ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Kimi K2.7 Code vs GLM-5

Kimi K2.7 Code comes out ahead, 64 to 55 on our weighted score, though GLM-5 is 9% 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. Z.ai (Zhipu)

    GLM-5

    Released Feb 12, 2026

    55/100
    • ECI145.8
    • Price$1.00 / $3.20
    • Context205K
    Full specs Remove GLM-5

  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 GLM-5 (55). It leads on capability, inputs & features and context window. GLM-5 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 · GLM-5 145.8
  • Lowest priceGLM-5GLM-5 $1.55 · Kimi K2.7 Code $1.71 per 1M tokens (3:1 blend)
  • Longest contextKimi K2.7 CodeKimi K2.7 Code 262,144 · GLM-5 204,800 tokens
  • Widest inputsKimi K2.7 CodeKimi K2.7 Code: Text, Images, Video · GLM-5: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightKimi K2.7 CodeGLM-5
CapabilityCapabilities Index (ECI)50%7873
Price25%3941
Inputs & features15%8035
Context window10%3732
Overall100%64/10055/100
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 GLM-5 specifications side by side
SpecificationKimi K2.7 CodeMoonshot AIGLM-5Z.ai (Zhipu)
Capability
Capabilities Index (ECI)150.0 (best)145.8
ECI rank#49 of 148 (best)#74 of 148
GPQA DiamondGraduate-level science questions87.9% (best)87.8%
FrontierMath Tiers 1–3Research-level mathematics54.0%—
OTIS Mock AIME 2024–2025Competition mathematics95.6% (best)80.0%
SWE-bench VerifiedFixing real GitHub issues—72.1%
SimpleQA VerifiedShort factual questions36.5%—
Price per million tokens
Input$0.95 (best)$1.00
Output$4.00$3.20 (best)
Cached input$0.19 (best)$0.20
Blended (3:1)$1.71$1.55 (best)
Long-context rateSame rateSame rate
Price sourceOfficial Moonshot AI APIOfficial Z.AI API
Limits
Context window262,144 tokens (best)204,800 tokens
Max output262,144 tokens (best)131,072 tokens
Inputs and features
TextYesYes
ImagesYesNo
PDFsNoNo
AudioNoNo
VideoYesNo
ReasoningYesYes
Tool callingYesYes
Structured outputYesNo
Availability
WeightsOpenOpen
API model IDkimi-k2.7-codeglm-5
API providers51 (best)27
ReleasedJun 12, 2026Feb 12, 2026
Knowledge cutoffJan 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 K2.7 Code$17.50
  • GLM-5$16.40
04 — Questions

Which should you choose?

Which is better: Kimi K2.7 Code or GLM-5?

Kimi K2.7 Code is the better all-round choice, scoring 64/100 against GLM-5 (55). It leads on capability, inputs & features and context window. GLM-5 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Kimi K2.7 Code or GLM-5?

GLM-5 is cheaper at $1.00 input / $3.20 output per million tokens (official Z.AI API price). 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 $1.55 per million tokens for GLM-5 versus $1.71 for Kimi K2.7 Code (1.1× 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 GLM-5 145.8 (#74 of 148). Their confidence ranges do not overlap (148.1–151.8 vs 143.9–147.7), so the gap is a real one. On individual benchmarks: GPQA Diamond — Kimi K2.7 Code 87.9%, GLM-5 87.8%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, GLM-5 80.0%.

Which is better for coding?

There are no published SWE-bench Verified results for 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 204,800 for GLM-5. Maximum output per response: Kimi K2.7 Code up to 262,144, GLM-5 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Kimi K2.7 Code accepts text, images and video; GLM-5 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. GLM-5 came out Feb 12, 2026. Knowledge cutoff: 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

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