math and science. Since DeepSeek V3 is both the stronger and the cheaper option here, a split mainly makes sense if DeepSeek R1 covers a capability you specifically need.