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Comparison · 2 models · Updated Nov 2, 2026

DeepSeek V4.1 Flash 1017 vs Gemini 3.7 Flash

DeepSeek V4.1 Flash 1017 comes out ahead, 79 to 74 on our weighted score, and it is the cheaper option too.

  1. Our pick

    DeepSeek

    DeepSeek V4.1 Flash 1017

    Released Oct 17, 2026New

    79/100
    • ECI158.8
    • Price$0.15 / $0.60
    • Context1M
    Full specs Remove DeepSeek V4.1 Flash 1017

  2. Google

    Gemini 3.7 Flash

    Released Aug 13, 2026

    74/100
    • ECI158.2
    • Price$0.75 / $3.75
    • Context1.05M
    Full specs Remove Gemini 3.7 Flash

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

DeepSeek V4.1 Flash 1017 is our pick

DeepSeek V4.1 Flash 1017 is the better all-round choice, scoring 79/100 against Gemini 3.7 Flash (74). It leads on price. Gemini 3.7 Flash wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityDeepSeek V4.1 Flash 1017Capabilities Index (ECI): DeepSeek V4.1 Flash 1017 158.8 · Gemini 3.7 Flash 158.2
  • Lowest priceDeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017 $0.263 · Gemini 3.7 Flash $1.50 per 1M tokens (3:1 blend)
  • Longest contextGemini 3.7 FlashGemini 3.7 Flash 1,048,576 · DeepSeek V4.1 Flash 1017 1,000,000 tokens
  • Widest inputsGemini 3.7 FlashDeepSeek V4.1 Flash 1017: Text, Images · Gemini 3.7 Flash: Text, Images, PDFs, Audio, Video
  • Self-hostingDeepSeek V4.1 Flash 1017Publishes downloadable weights (MIT)
How the score is built
MeasureWeightDeepSeek V4.1 Flash 1017Gemini 3.7 Flash
CapabilityCapabilities Index (ECI)50%8787
Price25%7440
Inputs & features15%70100
Context window10%6061
Overall100%79/10074/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

DeepSeek V4.1 Flash 1017 vs Gemini 3.7 Flash specifications side by side
SpecificationDeepSeek V4.1 Flash 1017DeepSeekGemini 3.7 FlashGoogle
Capability
Capabilities Index (ECI)158.8 (best)158.2
ECI rank#19 of 165 (best)#21 of 165
GPQA DiamondGraduate-level science questions93.2%94.8% (best)
FrontierMath Tiers 1–3Research-level mathematics71.2%71.6% (best)
OTIS Mock AIME 2024–2025Competition mathematics99.4% (best)97.2%
SimpleQA VerifiedShort factual questions42.5%69.2% (best)
Price per million tokens
Input$0.15 (best)$0.75
Output$0.60 (best)$3.75
Cached input$0.003 (best)$0.075
Blended (3:1)$0.263 (best)$1.50
Long-context rateSame rateSame rate
Price sourceOfficial DeepSeek APIOfficial Google API
Limits
Context window1,000,000 tokens1,048,576 tokens (best)
Max output384,000 tokens (best)65,536 tokens
Inputs and features
TextYesYes
ImagesYesYes
PDFsNoYes
AudioNoYes
VideoNoYes
ReasoningYeslow · high · maxYeslow · medium · high
Tool callingYesYes
Structured outputYesYes
Availability
WeightsOpenMITProprietary
API model IDdeepseek-flashgemini-3.7-flash
API providers40 (best)31
ReleasedOct 17, 2026Aug 13, 2026
Knowledge cutoffMay 2025Mar 2026
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • DeepSeek V4.1 Flash 1017$2.70
  • Gemini 3.7 Flash$15.00
04 — Questions

Which should you choose?

Which is better: DeepSeek V4.1 Flash 1017 or Gemini 3.7 Flash?

DeepSeek V4.1 Flash 1017 is the better all-round choice, scoring 79/100 against Gemini 3.7 Flash (74). It leads on price. Gemini 3.7 Flash wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, DeepSeek V4.1 Flash 1017 or Gemini 3.7 Flash?

DeepSeek V4.1 Flash 1017 is cheaper at $0.15 input / $0.60 output per million tokens (official DeepSeek API price). Gemini 3.7 Flash costs $0.75 input / $3.75 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for DeepSeek V4.1 Flash 1017 versus $1.50 for Gemini 3.7 Flash (5.7× as much).

Which scores higher on benchmarks?

DeepSeek V4.1 Flash 1017 scores higher on the Capabilities Index (ECI): DeepSeek V4.1 Flash 1017 158.8 (#19 of 165) and Gemini 3.7 Flash 158.2 (#21 of 165). The confidence ranges of the top two overlap (156.7–162.2 vs 156.2–161.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — Gemini 3.7 Flash 94.8%, DeepSeek V4.1 Flash 1017 93.2%; FrontierMath Tiers 1–3 — Gemini 3.7 Flash 71.6%, DeepSeek V4.1 Flash 1017 71.2%; OTIS Mock AIME 2024–2025 — DeepSeek V4.1 Flash 1017 99.4%, Gemini 3.7 Flash 97.2%; SimpleQA Verified — Gemini 3.7 Flash 69.2%, DeepSeek V4.1 Flash 1017 42.5%.

Which is better for coding?

There are no published SWE-bench Verified results for DeepSeek V4.1 Flash 1017 and Gemini 3.7 Flash yet, so there is no like-for-like coding score. On overall capability, DeepSeek V4.1 Flash 1017 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.

Which has the bigger context window?

Gemini 3.7 Flash has the largest context window at 1,048,576 tokens, against 1,000,000 for DeepSeek V4.1 Flash 1017. Maximum output per response: DeepSeek V4.1 Flash 1017 up to 384,000, Gemini 3.7 Flash up to 65,536 tokens.

Which can read images, PDFs, audio or video?

DeepSeek V4.1 Flash 1017 accepts text and images; Gemini 3.7 Flash accepts text, images, PDFs, audio and video. Gemini 3.7 Flash handles the widest range of inputs.

Are any of these open source?

DeepSeek V4.1 Flash 1017 publishes its weights (MIT) and can be self-hosted; Gemini 3.7 Flash is proprietary.

Which is newer?

DeepSeek V4.1 Flash 1017 is the newest, released Oct 17, 2026. Gemini 3.7 Flash came out Aug 13, 2026. Knowledge cutoff: DeepSeek V4.1 Flash 1017 May 2025, Gemini 3.7 Flash Mar 2026.

How do you decide the winner?

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.

05 — Keep comparing

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