ZMIME
Comparison · 3 models · Updated Oct 4, 2026

GLM-5.1 vs Claude Opus 4.5 vs Kimi K2.7 Code

Kimi K2.7 Code comes out ahead, 64 to 57 and 55 on our weighted score, and it is the cheaper option too.

  1. Z.ai (Zhipu)

    GLM-5.1

    Released Apr 7, 2026

    57/100
    • ECI149.9
    • Price$1.40 / $4.40
    • Context200K
  2. Anthropic

    Claude Opus 4.5

    Released Nov 24, 2025

    55/100
    • ECI150.1
    • Price$5.00 / $25.00
    • Context200K
  3. Our pick

    Moonshot AI

    Kimi K2.7 Code

    Released Jun 12, 2026

    64/100
    • ECI150.0
    • Price$0.95 / $4.00
    • Context262K
01 — Verdict

Kimi K2.7 Code is our pick

Kimi K2.7 Code is the better all-round choice, scoring 64/100 against GLM-5.1 (57) and Claude Opus 4.5 (55). It leads on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

  • CapabilityClaude Opus 4.5Capabilities Index (ECI): Claude Opus 4.5 150.1 · Kimi K2.7 Code 150.0 · GLM-5.1 149.9
  • Lowest priceKimi K2.7 CodeKimi K2.7 Code $1.71 · GLM-5.1 $2.15 · Claude Opus 4.5 $10.00 per 1M tokens (3:1 blend)
  • Longest contextKimi K2.7 CodeKimi K2.7 Code 262,144 · GLM-5.1 200,000 · Claude Opus 4.5 200,000 tokens
  • Widest inputsClaude Opus 4.5 and Kimi K2.7 CodeGLM-5.1: Text · Claude Opus 4.5: Text, Images, PDFs · Kimi K2.7 Code: Text, Images, Video
  • Self-hostingGLM-5.1 and Kimi K2.7 CodePublishes downloadable weights
How the score is built
MeasureWeightGLM-5.1Claude Opus 4.5Kimi K2.7 Code
CapabilityCapabilities Index (ECI)50%787878
Price25%34239
Inputs & features15%458080
Context window10%323237
Overall100%57/10055/10064/100
02 — Side by side

Every spec in one table

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

GLM-5.1 vs Claude Opus 4.5 vs Kimi K2.7 Code specifications side by side
SpecificationGLM-5.1Z.ai (Zhipu)Claude Opus 4.5AnthropicKimi K2.7 CodeMoonshot AI
Capability
Capabilities Index (ECI)149.9150.1 (best)150.0
ECI rank#51 of 148#48 of 148 (best)#49 of 148
GPQA DiamondGraduate-level science questions89.9% (best)86.1%87.9%
FrontierMath Tiers 1–3Research-level mathematics36.8%34.4%54.0% (best)
OTIS Mock AIME 2024–2025Competition mathematics93.3%86.1%95.6% (best)
SWE-bench VerifiedFixing real GitHub issues74.2%76.7% (best)—
SimpleQA VerifiedShort factual questions34.0%45.7% (best)36.5%
Price per million tokens
Input$1.40$5.00$0.95 (best)
Output$4.40$25.00$4.00 (best)
Cached input$0.26$0.50$0.19 (best)
Blended (3:1)$2.15$10.00$1.71 (best)
Long-context rateSame rateSame rateSame rate
Price sourceOfficial Z.AI APIOfficial Anthropic APIOfficial Moonshot AI API
Limits
Context window200,000 tokens200,000 tokens262,144 tokens (best)
Max output131,072 tokens64,000 tokens262,144 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesYes
PDFsNoYesNo
AudioNoNoNo
VideoNoNoYes
ReasoningYesYeslow · medium · highYes
Tool callingYesYesYes
Structured outputYesYesYes
Availability
WeightsOpenProprietaryOpen
API model IDglm-5.1claude-opus-4-5kimi-k2.7-code
API providers402851 (best)
ReleasedApr 7, 2026Nov 24, 2025Jun 12, 2026
Knowledge cutoff—May 2025Jan 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-5.1$22.80
  • Claude Opus 4.5$100.00
  • Kimi K2.7 Code$17.50
04 — Questions

Which should you choose?

Which is better: GLM-5.1, Claude Opus 4.5 or Kimi K2.7 Code?

Kimi K2.7 Code is the better all-round choice, scoring 64/100 against GLM-5.1 (57) and Claude Opus 4.5 (55). It leads on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, GLM-5.1, Claude Opus 4.5 or Kimi K2.7 Code?

Kimi K2.7 Code is cheaper at $0.95 input / $4.00 output per million tokens (official Moonshot AI API price). GLM-5.1 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price); Claude Opus 4.5 costs $5.00 input / $25.00 output per million tokens (official Anthropic API price). At a typical mix of three input tokens to one output token, that is $1.71 per million tokens for Kimi K2.7 Code versus $2.15 for GLM-5.1 (1.3× as much) and $10.00 for Claude Opus 4.5 (5.8× as much).

Which scores higher on benchmarks?

Claude Opus 4.5 scores higher on the Capabilities Index (ECI): Claude Opus 4.5 150.1 (#48 of 148), Kimi K2.7 Code 150.0 (#49 of 148) and GLM-5.1 149.9 (#51 of 148). The confidence ranges of the top two overlap (147.9–153.0 vs 148.1–151.8), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5.1 89.9%, Kimi K2.7 Code 87.9%, Claude Opus 4.5 86.1%; FrontierMath Tiers 1–3 — Kimi K2.7 Code 54.0%, GLM-5.1 36.8%, Claude Opus 4.5 34.4%; OTIS Mock AIME 2024–2025 — Kimi K2.7 Code 95.6%, GLM-5.1 93.3%, Claude Opus 4.5 86.1%; SimpleQA Verified — Claude Opus 4.5 45.7%, Kimi K2.7 Code 36.5%, GLM-5.1 34.0%.

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, Claude Opus 4.5 leads, which tends to carry over to coding, but test on your own codebase. All three 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 200,000 for GLM-5.1 and 200,000 for Claude Opus 4.5. Maximum output per response: GLM-5.1 up to 131,072, Claude Opus 4.5 up to 64,000, Kimi K2.7 Code up to 262,144 tokens.

Which can read images, PDFs, audio or video?

GLM-5.1 accepts text; Claude Opus 4.5 accepts text, images and PDFs; Kimi K2.7 Code accepts text, images and video. Claude Opus 4.5 handles the widest range of inputs.

Are any of these open source?

GLM-5.1 and Kimi K2.7 Code publishes its weights and can be self-hosted; Claude Opus 4.5 is proprietary.

Which is newer?

Kimi K2.7 Code is the newest, released Jun 12, 2026. GLM-5.1 came out Apr 7, 2026; Claude Opus 4.5 came out Nov 24, 2025. Knowledge cutoff: Claude Opus 4.5 May 2025, 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.