ZMIME
Comparison · 2 models · Updated Oct 4, 2026

Kimi K2.6 vs GLM-5.3

Too close to call on our weighted score (Kimi K2.6 65, GLM-5.3 64). The right pick depends on what you value most.

  1. Moonshot AI

    Kimi K2.6

    Released Apr 21, 2026

    65/100
    • ECI151.1
    • Price$0.95 / $4.00
    • Context262K
    Full specs Remove Kimi K2.6

  2. Z.ai (Zhipu)

    GLM-5.3

    Released Aug 14, 2026

    64/100
    • ECI155.8
    • Price$1.40 / $4.40
    • Context1M
    Full specs Remove GLM-5.3

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Too close to call

It is close. Our weighted score puts them within 1 points (Kimi K2.6 65/100, GLM-5.3 64/100), so choose by what matters most for your work: GLM-5.3 for raw capability and Kimi K2.6 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityGLM-5.3Capabilities Index (ECI): GLM-5.3 155.8 · Kimi K2.6 151.1
  • Lowest priceKimi K2.6Kimi K2.6 $1.71 · GLM-5.3 $2.15 per 1M tokens (3:1 blend)
  • Longest contextGLM-5.3GLM-5.3 1,000,000 · Kimi K2.6 262,144 tokens
  • Widest inputsKimi K2.6Kimi K2.6: Text, Images, Video · GLM-5.3: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightKimi K2.6GLM-5.3
CapabilityCapabilities Index (ECI)50%7985
Price25%3934
Inputs & features15%8045
Context window10%3760
Overall100%65/10064/100
02 — Side by side

Every spec in one table

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

Kimi K2.6 vs GLM-5.3 specifications side by side
SpecificationKimi K2.6Moonshot AIGLM-5.3Z.ai (Zhipu)
Capability
Capabilities Index (ECI)151.1155.8 (best)
ECI rank#45 of 148#24 of 148 (best)
GPQA DiamondGraduate-level science questions90.8%90.9% (best)
FrontierMath Tiers 1–3Research-level mathematics57.2%68.8% (best)
OTIS Mock AIME 2024–2025Competition mathematics96.1% (best)91.1%
SWE-bench VerifiedFixing real GitHub issues76.7%—
SimpleQA VerifiedShort factual questions34.9%41.0% (best)
Price per million tokens
Input$0.95 (best)$1.40
Output$4.00 (best)$4.40
Cached input$0.16 (best)$0.26
Blended (3:1)$1.71 (best)$2.15
Long-context rateSame rateSame rate
Price sourceOfficial Moonshot AI APIOfficial Z.AI API
Limits
Context window262,144 tokens1,000,000 tokens (best)
Max output262,144 tokens (best)131,072 tokens
Inputs and features
TextYesYes
ImagesYesNo
PDFsNoNo
AudioNoNo
VideoYesNo
ReasoningYesYeslow · high · max
Tool callingYesYes
Structured outputYesYes
Availability
WeightsOpenOpen
API model IDkimi-k2.6glm-5.3
API providers4662 (best)
ReleasedApr 21, 2026Aug 14, 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 K2.6$17.50
  • GLM-5.3$22.80
04 — Questions

Which should you choose?

Which is better: Kimi K2.6 or GLM-5.3?

It is close. Our weighted score puts them within 1 points (Kimi K2.6 65/100, GLM-5.3 64/100), so choose by what matters most for your work: GLM-5.3 for raw capability and Kimi K2.6 on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Kimi K2.6 or GLM-5.3?

Kimi K2.6 is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). GLM-5.3 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $1.71 per million tokens for Kimi K2.6 versus $2.15 for GLM-5.3 (1.3× as much).

Which scores higher on benchmarks?

GLM-5.3 scores higher on the Capabilities Index (ECI): GLM-5.3 155.8 (#24 of 148) and Kimi K2.6 151.1 (#45 of 148). Their confidence ranges do not overlap (153.7–158.3 vs 149.1–152.8), so the gap is a real one. On individual benchmarks: GPQA Diamond — GLM-5.3 90.9%, Kimi K2.6 90.8%; FrontierMath Tiers 1–3 — GLM-5.3 68.8%, Kimi K2.6 57.2%; OTIS Mock AIME 2024–2025 — Kimi K2.6 96.1%, GLM-5.3 91.1%; SimpleQA Verified — GLM-5.3 41.0%, Kimi K2.6 34.9%.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-5.3 yet, so there is no like-for-like coding score. On overall capability, GLM-5.3 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?

GLM-5.3 has the largest context window at 1,000,000 tokens, against 262,144 for Kimi K2.6. Maximum output per response: Kimi K2.6 up to 262,144, GLM-5.3 up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Kimi K2.6 accepts text, images and video; GLM-5.3 accepts text. Kimi K2.6 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?

GLM-5.3 is the newest, released Aug 14, 2026. Kimi K2.6 came out Apr 21, 2026. Knowledge cutoff: Kimi K2.6 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