Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. Pick Gemini 3.1 Pro for largest mainstream production context (2m) or long video and document analysis. Choose DeepSeek V4 if you need self-hosting or data privacy; Gemini 3.1 Pro if you want a managed API.
DeepSeek V4 (DeepSeek, China) and Gemini 3.1 Pro (Google, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Gemini 3.1 Pro is a 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Price: DeepSeek V4 is about 28× cheaper on input ($0.435/$0.87 per 1M tokens vs $12/$18 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
Context window: Gemini 3.1 Pro holds 2× more — 2M (~3,000 pages) vs 1M (~1,500 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 3 months (released April 24, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
DeepSeek V4
Gemini 3.1 Pro
Provider
DeepSeek (China)
Google (US)
Released
April 24, 2026
February 2026
Context window
1M (~1,500 pages)
2M (~3,000 pages)
Price (in/out)
$0.435/$0.87 per 1M tokens
$12/$18 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image, audio, video, code
SWE-Bench Verified
80.6%
Not published
MRCR v2 @ 1M
Not published
26.3%
Who wins what
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.
Largest mainstream production context (2M): Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
Long video and document analysis: Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
Agentic reasoning (high ARC-AGI-2): Gemini 3.1 Pro — A core design strength of Gemini 3.1 Pro.
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: Gemini 3.1 Pro — Its 2M window is about 2× larger, fitting roughly 3,000 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 Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Gemini 3.1 Pro — Larger 2M window fits more in one prompt.
A team with data-privacy or self-hosting needs: DeepSeek V4 — Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is near-frontier coding at ~1/12 the cost: DeepSeek V4 — It is specifically built for that.
Anyone whose priority is largest mainstream production context (2m): Gemini 3.1 Pro — That is its strongest area.
An enterprise with regional data-residency rules: Gemini 3.1 Pro or DeepSeek V4 — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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.
Gemini 3.1 Pro: where it fits
A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. Released February 2026 by Google, it is built for largest mainstream production context (2M), long video and document analysis, agentic reasoning (high ARC-AGI-2), and multimodal understanding.
Its trade-offs: long-context recall drops sharply past 256K, and premium price per token. At $12 in / $18 out per million tokens, it sits in the premium price band.
The bottom line for this matchup
The defining split here is open vs. closed. DeepSeek V4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.1 Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is DeepSeek V4 or Gemini 3.1 Pro better for coding?
Public SWE-Bench figures are not available for Gemini 3.1 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 Gemini 3.1 Pro leans toward largest mainstream production context (2m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4 or Gemini 3.1 Pro?
DeepSeek V4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $12/$18 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Gemini 3.1 Pro — 2M vs 1M, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4 and Gemini 3.1 Pro together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, Gemini 3.1 Pro and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, DeepSeek V4 or Gemini 3.1 Pro?
DeepSeek V4 — released April 24, 2026, about 3 months after Gemini 3.1 Pro.
DeepSeek V4 vs Gemini 3.1 Pro
DeepSeek · China | Google · US · Updated June 2026
Quick verdict
Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. Pick Gemini 3.1 Pro for largest mainstream production context (2m) or long video and document analysis. Choose DeepSeek V4 if you need self-hosting or data privacy; Gemini 3.1 Pro if you want a managed API.
DeepSeek V4 (DeepSeek, China) and Gemini 3.1 Pro (Google, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. DeepSeek V4 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Gemini 3.1 Pro is a 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Price: DeepSeek V4 is about 28× cheaper on input ($0.435/$0.87 per 1M tokens vs $12/$18 per 1M tokens) — a large enough gap that at scale it can be the single biggest line item in the decision.
▸Context window: Gemini 3.1 Pro holds 2× more — 2M (~3,000 pages) vs 1M (~1,500 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 3 months (released April 24, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
DeepSeek V4
Gemini 3.1 Pro
Provider
DeepSeek (China)
Google (US)
Released
April 24, 2026
February 2026
Context window
1M (~1,500 pages)
2M (~3,000 pages)
Price (in/out)
$0.435/$0.87 per 1M tokens
$12/$18 per 1M tokens
Open weight?
Yes — self-hostable
No — API only
Modalities
text, code
text, image, audio, video, code
SWE-Bench Verified
80.6%
Not published
MRCR v2 @ 1M
Not published
26.3%
Who wins what
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.
Largest mainstream production context (2M)
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
Long video and document analysis
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
Agentic reasoning (high ARC-AGI-2)
Gemini 3.1 Pro
A core design strength of Gemini 3.1 Pro.
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
Gemini 3.1 Pro
Its 2M window is about 2× larger, fitting roughly 3,000 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 Gemini 3.1 Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Gemini 3.1 Pro
Larger 2M window fits more in one prompt.
A team with data-privacy or self-hosting needs
→ DeepSeek V4
Open weights let you run it on your own hardware; Gemini 3.1 Pro is API-only.
Anyone whose priority is near-frontier coding at ~1/12 the cost
→ DeepSeek V4
It is specifically built for that.
Anyone whose priority is largest mainstream production context (2m)
→ Gemini 3.1 Pro
That is its strongest area.
An enterprise with regional data-residency rules
→ Gemini 3.1 Pro or DeepSeek V4
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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.
Gemini 3.1 Pro: where it fits
A 2M-token multimodal workhorse — huge breadth, but recall fades deep in the window. Released February 2026 by Google, it is built for largest mainstream production context (2M), long video and document analysis, agentic reasoning (high ARC-AGI-2), and multimodal understanding.
Its trade-offs: long-context recall drops sharply past 256K, and premium price per token. At $12 in / $18 out per million tokens, it sits in the premium price band.
The bottom line for this matchup
The defining split here is open vs. closed. DeepSeek V4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Gemini 3.1 Pro gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both DeepSeek V4 and Gemini 3.1 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 Gemini 3.1 Pro better for coding?
Public SWE-Bench figures are not available for Gemini 3.1 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 Gemini 3.1 Pro leans toward largest mainstream production context (2m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4 or Gemini 3.1 Pro?
DeepSeek V4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Gemini 3.1 Pro is API-metered at $12/$18 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Gemini 3.1 Pro — 2M vs 1M, about 2× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4 and Gemini 3.1 Pro together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, Gemini 3.1 Pro and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, DeepSeek V4 or Gemini 3.1 Pro?
DeepSeek V4 — released April 24, 2026, about 3 months after Gemini 3.1 Pro.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.