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AI Choice Engine

Model comparison

GLM 5.3 Flash vs Kimi K2.7 Code

Z.ai against Moonshot AI, compared on context, price, and verified benchmark results.

Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

Z.ai

GLM 5.3 Flash

Balanced · Open weights

vs

Moonshot AI

Kimi K2.7 Code

Balanced · Open weights

AI model capability comparison
SpecificationGLM 5.3 FlashKimi K2.7 Code
ProviderZ.aiMoonshot AI
TierBalancedBalanced
Context windowWinner: 1.05M262K
Max output131KNot verifiedUnverified
Input / 1M tokensWinner: $0.15$0.95
Output / 1M tokensWinner: $0.50$4
WeightsOpenOpen
Parameters320B total / 18B active (MoE)1T total / 32B active (MoE)
Reasoning levelsNot verifiedUnverifiedNot verifiedUnverified
Modalitiestext, image, videotext, image, video
LicenseMITModified MIT
API model idglm-5.3-flashkimi-k2.7-code
ReleasedAugust 26, 2026June 12, 2026
Artificial Analysis Intelligence Index (2026-09-26)Winner: 41.825.8
SWE-bench Verified (2026-09-01)92Not verifiedUnverified
Terminal-Bench 2.1 (2026-08)84.3Not verifiedUnverified
DeepSWE 1.1 (2026-08)63.4Not verifiedUnverified
Humanity's Last Exam (2026-08)55.3Not verifiedUnverified

Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

  • 2026-09-26
    Artificial Analysis

    Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.

    Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check measuredAt.

  • 2026-09-01
    vals.ai SWE-bench Verified leaderboard (independent, mini-swe-agent bash-only); not published by Z.ai

    Real GitHub issue resolution: does the model's patch pass the hidden tests.

    Comparable with caveatPost-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.

  • 2026-08
    Z.ai GLM-5.3-Flash blog (vendor, mini-swe-agent, 400K context)

    Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.

    Directly comparable

  • 2026-08
    GLM-5.3-Flash HF model card (vendor, with tools, full set)

    Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.

    Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

Pricing tiers

GLM 5.3 Flash: $0.15/$0.50 per MTok on Z.ai's first-party API (cached input $0.03); third-party hosts (GMI, Novita, Together) list the same $0.15/$0.50. No promo tier is listed. The faster GLM-5.3-FlashX costs $0.37/$1.25.

Kimi K2.7 Code: $0.95/$4.00 per MTok — same rates as K2.6; cached input $0.19. Coding-focused build of the K2.6 recipe (1T total / 32B active).

BalancedOpen weightsRecord checked September 26, 2026

GLM 5.3 Flash

GLM 5.3 Flash is Z.ai's natively multimodal open-weight workhorse — 1M context, hybrid sparse/linear attention, near-flagship Intelligence Index at a fraction of the cost.

Best for

  • Cost-efficient long-context
  • Multimodal input
  • Coding agents

Watch out

Self-hosting needs ~186GB GPU memory at 4-bit (multi-GPU). Third-party hosts charge more than Z.ai's own API.

BalancedOpen weightsRecord checked September 26, 2026

Kimi K2.7 Code

Kimi K2.7 Code is Moonshot's coding-specialised build of K2.6 — same 1T MoE and 256K context, tuned for agentic coding workflows.

Best for

  • Agentic coding on open weights
  • Kimi ecosystem teams
  • Repo-scale refactors at K2.6 rates

Watch out

256K context matches K2.6, not K3's 1M; appears on the DeepSWE and CursorBench boards at mid-table scores.

When the cheaper one wins

GLM 5.3 Flash is cheaper on output at $0.50 per million tokens against $4 for Kimi K2.7 Code — about 8.0×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. On DeepSWE 1.1, GLM 5.3 Flash is 63.4% Pass@1 at $0.48/task versus Kimi K2.7 Code at 30.5% / $2.82/task. These are standard-tier API rates, excluding batch and cache discounts.

Run the model picker

Evidence confidence: High

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

  • Pricing verified on both sides — Input and output rates are verified for both models.
  • 3/5 core specs verified on both sides — Not published for at least one side: max output, reasoning levels.
  • 1 shared named benchmark with differing scores — Measured on: Artificial Analysis Intelligence Index.
  • Verified within the last 90 days — Newest catalog check was 2 days ago.
  • Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

Related comparisons

Diving deeper on one model? GLM 5.3 Flash · Kimi K2.7 Code

Common questions

GLM 5.3 Flash vs Kimi K2.7 Code

Answered from the verified figures on this page rather than general guidance.

Is GLM 5.3 Flash or Kimi K2.7 Code cheaper for input?
GLM 5.3 Flash is cheaper at $0.15 per million input tokens, against $0.95 for Kimi K2.7 Code — roughly 6.3× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GLM 5.3 Flash has tiered pricing: $0.15/$0.50 per MTok on Z.ai's first-party API (cached input $0.03); third-party hosts (GMI, Novita, Together) list the same $0.15/$0.50. No promo tier is listed. The faster GLM-5.3-FlashX costs $0.37/$1.25. Kimi K2.7 Code has tiered pricing: $0.95/$4.00 per MTok — same rates as K2.6; cached input $0.19. Coding-focused build of the K2.6 recipe (1T total / 32B active).
Is GLM 5.3 Flash or Kimi K2.7 Code cheaper for output?
GLM 5.3 Flash is cheaper at $0.50 per million output tokens, against $4 for Kimi K2.7 Code — roughly 8.0× the price. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; GLM 5.3 Flash has tiered pricing: $0.15/$0.50 per MTok on Z.ai's first-party API (cached input $0.03); third-party hosts (GMI, Novita, Together) list the same $0.15/$0.50. No promo tier is listed. The faster GLM-5.3-FlashX costs $0.37/$1.25. Kimi K2.7 Code has tiered pricing: $0.95/$4.00 per MTok — same rates as K2.6; cached input $0.19. Coding-focused build of the K2.6 recipe (1T total / 32B active).
Which has the larger context window, GLM 5.3 Flash or Kimi K2.7 Code?
GLM 5.3 Flash accepts 1.05M tokens against 262K for Kimi K2.7 Code. This only matters if you routinely send very long documents or large codebases.
Should I use GLM 5.3 Flash or Kimi K2.7 Code?
Both sit in the balanced tier, so the choice usually comes down to price and context rather than capability. GLM 5.3 Flash suits cost-efficient long-context; Kimi K2.7 Code suits agentic coding on open weights.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Input price: GLM 5.3 Flash costs $0.15 per 1M tokens versus $0.95 for Kimi K2.7 Code — a 6.3x difference at the headline tier.
  • Context: GLM 5.3 Flash takes 1.05M against 262K for Kimi K2.7 Code — only decisive if your prompts approach the smaller window.
  • Measured capability: GLM 5.3 Flash leads Artificial Analysis Intelligence Index 41.8 to 25.8 (measured 2026-09-26).
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