Model comparison · accepted catalogue facts 2026-10-21

Kimi K3 vs GLM 5.2

Neither model has both an equal-or-higher measured score and an equal-or-lower estimated price in this pair. Retaining your current model is a valid outcome.

Editorial comparison: Direct benchmark rivals: Kimi K3 (Moonshot AI) vs GLM 5.2 (Z.ai) with overlapping confidence intervals Inclusion does not recommend either model.

Kimi K3

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1475.6

Balanced effective USD / 1M tokens · lower is better

$3.05/M

Price under confirmation since Oct 13, 2026

GLM 5.2

LMArena general · Elo · higher is better

Scale starts at 1040 Elo

1470.0

Balanced effective USD / 1M tokens · lower is better

$1.12/M

Price under confirmation since Oct 12, 2026

In-Depth Analysis

Kimi K3 vs GLM 5.2: Measured Technical Breakdown

Comprehensive comparison across measured human-preference evaluation, effective production token costs, architectural limits, and migration trade-offs.

Measured Capability (LMArena)

Kimi K3 leads by 5.6 Elo

Kimi K3: 1475.6 ±4.6 · GLM 5.2: 1470 ±4.17 Intervals overlap — not a guaranteed quality advantage

Cost Comparison Across Canonical Production Workloads

Rates represent effective cost per 1M tokens under standard token blends. Cached prompt reads are factored according to workload context reuse.

Workload & Ratio	Kimi K3	GLM 5.2	Cost Difference	Advantage
Balanced 3 tokens in per 1 out	$3.05 /1M tokens	$1.12 /1M tokens	$1.93 (63% diff)	GLM 5.2 costs less
Summarise Huge input, tiny output	$1.14 /1M tokens	$0.422 /1M tokens	$0.720 (63% diff)	GLM 5.2 costs less
Chat Long cached system prompt	$4.38 /1M tokens	$1.56 /1M tokens	$2.83 (65% diff)	GLM 5.2 costs less
Code gen Output dominates	$6.28 /1M tokens	$2.24 /1M tokens	$4.04 (64% diff)	GLM 5.2 costs less
Agentic Context replayed each turn	$2.01 /1M tokens	$0.693 /1M tokens	$1.32 (66% diff)	GLM 5.2 costs less

Architectural & Operational Specifications

Specification	Kimi K3	GLM 5.2	Operational Note
Context window	1,048,576 tokens	1,048,576 tokens	—
Max output generation	943,718 tokens	943,718 tokens	—
Base input price	$0.730 / 1M tokens	$0.309 / 1M tokens	GLM 5.2 input rate is cheaper
Base output price	$10.00 / 1M tokens	$3.55 / 1M tokens	GLM 5.2 output rate is cheaper
Cached input price	$0.550 / 1M tokens	$0.137 / 1M tokens	GLM 5.2 offers lower cached prompt cost
Reasoning & thinking tokens	Supported; standard rates	Supported; standard rates	—
Input modalities	text, image, video	text	—
Tool calling / function use	Supported	Supported	—
Open weights & licence	Open weights (kimi-k3)	Open weights (mit)	—
Provider & reseller market	Moonshot AI · 25 provider offers (4.73× spread)	Z.ai · 33 provider offers (9.21× spread)	—

When to Choose Kimi K3

Higher measured general capability (1475.6 Elo on human evaluation).
Broader multimodal input support: text, image, video.
Direct integration with Moonshot AI tooling and API features.

When to Choose GLM 5.2

Direct integration with Z.ai tooling and API features.

Risks when moving from Kimi K3 → GLM 5.2:

image input is not supported by the candidate.
video input is not supported by the candidate.

Detailed Evidence Passports & Independent Audits

Kimi K3 Full model card, pricing ladder & provider offers → GLM 5.2 Full model card, pricing ladder & provider offers → Alternatives to Kimi K3 Better-and-cheaper dominance audit → Alternatives to GLM 5.2 Better-and-cheaper dominance audit →

Compare · model decision

Choose two or three models

Inspect the differences, estimate your workload and keep the evidence. All standard catalogue models are available, including unrated ones.

