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Comparison · 3 models · Updated Oct 4, 2026

DeepSeek-R1 vs DeepSeek-V3.1 vs Gemini 2.5 Flash

Gemini 2.5 Flash comes out ahead, 68 to 55 and 51 on our weighted score, though DeepSeek-V3.1 is 29% cheaper per token.

  1. DeepSeek

    DeepSeek-R1

    Released Jan 20, 2025

    51/100
    • ECI139.0
    • Price$0.70 / $2.60
    • Context128K
    Full specs Remove DeepSeek-R1

  2. DeepSeek

    DeepSeek-V3.1

    Released Aug 21, 2025

    55/100
    • ECI139.9
    • Price$0.385 / $1.25
    • Context131K
    Full specs Remove DeepSeek-V3.1
  3. Our pick

    Google

    Gemini 2.5 Flash

    Released Jun 17, 2025

    68/100
    • ECI140.8
    • Price$0.30 / $2.50
    • Context1.05M
    Full specs Remove Gemini 2.5 Flash
01 — Verdict

Gemini 2.5 Flash is our pick

Gemini 2.5 Flash is the better all-round choice, scoring 68/100 against DeepSeek-V3.1 (55) and DeepSeek-R1 (51). It leads on inputs & features and context window. DeepSeek-V3.1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGemini 2.5 FlashCapabilities Index (ECI): Gemini 2.5 Flash 140.8 · DeepSeek-V3.1 139.9 · DeepSeek-R1 139.0
  • Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · Gemini 2.5 Flash $0.85 · DeepSeek-R1 $1.18 per 1M tokens (3:1 blend)
  • Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · DeepSeek-V3.1 131,072 · DeepSeek-R1 128,000 tokens
  • Widest inputsGemini 2.5 FlashDeepSeek-R1: Text · DeepSeek-V3.1: Text · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video
  • Self-hostingDeepSeek-R1 and DeepSeek-V3.1Publishes downloadable weights (MIT License)
How the score is built
MeasureWeightDeepSeek-R1DeepSeek-V3.1Gemini 2.5 Flash
CapabilityCapabilities Index (ECI)50%646567
Price25%476053
Inputs & features15%3535100
Context window10%242461
Overall100%51/10055/10068/100
02 — Side by side

Every spec in one table

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

DeepSeek-R1 vs DeepSeek-V3.1 vs Gemini 2.5 Flash specifications side by side
SpecificationDeepSeek-R1DeepSeekDeepSeek-V3.1DeepSeekGemini 2.5 FlashGoogle
Capability
Capabilities Index (ECI)139.0139.9140.8 (best)
ECI rank#104 of 148#100 of 148#97 of 148 (best)
GPQA DiamondGraduate-level science questions71.7%——
OTIS Mock AIME 2024–2025Competition mathematics53.3%——
Price per million tokens
Input$0.70$0.385$0.30 (best)
Output$2.60$1.25 (best)$2.50
Cached input——$0.03
Blended (3:1)$1.18$0.601 (best)$0.85
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 11 providersMedian of 8 providersOfficial Google API
Limits
Context window128,000 tokens131,072 tokens1,048,576 tokens (best)
Max output32,768 tokens8,192 tokens65,536 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoNoYes
PDFsNoNoYes
AudioNoNoYes
VideoNoNoYes
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoYes
Availability
WeightsOpenOpenMIT LicenseProprietary
API model ID——gemini-2.5-flash
API providers12822 (best)
ReleasedJan 20, 2025Aug 21, 2025Jun 17, 2025
Knowledge cutoffJul 2024—Jan 2025
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-R1$12.20
  • DeepSeek-V3.1$6.35
  • Gemini 2.5 Flash$8.00
04 — Questions

Which should you choose?

Which is better: DeepSeek-R1, DeepSeek-V3.1 or Gemini 2.5 Flash?

Gemini 2.5 Flash is the better all-round choice, scoring 68/100 against DeepSeek-V3.1 (55) and DeepSeek-R1 (51). It leads on inputs & features and context window. DeepSeek-V3.1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, DeepSeek-R1, DeepSeek-V3.1 or Gemini 2.5 Flash?

DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). Gemini 2.5 Flash costs $0.30 input / $2.50 output per million tokens (official Google API price); DeepSeek-R1 costs $0.70 input / $2.60 output per million tokens (median across 11 API providers). At a typical mix of three input tokens to one output token, that is $0.601 per million tokens for DeepSeek-V3.1 versus $0.85 for Gemini 2.5 Flash (1.4× as much) and $1.18 for DeepSeek-R1 (2× as much).

Which scores higher on benchmarks?

Gemini 2.5 Flash scores higher on the Capabilities Index (ECI): Gemini 2.5 Flash 140.8 (#97 of 148), DeepSeek-V3.1 139.9 (#100 of 148) and DeepSeek-R1 139.0 (#104 of 148). The confidence ranges of the top two overlap (138.5–142.3 vs 136.1–143.3), so treat the gap as small.

Which is better for coding?

There are no published SWE-bench Verified results for DeepSeek-R1, DeepSeek-V3.1 and Gemini 2.5 Flash yet, so there is no like-for-like coding score. On overall capability, Gemini 2.5 Flash leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.

Which has the bigger context window?

Gemini 2.5 Flash has the largest context window at 1,048,576 tokens, against 131,072 for DeepSeek-V3.1 and 128,000 for DeepSeek-R1. Maximum output per response: DeepSeek-R1 up to 32,768, DeepSeek-V3.1 up to 8,192, Gemini 2.5 Flash up to 65,536 tokens.

Which can read images, PDFs, audio or video?

DeepSeek-R1 accepts text; DeepSeek-V3.1 accepts text; Gemini 2.5 Flash accepts text, images, PDFs, audio and video. Gemini 2.5 Flash handles the widest range of inputs.

Are any of these open source?

DeepSeek-R1 and DeepSeek-V3.1 publishes its weights (MIT License) and can be self-hosted; Gemini 2.5 Flash is proprietary.

Which is newer?

DeepSeek-V3.1 is the newest, released Aug 21, 2025. Gemini 2.5 Flash came out Jun 17, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: DeepSeek-R1 Jul 2024, Gemini 2.5 Flash Jan 2025.

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 Oct 4, 2026.

05 — Keep comparing

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