Both are DeepSeek models. DeepSeek V4 is the newer, generally stronger default; reach for DeepSeek R1 when its lower price or specific profile matters more than the latest capabilities.
DeepSeek R1 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 R1 is the open-weight reasoning model that reset price expectations in early 2025. 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: nearly identical — $0.55/$2.19 per 1M tokens vs $0.435/$0.87 per 1M tokens. Cost will not be the deciding factor here.
Context window: DeepSeek V4 holds 7.8× more — 1M (~1,500 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 V4 is the newer model by about 15 months (released April 24, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
DeepSeek R1
DeepSeek V4
Provider
DeepSeek (China)
DeepSeek (China)
Released
2025
April 24, 2026
Context window
128K (~192 pages)
1M (~1,500 pages)
Price (in/out)
$0.55/$2.19 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
Not published
80.6%
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.
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 V4 — At $0.435/$0.87 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 7.8× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: DeepSeek V4 — At $0.435/$0.87 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 V4 — Larger 1M 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 near-frontier coding at ~1/12 the cost: DeepSeek V4 — That is its strongest area.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released 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 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 R1 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 R1 or DeepSeek V4 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 V4 leans toward near-frontier coding at ~1/12 the cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek R1 or DeepSeek V4?
DeepSeek V4 is cheaper — $0.55/$2.19 per 1M tokens vs $0.435/$0.87 per 1M tokens, roughly 1.3× apart on input.
Which has the bigger context window?
DeepSeek V4 — 1M vs 128K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from DeepSeek R1 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 R1 or DeepSeek V4?
DeepSeek V4 — released April 24, 2026, about 15 months after DeepSeek R1.
DeepSeek R1 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 R1 when its lower price or specific profile matters more than the latest capabilities.
DeepSeek R1 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 R1 is the open-weight reasoning model that reset price expectations in early 2025. 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: nearly identical — $0.55/$2.19 per 1M tokens vs $0.435/$0.87 per 1M tokens. Cost will not be the deciding factor here.
▸Context window: DeepSeek V4 holds 7.8× more — 1M (~1,500 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 V4 is the newer model by about 15 months (released April 24, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
DeepSeek R1
DeepSeek V4
Provider
DeepSeek (China)
DeepSeek (China)
Released
2025
April 24, 2026
Context window
128K (~192 pages)
1M (~1,500 pages)
Price (in/out)
$0.55/$2.19 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
Not published
80.6%
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.
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 V4
At $0.435/$0.87 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 7.8× larger, fitting roughly 1,500 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ DeepSeek V4
At $0.435/$0.87 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 V4
Larger 1M 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 near-frontier coding at ~1/12 the cost
→ DeepSeek V4
That is its strongest area.
DeepSeek R1: where it fits
The open-weight reasoning model that reset price expectations in early 2025. Released 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 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 R1 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 R1 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.
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 V4 leans toward near-frontier coding at ~1/12 the cost, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek R1 or DeepSeek V4?
DeepSeek V4 is cheaper — $0.55/$2.19 per 1M tokens vs $0.435/$0.87 per 1M tokens, roughly 1.3× apart on input.
Which has the bigger context window?
DeepSeek V4 — 1M vs 128K, about 7.8× larger. Useful only if the model actually reasons over the full window, which not all do.
Should I upgrade from DeepSeek R1 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 R1 or DeepSeek V4?
DeepSeek V4 — released April 24, 2026, about 15 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.