Both are DeepSeek models. DeepSeek V4-Pro is the newer, generally stronger default; reach for DeepSeek V4 when a specific cost or latency profile matters more than the latest capabilities.
DeepSeek V4 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 V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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
Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Specifications
Spec
DeepSeek V4
DeepSeek V4-Pro
Provider
DeepSeek (China)
DeepSeek (China)
Released
April 24, 2026
April 24, 2026
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.435/$0.87 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
80.6%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost: DeepSeek V4 — DeepSeek V4 lists near-frontier coding at ~1/12 the cost among its strengths; DeepSeek V4-Pro does not.
Open MIT-licensed weights you can self-host: DeepSeek V4 — DeepSeek V4 lists open MIT-licensed weights you can self-host among its strengths; DeepSeek V4-Pro does not.
No long-context surcharge: DeepSeek V4 — DeepSeek V4 lists no long-context surcharge among its strengths; DeepSeek V4-Pro does not.
1M-token context with up to 384K output tokens: DeepSeek V4-Pro — DeepSeek V4-Pro lists 1M-token context with up to 384K output tokens among its strengths; DeepSeek V4 does not.
Permanent low pricing at $0.435/$0.87 per million, set May 2026: DeepSeek V4-Pro — DeepSeek V4-Pro lists permanent low pricing at $0.435/$0.87 per million, set May 2026 among its strengths; DeepSeek V4 does not.
Which should you pick?
Anyone whose priority is near-frontier coding at ~1/12 the cost: DeepSeek V4 — 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 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 are real: 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.
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 V4 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 V4 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 V4 leans toward near-frontier coding at ~1/12 the cost 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 V4 or DeepSeek V4-Pro?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from DeepSeek V4-Pro to DeepSeek V4?
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 V4 or DeepSeek V4-Pro?
They were released around the same time (April 24, 2026 and April 24, 2026).
DeepSeek V4 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 V4 when a specific cost or latency profile matters more than the latest capabilities.
DeepSeek V4 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 V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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
▸Context window: both advertise 1M (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
Side-by-side specs
Spec
DeepSeek V4
DeepSeek V4-Pro
Provider
DeepSeek (China)
DeepSeek (China)
Released
April 24, 2026
April 24, 2026
Context window
1M (~1,500 pages)
1M (~1,500 pages)
Price (in/out)
$0.435/$0.87 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
80.6%
Not published
MRCR v2 @ 1M
Not published
Not published
Who wins what
Near-frontier coding at ~1/12 the cost
DeepSeek V4
DeepSeek V4 lists near-frontier coding at ~1/12 the cost among its strengths; DeepSeek V4-Pro does not.
Open MIT-licensed weights you can self-host
DeepSeek V4
DeepSeek V4 lists open MIT-licensed weights you can self-host among its strengths; DeepSeek V4-Pro does not.
No long-context surcharge
DeepSeek V4
DeepSeek V4 lists no long-context surcharge among its strengths; DeepSeek V4-Pro does not.
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 are real: 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.
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 V4 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 V4 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 V4 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 V4 leans toward near-frontier coding at ~1/12 the cost 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 V4 or DeepSeek V4-Pro?
They are priced almost identically, so cost will not decide between them.
Which has the bigger context window?
Both advertise 1M (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Should I upgrade from DeepSeek V4-Pro to DeepSeek V4?
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 V4 or DeepSeek V4-Pro?
They were released around the same time (April 24, 2026 and April 24, 2026).
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.