ZMIME
Comparison · 2 models · Updated Oct 4, 2026

DeepSeek-R1 vs Kimi K2.7 Code

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

  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 CodeDeepSeek-R1: Text · Kimi K2.7 Code: Text, Images, Video
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightDeepSeek-R1Kimi K2.7 Code
CapabilityCapabilities Index (ECI)50%6478
Price25%4739
Inputs & features15%3580
Context window10%2437
Overall100%51/10064/100
02 — Side by side

Every spec in one table

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

DeepSeek-R1 vs Kimi K2.7 Code specifications side by side
SpecificationDeepSeek-R1DeepSeekKimi K2.7 CodeMoonshot AI
Capability
Capabilities Index (ECI)139.0150.0 (best)
ECI rank#104 of 148#49 of 148 (best)
GPQA DiamondGraduate-level science questions71.7%87.9% (best)
FrontierMath Tiers 1–3Research-level mathematics—54.0%
OTIS Mock AIME 2024–2025Competition mathematics53.3%95.6% (best)
SimpleQA VerifiedShort factual questions—36.5%
Price per million tokens
Input$0.70 (best)$0.95
Output$2.60 (best)$4.00
Cached input—$0.19
Blended (3:1)$1.18 (best)$1.71
Long-context rateSame rateSame rate
Price sourceMedian of 11 providersOfficial Moonshot AI API
Limits
Context window128,000 tokens262,144 tokens (best)
Max output32,768 tokens262,144 tokens (best)
Inputs and features
TextYesYes
ImagesNoYes
PDFsNoNo
AudioNoNo
VideoNoYes
ReasoningYesYes
Tool callingYesYes
Structured outputNoYes
Availability
WeightsOpenOpen
API model ID—kimi-k2.7-code
API providers1251 (best)
ReleasedJan 20, 2025Jun 12, 2026
Knowledge cutoffJul 2024Jan 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
  • Kimi K2.7 Code$17.50
04 — Questions

Which should you choose?

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

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, DeepSeek-R1 or Kimi K2.7 Code?

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 DeepSeek-R1 and Kimi K2.7 Code 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: DeepSeek-R1 up to 32,768, Kimi K2.7 Code up to 262,144 tokens.

Which can read images, PDFs, audio or video?

DeepSeek-R1 accepts text; Kimi K2.7 Code accepts text, images and video. 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: DeepSeek-R1 Jul 2024, Kimi K2.7 Code 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

Related comparisons