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Comparison · 3 models · Updated Oct 4, 2026

Qwen3 14B vs Gemini 2.5 Flash vs DeepSeek-V3.1

Gemini 2.5 Flash comes out ahead, 68 to 55 and 54 on our weighted score, though DeepSeek-V3.1 is 29% cheaper per token.

  1. Alibaba (Qwen)

    Qwen3 14B

    Released Apr 29, 2025

    54/100
    • ECI138.2
    • Price$0.35 / $1.40
    • Context131K
    Full specs Remove Qwen3 14B
  2. 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

  3. DeepSeek

    DeepSeek-V3.1

    Released Aug 21, 2025

    55/100
    • ECI139.9
    • Price$0.385 / $1.25
    • Context131K
    Full specs Remove DeepSeek-V3.1
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 Qwen3 14B (54). It leads on inputs & features and context window. 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 · Qwen3 14B 138.2
  • Lowest priceDeepSeek-V3.1DeepSeek-V3.1 $0.601 · Qwen3 14B $0.613 · Gemini 2.5 Flash $0.85 per 1M tokens (3:1 blend)
  • Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · Qwen3 14B 131,072 · DeepSeek-V3.1 131,072 tokens
  • Widest inputsGemini 2.5 FlashQwen3 14B: Text · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video · DeepSeek-V3.1: Text
  • Self-hostingQwen3 14B and DeepSeek-V3.1Publishes downloadable weights (MIT License)
How the score is built
MeasureWeightQwen3 14BGemini 2.5 FlashDeepSeek-V3.1
CapabilityCapabilities Index (ECI)50%636765
Price25%605360
Inputs & features15%3510035
Context window10%246124
Overall100%54/10068/10055/100
02 — Side by side

Every spec in one table

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

Qwen3 14B vs Gemini 2.5 Flash vs DeepSeek-V3.1 specifications side by side
SpecificationQwen3 14BAlibaba (Qwen)Gemini 2.5 FlashGoogleDeepSeek-V3.1DeepSeek
Capability
Capabilities Index (ECI)138.2140.8 (best)139.9
ECI rank#107 of 148#97 of 148 (best)#100 of 148
GPQA DiamondGraduate-level science questions63.8%——
OTIS Mock AIME 2024–2025Competition mathematics66.4%——
Price per million tokens
Input$0.35$0.30 (best)$0.385
Output$1.40$2.50$1.25 (best)
Cached input—$0.03—
Blended (3:1)$0.613$0.85$0.601 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Alibaba APIOfficial Google APIMedian of 8 providers
Limits
Context window131,072 tokens1,048,576 tokens (best)131,072 tokens
Max output8,192 tokens65,536 tokens (best)8,192 tokens
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoYesNo
AudioNoYesNo
VideoNoYesNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoYesNo
Availability
WeightsOpenProprietaryOpenMIT License
API model IDqwen3-14bgemini-2.5-flash—
API providers122 (best)8
ReleasedApr 29, 2025Jun 17, 2025Aug 21, 2025
Knowledge cutoffApr 2025Jan 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.

  • Qwen3 14B$6.30
  • Gemini 2.5 Flash$8.00
  • DeepSeek-V3.1$6.35
04 — Questions

Which should you choose?

Which is better: Qwen3 14B, Gemini 2.5 Flash or DeepSeek-V3.1?

Gemini 2.5 Flash is the better all-round choice, scoring 68/100 against DeepSeek-V3.1 (55) and Qwen3 14B (54). It leads on inputs & features and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Qwen3 14B, Gemini 2.5 Flash or DeepSeek-V3.1?

DeepSeek-V3.1 is cheaper at $0.385 input / $1.25 output per million tokens (median across 8 API providers). Qwen3 14B costs $0.35 input / $1.40 output per million tokens (official Alibaba API price); Gemini 2.5 Flash costs $0.30 input / $2.50 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $0.601 per million tokens for DeepSeek-V3.1 versus $0.613 for Qwen3 14B (1× as much) and $0.85 for Gemini 2.5 Flash (1.4× 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 Qwen3 14B 138.2 (#107 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 Qwen3 14B, Gemini 2.5 Flash and DeepSeek-V3.1 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 Qwen3 14B and 131,072 for DeepSeek-V3.1. Maximum output per response: Qwen3 14B up to 8,192, Gemini 2.5 Flash up to 65,536, DeepSeek-V3.1 up to 8,192 tokens.

Which can read images, PDFs, audio or video?

Qwen3 14B accepts text; Gemini 2.5 Flash accepts text, images, PDFs, audio and video; DeepSeek-V3.1 accepts text. Gemini 2.5 Flash handles the widest range of inputs.

Are any of these open source?

Qwen3 14B 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; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: Qwen3 14B Apr 2025, 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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