DeepSeek V3.2 vs Gemini 3.1 Pro
At a Glance
| Compare |  DeepSeek V3.2DeepSeek |  Gemini 3.1 ProGoogle DeepMind |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #9 of 4679.7 score · 3/3 sources · complete |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #27 of 44$0.161 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #9 of 3859.2 score · 3/3 sources · complete |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.26Deepinfra ↗ · Sep 21, 2026 | $2.00Google AI ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $0.38Deepinfra ↗ · Sep 21, 2026 | $12.00Google AI ↗ · Aug 29, 2026 |
| Context windowMaximum documented tokens | 164K | 1,049K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 29, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | DeepSeek-V3.2 | Gemini 3.1 Pro |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,424.8096% of row best · rating · deepseek-v3.2; 95% CI [1421.27271719, 1428.32733406]; votes 46458; rank 96 | 1,480.08100% of row best · rating · gemini-3.1-pro-preview; 95% CI [1476.92899018, 1483.22190450]; votes 106951; rank 16 |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 1 benchmark winNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | DeepSeek-V3.2 | Gemini 3.1 Pro |
|---|---|---|
| Developer | DeepSeek | Google DeepMind |
| Family | Deepseek V3 2 | Gemini 3 |
| Model | DeepSeek-V3.2 | Gemini 3.1 Pro |
| Version | DeepSeek-V3.2 | Gemini 3.1 Pro |
| Lifecycle | active | preview |
| Released | 2025-12-01 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video, Audio, Document |
| Output modalities | Text | Text |
| Context window | 164K | 1,049K |
| Total parameters | 685.4B | Unknown |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard) | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | chat, generation, reasoning | chat, generation, reasoning, tools |
DeepSeek V3.2 Capabilities
Gemini 3.1 Pro Capabilities
Related Comparisons
All text comparisons →| A | Pair | B | Context |
|---|---|---|---|
DeepSeek-V3.2DeepSeek | vs | Gemini 2.5 ProGoogle DeepMind | cross-developer peerstext |
DeepSeek-V3.2DeepSeek | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
Claude Fable 5.1Anthropic | vs | DeepSeek-V3.2DeepSeek | cross-developer peerstext |
DeepSeek-V3.2DeepSeek | vs | DeepSeek-V4.1-FlashDeepSeek | family variantstext |
DeepSeek-V3.2DeepSeek | vs | Grok 4.6xAI | cross-developer peerstext |
DeepSeek-V3.2DeepSeek | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
DeepSeek-V3.2DeepSeek | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | DeepSeek-V3.2DeepSeek | cross-developer peerstext |
DeepSeek-V3.2DeepSeek | vs |  GLM-5.3Z.ai | cross-developer peerstext |
DeepSeek-V3.2DeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | DeepSeek-V3.2DeepSeek | cross-developer peerstext |
DeepSeek-V3.2DeepSeek | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V3.2 vs Gemini 3.1 Pro FAQs
Is DeepSeek V3.2 or Gemini 3.1 Pro better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V3.2 and Gemini 3.1 Pro, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V3.2 or Gemini 3.1 Pro?+
DeepSeek V3.2 is $0.26 and Gemini 3.1 Pro is $2.00 per million tokens, so DeepSeek V3.2 is cheaper on this metric. DeepSeek V3.2 is $0.38 and Gemini 3.1 Pro is $12.00 per million tokens, so DeepSeek V3.2 is cheaper on this metric.
Which has a larger context window, DeepSeek V3.2 or Gemini 3.1 Pro?+
Gemini 3.1 Pro has the larger sourced context window. DeepSeek V3.2 supports 164K and Gemini 3.1 Pro supports 1,049K.
Which performs better in benchmarks, DeepSeek V3.2 or Gemini 3.1 Pro?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V3.2 or Gemini 3.1 Pro be self-hosted?+
DeepSeek V3.2 is the only model in this pair currently marked as self-hostable. DeepSeek V3.2 is open weight; Gemini 3.1 Pro is not marked open weight.
Can DeepSeek V3.2 and Gemini 3.1 Pro understand images?+
DeepSeek V3.2 is not documented with image input; Gemini 3.1 Pro is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V3.2 or Gemini 3.1 Pro?+
Neither has a larger sourced maximum output. DeepSeek V3.2 is — and Gemini 3.1 Pro is 66K.
Do DeepSeek V3.2 and Gemini 3.1 Pro support reasoning and tool use?+
DeepSeek V3.2: reasoning. Gemini 3.1 Pro: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V3.2 or Gemini 3.1 Pro?+
DeepSeek V3.2 has 3 sourced provider routes; Gemini 3.1 Pro has 2, so DeepSeek V3.2 has broader tracked availability.
Which offers better value, DeepSeek V3.2 or Gemini 3.1 Pro?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.