Catalogue facts
2026-10-21

A · Current or starting modelB · Comparison modelC · Optional third model

2 / 3 selected · 0 requirements

Open public comparison link

Public workload and requirements

These settings are included in the share link. The fixed-request scenario and private evaluation data below are excluded.

Workload Public prompt length (tokens) Required context (tokens) Required output limit (tokens) Required input modality

Tool useReasoningOpen weights

A ↔ B

Neither model has both an equal-or-higher measured score and an equal-or-lower estimated price in this pair. Retaining your current model is a valid outcome.

Estimated blend: 75% input, 25% output; 0% cached input. Base context tier. Prompt length selects the billing tier. An unlisted cache-read rate uses that tier’s full input rate in this blend; missing required input or output rates prevent an estimate. The fixed-request estimate below validates actual context and output limits and requires a published cache-read rate when cache hits are assumed.

What changes between these models

Quality first, then estimated price. LMArena scores are human-preference measurements, not a benchmark of your application. A missing score is unrated.

Kimi K3 · Meets the stated model requirements
GLM 5.2 · Meets the stated model requirements

Material differences and unknown evidence. Deltas compare each model with model A. Unknown facts remain visible.
Measured fact	A Kimi K3	B GLM 5.2
LMArena general score (Elo)	1,475.6	1,470Δ vs A: -5.6 Elo
Score interval half-width (± Elo)	4.6	4.17Δ vs A: -0.43 Elo
LMArena document score (Elo)	Unknown	Unknown
Estimated effective USD / 1M tokens	$3.0475	$1.12Δ vs A: -1.9275 USD/M
Base input USD / 1M tokens	$0.73	$0.309Δ vs A: -0.421 USD/M
Base output USD / 1M tokens	$10	$3.553Δ vs A: -6.447 USD/M
Base cached-input USD / 1M tokens	$0.55	$0.137Δ vs A: -0.413 USD/M
Price status	Under confirmation since 2026-10-13	Under confirmation since 2026-10-12
Input modalities	text, image, video	text
Recorded licence	kimi-k3	mit
Model provider	Moonshot AI	Z.ai
Distinct sellers in the catalogue	20	26Δ vs A: +6
Recorded retirement date	Unknown	Unknown

8 shared measured facts

Shared measured facts. Deltas compare each model with model A. Unknown facts remain visible.
Measured fact	A Kimi K3	B GLM 5.2
Benchmark reasoning effort	max	max
Context window (tokens)	1,048,576	1,048,576Δ vs A: 0 tokens
Output limit (tokens)	943,718	943,718Δ vs A: 0 tokens
Tool use	Supported	SupportedΔ vs A: 0
Reasoning	Supported	SupportedΔ vs A: 0
Open weights	Supported	SupportedΔ vs A: 0
Serving precision differs across offers	Supported	SupportedΔ vs A: 0
Retirement evidence	None announced; not a guarantee	None announced; not a guarantee

Provider counts and precision flags describe catalogue offers. They do not establish the precision, availability, region, tool behaviour or limits of your chosen endpoint. Open each model’s provider table before switching.

Complete price ladders and billing conditions

Kimi K3

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token threshold	Input	Output	Cached input
Base, up to and including first threshold	$0.73	$10	$0.55

GLM 5.2

Rates below are USD per 1M tokens. A threshold selects that whole input/output rate pair.

Input-token threshold	Input	Output	Cached input
Base, up to and including first threshold	$0.309	$3.553	$0.137

Migration preflight · compare each candidate with model A

Keeping Kimi K3 is a valid decision. No pairwise comparison certifies a drop-in replacement.

Kimi K3 → GLM 5.2

Known loss or failed requirement

image input is not supported by the candidate.
video input is not supported by the candidate.
Provider endpoint, serving precision, availability and task success need your own verification. