ZMIME
Comparison · 3 models · Updated Nov 2, 2026

GLM-4.5 vs Kimi K2 Instruct 0905 vs Morph V3 Large

Too close to call on our weighted score (GLM-4.5 39, Kimi K2 Instruct 0905 38, Morph V3 Large 30). The right pick depends on what you value most.

  1. Z.ai (Zhipu)

    GLM-4.5

    Released Jul 28, 2025

    39/100
    • ECI—
    • Price$0.60 / $2.20
    • Context131K
  2. Moonshot AI

    Kimi K2 Instruct 0905

    Released Sep 5, 2025

    38/100
    • ECI—
    • Price$0.60 / $2.50
    • Context262K
  3. Morph

    Morph V3 Large

    Released Jul 7, 2025

    30/100
    • ECI—
    • Price$0.90 / $1.90
    • Context262K
01 — Verdict

Too close to call

It is close. Our weighted score puts them within 1 points (GLM-4.5 39/100, Kimi K2 Instruct 0905 38/100, Morph V3 Large 30/100), so choose by what matters most for your work: GLM-4.5 on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

  • CapabilityNot enough dataNo independent benchmark covers every model here yet
  • Lowest priceGLM-4.5GLM-4.5 $1.00 · Kimi K2 Instruct 0905 $1.07 · Morph V3 Large $1.15 per 1M tokens (3:1 blend)
  • Longest contextKimi K2 Instruct 0905 and Morph V3 LargeKimi K2 Instruct 0905 262,144 · Morph V3 Large 262,144 · GLM-4.5 131,072 tokens
  • Widest inputsSame inputsGLM-4.5: Text · Kimi K2 Instruct 0905: Text · Morph V3 Large: Text
  • Self-hostingGLM-4.5 and Kimi K2 Instruct 0905Publishes downloadable weights (Modified MIT)
How the score is built
MeasureWeightGLM-4.5Kimi K2 Instruct 0905Morph V3 Large
Price50%484645
Inputs & features30%35250
Context window20%253737
Overall100%39/10038/10030/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

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

GLM-4.5 vs Kimi K2 Instruct 0905 vs Morph V3 Large specifications side by side
SpecificationGLM-4.5Z.ai (Zhipu)Kimi K2 Instruct 0905Moonshot AIMorph V3 LargeMorph
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.60 (best)$0.60 (best)$0.90
Output$2.20$2.50$1.90 (best)
Cached input$0.11——
Blended (3:1)$1.00 (best)$1.07$1.15
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIMedian of 12 providersMedian of 5 providers
Limits
Context window131,072 tokens262,144 tokens (best)262,144 tokens (best)
Max output98,304 tokens98,304 tokens131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningYesNoNo
Tool callingYesYesNo
Structured outputNoNoNo
Availability
WeightsOpenOpenModified MITProprietary
API model IDglm-4.5——
API providers19 (best)135
ReleasedJul 28, 2025Sep 5, 2025Jul 7, 2025
Knowledge cutoffApr 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.

  • GLM-4.5$10.40
  • Kimi K2 Instruct 0905$11.00
  • Morph V3 Large$12.80
04 — Questions

Which should you choose?

Which is better: GLM-4.5, Kimi K2 Instruct 0905 or Morph V3 Large?

It is close. Our weighted score puts them within 1 points (GLM-4.5 39/100, Kimi K2 Instruct 0905 38/100, Morph V3 Large 30/100), so choose by what matters most for your work: GLM-4.5 on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

Which is cheaper, GLM-4.5, Kimi K2 Instruct 0905 or Morph V3 Large?

GLM-4.5 is cheaper at $0.60 input / $2.20 output per million tokens (official Z.AI API price). Kimi K2 Instruct 0905 costs $0.60 input / $2.50 output per million tokens (median across 12 API providers); Morph V3 Large costs $0.90 input / $1.90 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $1.00 per million tokens for GLM-4.5 versus $1.07 for Kimi K2 Instruct 0905 (1.1× as much) and $1.15 for Morph V3 Large (1.1× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. GLM-4.5 has not been scored yet, Kimi K2 Instruct 0905 has not been scored yet and Morph V3 Large has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for GLM-4.5, Kimi K2 Instruct 0905 and Morph V3 Large yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Morph V3 Large does not support tool calling, which most coding agents need.

Which has the bigger context window?

Kimi K2 Instruct 0905 and Morph V3 Large have the largest context windows (262,144 and 262,144 tokens), against 131,072 for GLM-4.5. Maximum output per response: GLM-4.5 up to 98,304, Kimi K2 Instruct 0905 up to 98,304, Morph V3 Large up to 131,072 tokens.

Which can read images, PDFs, audio or video?

GLM-4.5 accepts text; Kimi K2 Instruct 0905 accepts text; Morph V3 Large accepts text. They handle the same number of input types.

Are any of these open source?

GLM-4.5 and Kimi K2 Instruct 0905 publishes its weights (Modified MIT) and can be self-hosted; Morph V3 Large is proprietary.

Which is newer?

Kimi K2 Instruct 0905 is the newest, released Sep 5, 2025. GLM-4.5 came out Jul 28, 2025; Morph V3 Large came out Jul 7, 2025. Knowledge cutoff: GLM-4.5 Apr 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.