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

DeepSeek-V4.1-Flash vs Gemini 3.8 Flash

DeepSeek-V4.1-Flash wins on Overall, Coding; Gemini 3.8 Flash wins on Multimodal. Every numeric field is compared below, with a worked monthly-cost example and a pick rule for each use case.

VendorDeepSeek / Google
Data fields15+ dimensions
Updated2026-09-08
Read~9 min
01

Verdict at a glance

Bottom line: Choose DeepSeek-V4.1-Flash when coding depth, long-context reliability, lower refusal matter most; choose Gemini 3.8 Flash when its stronger dimensions is the priority.

Choose DeepSeek-V4.1-Flash if you…

  • 1M context at ~890 bytes/token global KV
  • Top agentic coding (DeepSWE 74.2, Terminal-Bench 2.1 90.6)
  • Persistent KV cut to ~1/8 via bounded replay
  • Open weights, extremely low serving cost
  • Best for: Long-context AI agents, Agentic coding, High-throughput deployment

Choose Gemini 3.8 Flash if you…

  • Best-coding Flash yet
  • Native text/image/audio/video
  • Very fast and low-priced
  • Stronger agentic reasoning
  • Best for: Fast tasks, Multimodal, Coding
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 27 tracked models.

DeepSeek-V4.1-Flash Higher overall
DeepSeek · #12 overall
Overall76
Coding89
Multimodal78
VS
Gemini 3.8 Flash
Google · #13 overall
Overall73
Coding73
Multimodal88

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.

DimensionDeepSeek-V4.1-FlashGemini 3.8 FlashVerdict
VendorDeepSeek (CN)Google (US)Different vendors
Released2026.092026.09Gemini 3.8 Flash is newer
Overall (rank)76 · #1273 · #13DeepSeek-V4.1-Flash +3
Coding89 · #573 · #14DeepSeek-V4.1-Flash +16
Multimodal78 · #1688 · #9Gemini 3.8 Flash +10
Context window1M1MTie
Max output128K64KGemini 3.8 Flash longer
Effective-context9890DeepSeek-V4.1-Flash more reliable
Input $/1M$0.14$0.75DeepSeek-V4.1-Flash cheaper
Output $/1M$0.28$3.75DeepSeek-V4.1-Flash cheaper
Cache discountnonenoneTie
Speed~85 tok/s~85 tok/sTie
TTFT0.3s0.3sTie
Function calling8478DeepSeek-V4.1-Flash ahead
Refusal rate~5%~10%DeepSeek-V4.1-Flash less restrictive
English8488Gemini 3.8 Flash
Chinese8674DeepSeek-V4.1-Flash
Modalitiestext, imagetext, image, audio, videodifferent coverage
Open weightsYesNoDeepSeek-V4.1-Flash is open
Fine-tuningYesYes
Free tierDeepSeek App free; open weights to self-hostGemini App free; API free tier; intro price through Dec 31, 2026
SOC2 / no-trainno / yesyes / yes
Private deploymentYesNoDeepSeek-V4.1-Flash

Fields drawn from vendor public documentation and the Modelspectra 27-model dataset; speed varies with network, concurrency and prompt length. Verify current pricing before purchase.

04

Dimension-by-dimension analysis

Reasoning & overall intelligence

DeepSeek-V4.1-Flash leads the overall aggregate by 3 points (76 vs 73). That is a meaningful, not marginal, edge on hard, multi-step reasoning, while Gemini 3.8 Flash remains a strong generalist that is not out of its depth on routine work.

Agentic coding

This is a clear gap: DeepSeek-V4.1-Flash scores 89 against 73. On multi-file edits, SWE-style tickets and long-horizon agent loops DeepSeek-V4.1-Flash needs fewer correction turns; Gemini 3.8 Flash is still competent for scripts and assisted completion but trails as task complexity rises.

Multimodal

Gemini 3.8 Flash leads multimodal 88 vs 78. A concrete modality difference: Gemini 3.8 Flash additionally handles audio, video.

Speed & latency

DeepSeek-V4.1-Flash is faster in interactive use: ~85 tok/s with 0.3s TTFT versus ~85 tok/s with 0.3s TTFT (about 1.0× 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 1M for DeepSeek-V4.1-Flash and 1M for Gemini 3.8 Flash. Effective-context scores point the same way as window size — DeepSeek-V4.1-Flash is ahead on usable recall (98 vs 90), so prefer it for long-document work where details cannot be missed.

Price & total cost

DeepSeek-V4.1-Flash is the cheaper API at $0.14/$0.28 versus Gemini 3.8 Flash at $0.75/$3.75 per 1M input/output tokens — list input is about 5.4× lower.

Chinese vs English

English: DeepSeek-V4.1-Flash 84 vs Gemini 3.8 Flash 88. Chinese: 86 vs 74. For Chinese-language production, DeepSeek-V4.1-Flash is the stronger pick. Note that non-Chinese models generally require overseas network access for their APIs.

Tool use & ecosystem

Function-calling score: DeepSeek-V4.1-Flash 84 vs Gemini 3.8 Flash 78, so DeepSeek-V4.1-Flash has the edge on structured tool use. Fine-tuning is available from DeepSeek-V4.1-Flash and Gemini 3.8 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 monthDeepSeek-V4.1-FlashGemini 3.8 FlashGap
List priceno cache applied$22100M in × $0.14  +  30M out × $0.28$188100M in × $0.75  +  30M out × $3.758.55×gap
With caching90% of inputs cache-hit$22no published cache discount$188no published cache discount8.55×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 DeepSeek-V4.1-Flash (coding 89 vs 73).
IF you are cost-driven at scale on routine, high-volume workloads  →  choose DeepSeek-V4.1-Flash ($$0.14/$$0.28 per 1M in/out).
IF long-document recall has to be near-perfect  →  choose DeepSeek-V4.1-Flash (effective context 98 vs 90).
IF the product is Chinese-first  →  choose DeepSeek-V4.1-Flash (Chinese 86 vs 74).
07

Frequently asked questions

Is Gemini 3.8 Flash worth the higher price over DeepSeek-V4.1-Flash?
At list the input rate is 5.4x higher, but cache discounts and output pricing narrow the effective gap. Pay the premium when Gemini 3.8 Flash's stronger dimensions protect revenue; for routine volume DeepSeek-V4.1-Flash is the economical pick.
Which is better for agentic coding?
DeepSeek-V4.1-Flash is decisively stronger for coding (89 vs 73 on the aggregate). The gap shows on SWE-style multi-file tasks and long agent loops that need fewer correction turns; Gemini 3.8 Flash is fine for routine scripts.
Does Gemini 3.8 Flash's higher refusal rate matter in production?
Gemini 3.8 Flash refuses about 10% of prompts versus 5% for DeepSeek-V4.1-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, DeepSeek-V4.1-Flash is about $22/month and Gemini 3.8 Flash about $188/month after cache discounts ($22 and $188 at list).
Does the bigger context window actually matter?
Nominal windows are DeepSeek-V4.1-Flash (1M) and Gemini 3.8 Flash (1M), but usable recall follows the effective-context score (98 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-V4.1-Flash is the Chinese model (Chinese score 86, domestic cloud, possible private deployment) while Gemini 3.8 Flash is the global model (Chinese 74, overseas API). Pick by language quality, access path and where data must reside.