Both are DeepSeek models. DeepSeek V4-Pro is the newer, generally stronger default; reach for DeepSeek V3.2 when its lower price or a specific cost or latency profile matters more than the latest capabilities.
DeepSeek V3.2 and DeepSeek V4-Pro are both DeepSeek models, so the real question is not which lab to trust but which tier fits your workload and budget. 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. DeepSeek V4-Pro is deepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices. 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 1.6× cheaper on input ($0.28/$0.42 per 1M tokens vs $0.435/$0.87 per 1M tokens) — modest, but it adds up at steady volume.
Context window: DeepSeek V4-Pro holds 7.6× more — 1M (~1,500 pages) vs 131K (~197 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 V4-Pro is the newer model by about 5 months (released April 24, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek V3.2
DeepSeek V4-Pro
Provider
DeepSeek (China)
DeepSeek (China)
Released
December 1, 2025
April 24, 2026
Context window
131K (~197 pages)
1M (~1,500 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
$0.435/$0.87 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
73.1%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA): DeepSeek V3.2 — DeepSeek V4-Pro is comparatively weak here — overlaps DeepSeek V4 and V3.2 already in this comparison
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes): DeepSeek V3.2 — 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 — and it runs cheaper at $0.28/$0.42 per 1M tokens.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386): DeepSeek V3.2 — DeepSeek V3.2 lists elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386) among its strengths; DeepSeek V4-Pro does not.
Open-weight 1.6T MoE (~49B active) — DeepSeek's largest, self-hostable: DeepSeek V4-Pro — DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices — and it carries the larger 1M context.
1M-token context with up to 384K output tokens: DeepSeek V4-Pro — Its 1M window holds about 7.6× more than DeepSeek V3.2's 131K in a single prompt.
Permanent low pricing at $0.435/$0.87 per million, set May 2026: DeepSeek V4-Pro — DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices — and it is the newer of the two.
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 V4-Pro — Its 1M window is about 7.6× larger than DeepSeek V3.2's 131K, fitting roughly 1,500 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 V4-Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: DeepSeek V4-Pro — Larger 1M window fits more in one prompt.
Anyone whose priority is long-context efficiency via deepseek sparse attention (dsa): DeepSeek V3.2 — It is specifically built for that.
Anyone whose priority is open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable: DeepSeek V4-Pro — That is its strongest area.
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 are real: 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.
DeepSeek V4-Pro: where it fits
DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices. Released April 24, 2026 by DeepSeek, it is built for open-weight 1.6T MoE (~49B active) — DeepSeek's largest, self-hostable, 1M-token context with up to 384K output tokens, permanent low pricing at $0.435/$0.87 per million, set May 2026, and sources place it near the top of open-weight coding, around 80 on SWE-Bench Verified.
Its trade-offs: independent SWE-Bench Verified placement is inconsistent across sources, pro and Pro-Max variants are quoted with different scores, text and code only — no image, audio or video, and overlaps DeepSeek V4 and V3.2 already in this comparison. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Because DeepSeek V3.2 and DeepSeek V4-Pro come from the same lab (DeepSeek), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. DeepSeek V4-Pro 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 V4-Pro and drop down only with a concrete reason.
Frequently asked questions
Is DeepSeek V3.2 or DeepSeek V4-Pro better for coding?
Public SWE-Bench figures are not available for DeepSeek V4-Pro, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa) while DeepSeek V4-Pro leans toward open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or DeepSeek V4-Pro?
DeepSeek V3.2 is cheaper — $0.28/$0.42 per 1M tokens vs $0.435/$0.87 per 1M tokens, roughly 1.6× apart on input.
Which has the bigger context window?
DeepSeek V4-Pro — 1M vs 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from DeepSeek V3.2 to DeepSeek V4-Pro?
Since both are DeepSeek models, the newer one (DeepSeek V4-Pro) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, DeepSeek V3.2 or DeepSeek V4-Pro?
DeepSeek V4-Pro — released April 24, 2026, about 5 months after DeepSeek V3.2.
DeepSeek V3.2 vs DeepSeek V4-Pro
DeepSeek · China | DeepSeek · China · Updated June 2026
Quick verdict
Both are DeepSeek models. DeepSeek V4-Pro is the newer, generally stronger default; reach for DeepSeek V3.2 when its lower price or a specific cost or latency profile matters more than the latest capabilities.
