MModelspectraIndependent AI Model Intelligence
Head-to-head · Updated 2026-09-08
Model Comparison · Head to Head

Gemini 3.1 Pro vs DeepSeek-V4-Flash

Gemini 3.1 Pro wins on Overall, Coding, Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorGoogle / DeepSeek
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose Gemini 3.1 Pro when coding depth, long-context reliability matter most; choose DeepSeek-V4-Flash when lower cost, lower latency is the priority.

Choose Gemini 3.1 Pro if you…

  • Native audio and video
  • Strong scientific computing
  • Largest 2M context
  • Search integration
  • Best for: Multimodal, Scientific reasoning, Audio & video

Choose DeepSeek-V4-Flash if you…

  • Rock-bottom price
  • Fast
  • Open source
  • Fit for simple tasks
  • Best for: Ultra-fast response, Low cost, High concurrency
02

Head-to-head aggregate scores

Scores are 0–100, aggregated from public benchmark information and independently weighted across three leaderboards. Rank is out of 22 tracked models.

Gemini 3.1 Pro Higher overall
Google · #3 overall
Overall83
Coding74
Multimodal92
VS
DeepSeek-V4-Flash
DeepSeek · #16 overall
Overall67
Coding65
Multimodal65

Aggregated from public sources and independently weighted; methodology on the Terms page. Scores within 3 points are treated as statistically tied.

03

Specs & pricing — every field side by side

List API prices in USD per 1M tokens. The highlighted cell is the stronger value/capability on that row.

DimensionGemini 3.1 ProDeepSeek-V4-FlashVerdict
VendorGoogle (US)DeepSeek (CN)Different vendors
Released2026.052026.04Gemini 3.1 Pro is newer
Overall (rank)83 · #367 · #16Gemini 3.1 Pro +16
Coding74 · #965 · #16Gemini 3.1 Pro +9
Multimodal92 · #265 · #20Gemini 3.1 Pro +27
Context window2M1MGemini 3.1 Pro larger
Max output128K128KTie
Effective-context9290Gemini 3.1 Pro more reliable
Input $/1M$2$0.14DeepSeek-V4-Flash cheaper
Output $/1M$12$0.28DeepSeek-V4-Flash cheaper
Cache discountnonenoneTie
Speed~60 tok/s~85 tok/sDeepSeek-V4-Flash faster
TTFT0.7s0.3sDeepSeek-V4-Flash snappier
Function calling7872Gemini 3.1 Pro ahead
Refusal rate~12%~5%DeepSeek-V4-Flash less restrictive
English9075Gemini 3.1 Pro
Chinese7580DeepSeek-V4-Flash
Modalitiestext, image, audio, videotextdifferent coverage
Open weightsNoYesDeepSeek-V4-Flash is open
Fine-tuningYesYes
Free tierGemini App free with limited quota; API free tier availableDeepSeek App free
SOC2 / no-trainyes / yesno / yes
Private deploymentNoYesDeepSeek-V4-Flash

Fields drawn from vendor public documentation and the Modelspectra 22-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

Gemini 3.1 Pro leads the overall aggregate by 16 points (83 vs 67). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while DeepSeek-V4-Flash remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a modest gap: Gemini 3.1 Pro scores 74 against 65. On multi-file edits, SWE-style tickets and long-horizon agent loops Gemini 3.1 Pro needs fewer correction turns; DeepSeek-V4-Flash is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Gemini 3.1 Pro leads multimodal 92 vs 65. A concrete modality difference: Gemini 3.1 Pro additionally handles audio, image, video.

Speed & latency

DeepSeek-V4-Flash is faster in interactive use: ~85 tok/s with 0.3s TTFT versus ~60 tok/s with 0.7s TTFT (about 1.4× the throughput). For conversational UIs where perceived responsiveness drives retention, that edge is a real product factor even when raw reasoning is lower.

Context: window vs usable recall

Nominal windows are 2M for Gemini 3.1 Pro and 1M for DeepSeek-V4-Flash. Effective-context scores point the same way as window size — Gemini 3.1 Pro is ahead on usable recall (92 vs 90), so prefer it for long-document work where details cannot be missed.

Price & total cost

DeepSeek-V4-Flash is the cheaper API at $0.14/$0.28 versus Gemini 3.1 Pro at $2/$12 per 1M input/output tokens — list input is about 14.3× lower.

Chinese vs English

English: Gemini 3.1 Pro 90 vs DeepSeek-V4-Flash 75. Chinese: 75 vs 80. For Chinese-language production, DeepSeek-V4-Flash is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: Gemini 3.1 Pro 78 vs DeepSeek-V4-Flash 72, so Gemini 3.1 Pro has the edge on structured tool use. Fine-tuning is available from Gemini 3.1 Pro and DeepSeek-V4-Flash. Factor in existing SDK/plugin familiarity — switching cost often outweighs a few-point tool-use gap.

05

Cost worked example — same workload, real token math

Assume a production workload of 100M input + 30M output tokens per month, with 90% of input tokens served from cache. Figures use public list prices.

Scenario · per monthGemini 3.1 ProDeepSeek-V4-FlashGap
List priceno cache applied$560100M in × $2  +  30M out × $12$22100M in × $0.14  +  30M out × $0.2825.45×gap
With caching90% of inputs cache-hit$560no published cache discount$22no published cache discount25.45×gap

Illustrative model; your input/output mix and cache-hit ratio change the result. Prices are list rates before any enterprise agreement.

06

Decision tree

IF the workload is agentic or multi-file coding and a wrong first pass is expensive  →  choose Gemini 3.1 Pro (coding 74 vs 65).
IF you serve real-time users and latency is a product KPI  →  choose DeepSeek-V4-Flash (~85 tok/s, 0.3s TTFT).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose DeepSeek-V4-Flash ($$0.14/$$0.28 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose Gemini 3.1 Pro (effective context 92 vs 90).
07

Frequently asked questions

Is Gemini 3.1 Pro worth the higher price over DeepSeek-V4-Flash?
At list the input rate is 14.3x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when Gemini 3.1 Pro's stronger dimensions protect revenue; for routine volume DeepSeek-V4-Flash is the economical pick.
Does Gemini 3.1 Pro's higher refusal rate matter in production?
Gemini 3.1 Pro refuses about 12% of prompts versus 5% for DeepSeek-V4-Flash. In unattended pipelines that means more retries, fallbacks and manual review, raising effective cost and latency even when the token price is lower.
How do costs compare at 100M tokens/month with caching?
At 100M input + 30M output with 90% of inputs cache-hit, Gemini 3.1 Pro is about $560/month and DeepSeek-V4-Flash about $22/month after cache discounts ($560 and $22 at list).
Does the bigger context window actually matter?
Nominal windows are Gemini 3.1 Pro (2M) and DeepSeek-V4-Flash (1M), but usable recall follows the effective-context score (92 vs 90). Prefer the higher effective-context model for long-document work where nothing can be missed.
How do they differ for Chinese-language and data-residency use?
DeepSeek-V4-Flash is the Chinese model (Chinese score 80, domestic cloud, possible private deployment) while Gemini 3.1 Pro is the global model (Chinese 75, overseas API). Pick by language quality, access path and where data must reside.
Can I self-host either model?
DeepSeek-V4-Flash ships open weights and can be self-hosted (GPU permitting) for data control; Gemini 3.1 Pro is a closed managed API with no self-hosting. Choose open weights when residency or cost-at-scale dominates, managed API for convenience.