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Comparison · 3 models · Updated Nov 2, 2026

Kimi K2 Instruct vs Kimi K2.5 vs Gemini 2.5 Flash

Gemini 2.5 Flash comes out ahead, 66 to 62 and 50 on our weighted score, and it is the cheaper option too.

  1. Moonshot AI

    Kimi K2 Instruct

    Released Jul 11, 2025

    50/100
    • ECI139.9
    • Price$0.57 / $2.30
    • Context131K
  2. Moonshot AI

    Kimi K2.5

    Released Jan 27, 2026

    62/100
    • ECI148.1
    • Price$0.60 / $3.00
    • Context262K
  3. Our pick

    Google

    Gemini 2.5 Flash

    Released Jun 17, 2025

    66/100
    • ECI140.6
    • Price$0.30 / $2.50
    • Context1.05M
01 — Verdict

Gemini 2.5 Flash is our pick

Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against Kimi K2.5 (62) and Kimi K2 Instruct (50). It leads on price, inputs & features and context window. Kimi K2.5 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2.5Capabilities Index (ECI): Kimi K2.5 148.1 · Gemini 2.5 Flash 140.6 · Kimi K2 Instruct 139.9
  • Lowest priceGemini 2.5 FlashGemini 2.5 Flash $0.85 · Kimi K2 Instruct $1.00 · Kimi K2.5 $1.20 per 1M tokens (3:1 blend)
  • Longest contextGemini 2.5 FlashGemini 2.5 Flash 1,048,576 · Kimi K2.5 262,144 · Kimi K2 Instruct 131,072 tokens
  • Widest inputsGemini 2.5 FlashKimi K2 Instruct: Text · Kimi K2.5: Text, Images, Video · Gemini 2.5 Flash: Text, Images, PDFs, Audio, Video
  • Self-hostingKimi K2 Instruct and Kimi K2.5Publishes downloadable weights (Modified MIT)
How the score is built
MeasureWeightKimi K2 InstructKimi K2.5Gemini 2.5 Flash
CapabilityCapabilities Index (ECI)50%647465
Price25%484451
Inputs & features15%2570100
Context window10%253761
Overall100%50/10062/10066/100
02 — Side by side

Every spec in one table

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

Kimi K2 Instruct vs Kimi K2.5 vs Gemini 2.5 Flash specifications side by side
SpecificationKimi K2 InstructMoonshot AIKimi K2.5Moonshot AIGemini 2.5 FlashGoogle
Capability
Capabilities Index (ECI)139.9148.1 (best)140.6
ECI rank#112 of 165#68 of 165 (best)#110 of 165
GPQA DiamondGraduate-level science questions—87.6%—
OTIS Mock AIME 2024–2025Competition mathematics—92.2%—
SWE-bench VerifiedFixing real GitHub issues—73.8%—
SimpleQA VerifiedShort factual questions—34.3%—
Price per million tokens
Input$0.57$0.60$0.30 (best)
Output$2.30 (best)$3.00$2.50
Cached input——$0.03
Blended (3:1)$1.00$1.20$0.85 (best)
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 11 providersMedian of 33 providersOfficial Google API
Limits
Context window131,072 tokens262,144 tokens1,048,576 tokens (best)
Max output32,768 tokens262,144 tokens (best)65,536 tokens
Inputs and features
TextYesYesYes
ImagesNoYesYes
PDFsNoNoYes
AudioNoNoYes
VideoNoYesYes
ReasoningNoYesYes
Tool callingYesYesYes
Structured outputNoNoYes
Availability
WeightsOpenModified MITOpenProprietary
API model ID——gemini-2.5-flash
API providers1133 (best)32
ReleasedJul 11, 2025Jan 27, 2026Jun 17, 2025
Knowledge cutoff—Jan 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.

  • Kimi K2 Instruct$10.30
  • Kimi K2.5$12.00
  • Gemini 2.5 Flash$8.00
04 — Questions

Which should you choose?

Which is better: Kimi K2 Instruct, Kimi K2.5 or Gemini 2.5 Flash?

Gemini 2.5 Flash is the better all-round choice, scoring 66/100 against Kimi K2.5 (62) and Kimi K2 Instruct (50). It leads on price, inputs & features and context window. Kimi K2.5 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Kimi K2 Instruct, Kimi K2.5 or Gemini 2.5 Flash?

Gemini 2.5 Flash is cheaper at $0.30 input / $2.50 output per million tokens (official Google API price). Kimi K2 Instruct costs $0.57 input / $2.30 output per million tokens (median across 11 API providers); Kimi K2.5 costs $0.60 input / $3.00 output per million tokens (median across 33 API providers). At a typical mix of three input tokens to one output token, that is $0.85 per million tokens for Gemini 2.5 Flash versus $1.00 for Kimi K2 Instruct (1.2× as much) and $1.20 for Kimi K2.5 (1.4× as much).

Which scores higher on benchmarks?

Kimi K2.5 scores higher on the Capabilities Index (ECI): Kimi K2.5 148.1 (#68 of 165), Gemini 2.5 Flash 140.6 (#110 of 165) and Kimi K2 Instruct 139.9 (#112 of 165). Their confidence ranges do not overlap (146.3–149.5 vs 138.4–142.1), so the gap is a real one.

Which is better for coding?

There are no published SWE-bench Verified results for Kimi K2 Instruct and Gemini 2.5 Flash yet, so there is no like-for-like coding score. On overall capability, Kimi K2.5 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 262,144 for Kimi K2.5 and 131,072 for Kimi K2 Instruct. Maximum output per response: Kimi K2 Instruct up to 32,768, Kimi K2.5 up to 262,144, Gemini 2.5 Flash up to 65,536 tokens.

Which can read images, PDFs, audio or video?

Kimi K2 Instruct accepts text; Kimi K2.5 accepts text, images and video; 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?

Kimi K2 Instruct and Kimi K2.5 publishes its weights (Modified MIT) and can be self-hosted; Gemini 2.5 Flash is proprietary.

Which is newer?

Kimi K2.5 is the newest, released Jan 27, 2026. Kimi K2 Instruct came out Jul 11, 2025; Gemini 2.5 Flash came out Jun 17, 2025. Knowledge cutoff: Kimi K2.5 Jan 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 Nov 2, 2026.