Both are DeepSeek models. DeepSeek V3.2 is the newer, generally stronger default; reach for DeepSeek R1 when a specific cost or latency profile matters more than the latest capabilities.
DeepSeek R1 and DeepSeek V3.2 are both DeepSeek models, so the real question is not which lab to trust but which tier fits your workload and budget. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences
Price: DeepSeek V3.2 is about 2× cheaper on input ($0.28/$0.42 per 1M tokens vs $0.55/$2.19 per 1M tokens) — modest, but it adds up at steady volume.
Context window: DeepSeek V3.2 holds 1× more — 131K (~197 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: DeepSeek V3.2 is the newer model by about 11 months (released December 1, 2025), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek R1
DeepSeek V3.2
Provider
DeepSeek (China)
DeepSeek (China)
Released
January 2025
December 1, 2025
Context window
128K (~192 pages)
131K (~197 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
$0.28/$0.42 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
Not published
73.1%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model: DeepSeek R1 — A core design strength of DeepSeek R1.
Transparent chain-of-thought: DeepSeek R1 — A core design strength of DeepSeek R1.
Low cost: DeepSeek R1 — A core design strength of DeepSeek R1.
Long-context efficiency via DeepSeek Sparse Attention (DSA): DeepSeek V3.2 — A core design strength of DeepSeek V3.2.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes): DeepSeek V3.2 — A core design strength of DeepSeek V3.2.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386): DeepSeek V3.2 — A core design strength of DeepSeek V3.2.
Lowest cost at scale: DeepSeek V3.2 — At $0.28/$0.42 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: DeepSeek V3.2 — Its 131K window is about 1× larger, fitting roughly 197 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek V3.2 — At $0.28/$0.42 per 1M tokens it undercuts DeepSeek R1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: DeepSeek V3.2 — Larger 131K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model: DeepSeek R1 — It is specifically built for that.
Anyone whose priority is long-context efficiency via deepseek sparse attention (dsa): DeepSeek V3.2 — That is its strongest area.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs: text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Because DeepSeek R1 and DeepSeek V3.2 come from the same lab (DeepSeek), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. DeepSeek V3.2 is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to DeepSeek V3.2 and drop down only with a concrete reason.
Frequently asked questions
Is DeepSeek R1 or DeepSeek V3.2 better for coding?
Public SWE-Bench figures are not available for DeepSeek R1, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek R1 leans toward open-weight reasoning model while DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek R1 or DeepSeek V3.2?
DeepSeek V3.2 is cheaper — $0.55/$2.19 per 1M tokens vs $0.28/$0.42 per 1M tokens, roughly 2× apart on input.
Which has the bigger context window?
DeepSeek V3.2 — 131K vs 128K, about 1× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from DeepSeek R1 to DeepSeek V3.2?
Since both are DeepSeek models, the newer one (DeepSeek V3.2) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, DeepSeek R1 or DeepSeek V3.2?
DeepSeek V3.2 — released December 1, 2025, about 11 months after DeepSeek R1.
DeepSeek R1 vs DeepSeek V3.2
DeepSeek · China | DeepSeek · China · Updated June 2026
Quick verdict
Both are DeepSeek models. DeepSeek V3.2 is the newer, generally stronger default; reach for DeepSeek R1 when a specific cost or latency profile matters more than the latest capabilities.
DeepSeek R1 and DeepSeek V3.2 are both DeepSeek models, so the real question is not which lab to trust but which tier fits your workload and budget. DeepSeek R1 is the open-weight reasoning model that reset price expectations in early 2025. DeepSeek V3.2 is a cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
Key differences at a glance
▸Price: DeepSeek V3.2 is about 2× cheaper on input ($0.28/$0.42 per 1M tokens vs $0.55/$2.19 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: DeepSeek V3.2 holds 1× more — 131K (~197 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: DeepSeek V3.2 is the newer model by about 11 months (released December 1, 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek R1
DeepSeek V3.2
Provider
DeepSeek (China)
DeepSeek (China)
Released
January 2025
December 1, 2025
Context window
128K (~192 pages)
131K (~197 pages)
Price (in/out)
$0.55/$2.19 per 1M tokens
$0.28/$0.42 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
Not published
73.1%
MRCR v2 @ 1M
Not published
Not published
Who wins what
Open-weight reasoning model
DeepSeek R1
A core design strength of DeepSeek R1.
Transparent chain-of-thought
DeepSeek R1
A core design strength of DeepSeek R1.
Low cost
DeepSeek R1
A core design strength of DeepSeek R1.
Long-context efficiency via DeepSeek Sparse Attention (DSA)
DeepSeek V3.2
A core design strength of DeepSeek V3.2.
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
A core design strength of DeepSeek V3.2.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
A core design strength of DeepSeek V3.2.
Lowest cost at scale
DeepSeek V3.2
At $0.28/$0.42 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
DeepSeek V3.2
Its 131K window is about 1× larger, fitting roughly 197 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V3.2
At $0.28/$0.42 per 1M tokens it undercuts DeepSeek R1, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ DeepSeek V3.2
Larger 131K window fits more in one prompt.
Anyone whose priority is open-weight reasoning model
→ DeepSeek R1
It is specifically built for that.
Anyone whose priority is long-context efficiency via deepseek sparse attention (dsa)
→ DeepSeek V3.2
That is its strongest area.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released January 2025 by DeepSeek, it is built for open-weight reasoning model, transparent chain-of-thought, low cost, and strong maths and code.
Its trade-offs are real: older than V4, smaller 128K context, and text/code focused. At $0.55 in / $2.19 out per million tokens, it sits in the budget price band.
DeepSeek V3.2: where it fits
A cost-efficient, open-weight (MIT) 685B-parameter MoE model whose DeepSeek Sparse Attention delivers GPT-5-comparable reasoning with far cheaper long-context inference. Released December 1, 2025 by DeepSeek, it is built for long-context efficiency via DeepSeek Sparse Attention (DSA), agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes), elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386), and low-cost, open-weight (MIT) self-hosting.
Its trade-offs: text-only — no image, audio, or video input, and sWE-Bench Verified (73.1) trails the top closed coding models (Claude 4.5 Sonnet 77.2, Gemini 3 Pro 76.2). At $0.28 in / $0.42 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Because DeepSeek R1 and DeepSeek V3.2 come from the same lab (DeepSeek), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. DeepSeek V3.2 is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to DeepSeek V3.2 and drop down only with a concrete reason.
Want both DeepSeek R1 and DeepSeek V3.2 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Is DeepSeek R1 or DeepSeek V3.2 better for coding?
Public SWE-Bench figures are not available for DeepSeek R1, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek R1 leans toward open-weight reasoning model while DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek R1 or DeepSeek V3.2?
DeepSeek V3.2 is cheaper — $0.55/$2.19 per 1M tokens vs $0.28/$0.42 per 1M tokens, roughly 2× apart on input.
Which has the bigger context window?
DeepSeek V3.2 — 131K vs 128K, about 1× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from DeepSeek R1 to DeepSeek V3.2?
Since both are DeepSeek models, the newer one (DeepSeek V3.2) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, DeepSeek R1 or DeepSeek V3.2?
DeepSeek V3.2 — released December 1, 2025, about 11 months after DeepSeek R1.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.