ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Kimi K2.7 Code vs DeepSeek-R1

Kimi K2.7 Code comes out ahead, 64 to 51 on our weighted score, though DeepSeek-R1 is 31% cheaper per token.

  1. Our pick

    Moonshot AI

    Kimi K2.7 Code

    Released Jun 12, 2026

    64/100
    • ECI150.0
    • Price$0.95 / $4.00
    • Context262K
    Full specs Remove Kimi K2.7 Code

  2. DeepSeek

    DeepSeek-R1

    Released Jan 20, 2025

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

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Kimi K2.7 Code is our pick

Kimi K2.7 Code is the better all-round choice, scoring 64/100 against DeepSeek-R1 (51). It leads on capability, inputs & features and context window. DeepSeek-R1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityKimi K2.7 CodeCapabilities Index (ECI): Kimi K2.7 Code 150.0 · DeepSeek-R1 139.0
  • Lowest priceDeepSeek-R1DeepSeek-R1 $1.18 · Kimi K2.7 Code $1.71 per 1M tokens (3:1 blend)
  • Longest contextKimi K2.7 CodeKimi K2.7 Code 262,144 · DeepSeek-R1 128,000 tokens
  • Widest inputsKimi K2.7 CodeKimi K2.7 Code: Text, Images, Video · DeepSeek-R1: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightKimi K2.7 CodeDeepSeek-R1
CapabilityCapabilities Index (ECI)50%7864
Price25%3947
Inputs & features15%8035
Context window10%3724
Overall100%64/10051/100
02 — Side by side

Every spec in one table

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

Kimi K2.7 Code vs DeepSeek-R1 specifications side by side
SpecificationKimi K2.7 CodeMoonshot AIDeepSeek-R1DeepSeek
Capability
Capabilities Index (ECI)150.0 (best)139.0
ECI rank#49 of 148 (best)#104 of 148
GPQA DiamondGraduate-level science questions87.9% (best)71.7%
FrontierMath Tiers 1–3Research-level mathematics54.0%—
OTIS Mock AIME 2024–2025Competition mathematics95.6% (best)53.3%
SimpleQA VerifiedShort factual questions36.5%—
Price per million tokens
Input$0.95$0.70 (best)
Output$4.00$2.60 (best)
Cached input$0.19—
Blended (3:1)$1.71$1.18 (best)
Long-context rateSame rateSame rate
Price sourceOfficial Moonshot AI APIMedian of 11 providers
Limits
Context window262,144 tokens (best)128,000 tokens
Max output262,144 tokens (best)32,768 tokens
Inputs and features
TextYesYes
ImagesYesNo
PDFsNoNo
AudioNoNo
VideoYesNo
ReasoningYesYes
Tool callingYesYes
Structured outputYesNo
Availability
WeightsOpenOpen
API model IDkimi-k2.7-code—
API providers51 (best)12
ReleasedJun 12, 2026Jan 20, 2025
Knowledge cutoffJan 2025Jul 2024
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 K2.7 Code$17.50
  • DeepSeek-R1$12.20
04 — Questions

Which should you choose?

Which is better: Kimi K2.7 Code or DeepSeek-R1?

Kimi K2.7 Code is the better all-round choice, scoring 64/100 against DeepSeek-R1 (51). It leads on capability, inputs & features and context window. DeepSeek-R1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Kimi K2.7 Code or DeepSeek-R1?

DeepSeek-R1 is cheaper at $0.70 input / $2.60 output per million tokens (median across 11 API providers). Kimi K2.7 Code costs $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). At a typical mix of three input tokens to one output token, that is $1.18 per million tokens for DeepSeek-R1 versus $1.71 for Kimi K2.7 Code (1.5× as much).

Which scores higher on benchmarks?

Kimi K2.7 Code scores higher on the Capabilities Index (ECI): Kimi K2.7 Code 150.0 (#49 of 148) and DeepSeek-R1 139.0 (#104 of 148). Their confidence ranges do not overlap (148.1–151.8 vs 136.2–140.4), so the gap is a real one. On individual benchmarks: GPQA Diamond — Kimi K2.7 Code 87.9%, DeepSeek-R1 71.7%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, DeepSeek-R1 53.3%.

Which is better for coding?

There are no published SWE-bench Verified results for Kimi K2.7 Code and DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, Kimi K2.7 Code 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?

Kimi K2.7 Code has the largest context window at 262,144 tokens, against 128,000 for DeepSeek-R1. Maximum output per response: Kimi K2.7 Code up to 262,144, DeepSeek-R1 up to 32,768 tokens.

Which can read images, PDFs, audio or video?

Kimi K2.7 Code accepts text, images and video; DeepSeek-R1 accepts text. Kimi K2.7 Code handles the widest range of inputs.

Are any of these open source?

Yes, both publish their weights, so you can self-host them.

Which is newer?

Kimi K2.7 Code is the newest, released Jun 12, 2026. DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: Kimi K2.7 Code Jan 2025, DeepSeek-R1 Jul 2024.

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

Related comparisons