DeepSeek V3.2 and DeepSeek V4-Pro are both DeepSeek models, so the real question is not which lab to trust but which tier fits your workload and budget. 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. DeepSeek V4-Pro is deepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices. 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 1.6× cheaper on input ($0.28/$0.42 per 1M tokens vs $0.435/$0.87 per 1M tokens) — modest, but it adds up at steady volume.
▸Context window: DeepSeek V4-Pro holds 7.6× more — 1M (~1,500 pages) vs 131K (~197 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 V4-Pro is the newer model by about 5 months (released April 24, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek V3.2
DeepSeek V4-Pro
Provider
DeepSeek (China)
DeepSeek (China)
Released
December 1, 2025
April 24, 2026
Context window
131K (~197 pages)
1M (~1,500 pages)
Price (in/out)
$0.28/$0.42 per 1M tokens
$0.435/$0.87 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, code
SWE-Bench Verified
73.1%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Long-context efficiency via DeepSeek Sparse Attention (DSA)
DeepSeek V3.2
DeepSeek V4-Pro is comparatively weak here — overlaps DeepSeek V4 and V3.2 already in this comparison
Agentic tool-use with thinking integrated into tool calls (thinking/non-thinking modes)
DeepSeek V3.2
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 — and it runs cheaper at $0.28/$0.42 per 1M tokens.
Elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386)
DeepSeek V3.2
DeepSeek V3.2 lists elite competition math and reasoning (AIME 2025 93.1, Codeforces 2386) among its strengths; DeepSeek V4-Pro does not.
DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices — and it carries the larger 1M context.
1M-token context with up to 384K output tokens
DeepSeek V4-Pro
Its 1M window holds about 7.6× more than DeepSeek V3.2's 131K in a single prompt.
Permanent low pricing at $0.435/$0.87 per million, set May 2026
DeepSeek V4-Pro
DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices — and it is the newer of the two.
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 V4-Pro
Its 1M window is about 7.6× larger than DeepSeek V3.2's 131K, fitting roughly 1,500 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 V4-Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ DeepSeek V4-Pro
Larger 1M window fits more in one prompt.
Anyone whose priority is long-context efficiency via deepseek sparse attention (dsa)
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 are real: 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.
DeepSeek V4-Pro: where it fits
DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices. Released April 24, 2026 by DeepSeek, it is built for open-weight 1.6T MoE (~49B active) — DeepSeek's largest, self-hostable, 1M-token context with up to 384K output tokens, permanent low pricing at $0.435/$0.87 per million, set May 2026, and sources place it near the top of open-weight coding, around 80 on SWE-Bench Verified.
Its trade-offs: independent SWE-Bench Verified placement is inconsistent across sources, pro and Pro-Max variants are quoted with different scores, text and code only — no image, audio or video, and overlaps DeepSeek V4 and V3.2 already in this comparison. At $0.435 in / $0.87 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Because DeepSeek V3.2 and DeepSeek V4-Pro come from the same lab (DeepSeek), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. DeepSeek V4-Pro 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 V4-Pro and drop down only with a concrete reason.
Want both DeepSeek V3.2 and DeepSeek V4-Pro 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 V3.2 or DeepSeek V4-Pro better for coding?
Public SWE-Bench figures are not available for DeepSeek V4-Pro, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V3.2 leans toward long-context efficiency via deepseek sparse attention (dsa) while DeepSeek V4-Pro leans toward open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V3.2 or DeepSeek V4-Pro?
DeepSeek V3.2 is cheaper — $0.28/$0.42 per 1M tokens vs $0.435/$0.87 per 1M tokens, roughly 1.6× apart on input.
Which has the bigger context window?
DeepSeek V4-Pro — 1M vs 131K, about 7.6× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from DeepSeek V3.2 to DeepSeek V4-Pro?
Since both are DeepSeek models, the newer one (DeepSeek V4-Pro) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, DeepSeek V3.2 or DeepSeek V4-Pro?
DeepSeek V4-Pro — released April 24, 2026, about 5 months after DeepSeek V3.2.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.