Both are DeepSeek models. DeepSeek V4 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 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 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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 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.
Coding: DeepSeek V4 leads SWE-Bench Verified by 7.5 points (73.1% vs 80.6%) — a real edge on hard, real-world software tasks.
Recency: DeepSeek V4 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
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%
80.6%
MRCR v2 @ 1M
Not published
Not published
Who wins what
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.
Near-frontier coding at ~1/12 the cost: DeepSeek V4 — A core design strength of DeepSeek V4.
Open MIT-licensed weights you can self-host: DeepSeek V4 — A core design strength of DeepSeek V4.
No long-context surcharge: DeepSeek V4 — A core design strength of DeepSeek V4.
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 — Its 1M window is about 7.6× larger, 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, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: DeepSeek V4 — 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 near-frontier coding at ~1/12 the cost: DeepSeek V4 — 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: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs: trails the very best on hardest agentic coding, and text/code focused, less multimodal. 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 come from the same lab (DeepSeek), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. DeepSeek V4 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 and drop down only with a concrete reason.
Frequently asked questions
Is DeepSeek V3.2 or DeepSeek V4 better for coding?
On SWE-Bench Verified, DeepSeek V3.2 scores 73.1% and DeepSeek V4 scores 80.6% — DeepSeek V4 has the measurable edge.
Which is cheaper, DeepSeek V3.2 or DeepSeek V4?
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 — 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?
Since both are DeepSeek models, the newer one (DeepSeek V4) 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?
DeepSeek V4 — released April 24, 2026, about 5 months after DeepSeek V3.2.
DeepSeek V3.2 vs DeepSeek V4
DeepSeek · China | DeepSeek · China · Updated June 2026
Quick verdict
Both are DeepSeek models. DeepSeek V4 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 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 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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 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.
▸Coding: DeepSeek V4 leads SWE-Bench Verified by 7.5 points (73.1% vs 80.6%) — a real edge on hard, real-world software tasks.
▸Recency: DeepSeek V4 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
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%
80.6%
MRCR v2 @ 1M
Not published
Not published
Who wins what
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.
Near-frontier coding at ~1/12 the cost
DeepSeek V4
A core design strength of DeepSeek V4.
Open MIT-licensed weights you can self-host
DeepSeek V4
A core design strength of DeepSeek V4.
No long-context surcharge
DeepSeek V4
A core design strength of DeepSeek V4.
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
Its 1M window is about 7.6× larger, 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, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ DeepSeek V4
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 near-frontier coding at ~1/12 the cost
→ DeepSeek V4
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: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs: trails the very best on hardest agentic coding, and text/code focused, less multimodal. 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 come from the same lab (DeepSeek), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. DeepSeek V4 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 and drop down only with a concrete reason.
Want both DeepSeek V3.2 and DeepSeek V4 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 better for coding?
On SWE-Bench Verified, DeepSeek V3.2 scores 73.1% and DeepSeek V4 scores 80.6% — DeepSeek V4 has the measurable edge.
Which is cheaper, DeepSeek V3.2 or DeepSeek V4?
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 — 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?
Since both are DeepSeek models, the newer one (DeepSeek V4) 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?
DeepSeek V4 — 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.