DeepSeek V4 Pro vs Gemini 3.7 Flash
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | DeepSeek-V4-Pro | Gemini 3.7 Flash |
|---|---|---|
| IntelligenceHigher is better · MM Intelligence v1.2 | UnrankedNot in the 22-model eligible cohort | #3 of 2287.8 score |
| CostLower is better · Published-token output estimate | #9 of 36$0.054 per LiveBench case | #15 of 36$0.085 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.0 | UnrankedNot in the 22-model eligible cohort | #2 of 2268.1 score |
Ranks come from the current complete eligible cohorts. Green highlights appear only when both models are ranked in the same metric. Missing required inputs remain unranked, and the three dimensions are not collapsed into an overall winner.
Benchmark Performance
Available Benchmarks
| Benchmark | DeepSeek-V4-Pro | Gemini 3.7 Flash |
|---|---|---|
| LMArena Text Arenatext-2026-09-01-011508720696 · arena_rating · leader | 1,451.0097% of row best · rating · deepseek-v4-pro; 95% CI [1447.00829245, 1454.98663953]; votes 54243; rank 41 | 1,491.10100% of row best · rating · gemini-3.7-flash-high; 95% CI [1482.94370265, 1499.25525622]; votes 5685; rank 6 |
| LiveBench2026-06-25 · overall · leader | 76.7992% of row best · percent · deepseek-v4-pro · 35,014 output tokens / case | 83.15100% of row best · percent · gemini-3.7-flash-high · 22,610 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark wins | 2 benchmark winsOverall lead |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Technical Differences
Side-by-Side Facts
| Field | DeepSeek-V4-Pro | Gemini 3.7 Flash |
|---|---|---|
| Developer | DeepSeek | Google DeepMind |
| Family | Deepseek V4 Pro | Gemini 3 |
| Model | DeepSeek-V4-Pro | Gemini 3.7 Flash |
| Version | DeepSeek-V4-Pro | Gemini 3.7 Flash |
| Lifecycle | active | active |
| Released | 2026-04-24 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Image, Video, Audio, Document |
| Output modalities | Text | Text |
| Context window | 1,049K | 1,049K |
| Total parameters | 1.6T | Unknown |
| Active parameters | 49B | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Google AI (Standard), Google Gemini (Standard) |
| Capabilities | chat, generation, reasoning | chat, generation, reasoning, tools |
13 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
DeepSeek V4 Pro Capabilities
Gemini 3.7 Flash Capabilities
Internal Comparison Graph
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
DeepSeek-V3.2DeepSeek | vs | DeepSeek-V4-ProDeepSeek | family variantstext |
DeepSeek-V4-ProDeepSeek | vs |  GPT-6 AstraOpenAI | cross-developer peerstext |
Claude Fable 5.1Anthropic | vs | DeepSeek-V4-ProDeepSeek | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs | Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs | Grok 4.6xAI | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | DeepSeek-V4-ProDeepSeek | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs |  GLM-5.3Z.ai | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | DeepSeek-V4-ProDeepSeek | cross-developer peerstext |
DeepSeek-V4-ProDeepSeek | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4 Pro vs Gemini 3.7 Flash FAQs
Is DeepSeek V4 Pro or Gemini 3.7 Flash better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4 Pro and Gemini 3.7 Flash, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4 Pro or Gemini 3.7 Flash?+
DeepSeek V4 Pro is $0.66 and Gemini 3.7 Flash is $0.75 per million tokens, so DeepSeek V4 Pro is cheaper on this metric. DeepSeek V4 Pro is $1.5441 and Gemini 3.7 Flash is $3.75 per million tokens, so DeepSeek V4 Pro is cheaper on this metric.
Which has a larger context window, DeepSeek V4 Pro or Gemini 3.7 Flash?+
Neither model has a larger sourced context window in this comparison. DeepSeek V4 Pro is 1,049K and Gemini 3.7 Flash is 1,049K.
Which performs better in benchmarks, DeepSeek V4 Pro or Gemini 3.7 Flash?+
Gemini 3.7 Flash leads the current overall benchmark count. The result uses 2 protocol-matched benchmarks from 2 publishers; it is not a universal quality score.
Can DeepSeek V4 Pro or Gemini 3.7 Flash be self-hosted?+
DeepSeek V4 Pro is the only model in this pair currently marked as self-hostable. DeepSeek V4 Pro is open weight; Gemini 3.7 Flash is not marked open weight.
Can DeepSeek V4 Pro and Gemini 3.7 Flash understand images?+
DeepSeek V4 Pro is not documented with image input; Gemini 3.7 Flash is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4 Pro or Gemini 3.7 Flash?+
Neither has a larger sourced maximum output. DeepSeek V4 Pro is — and Gemini 3.7 Flash is 66K.
Do DeepSeek V4 Pro and Gemini 3.7 Flash support reasoning and tool use?+
DeepSeek V4 Pro: reasoning. Gemini 3.7 Flash: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4 Pro or Gemini 3.7 Flash?+
DeepSeek V4 Pro has 6 sourced provider routes; Gemini 3.7 Flash has 2, so DeepSeek V4 Pro has broader tracked availability.
Which offers better value, DeepSeek V4 Pro or Gemini 3.7 Flash?+
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.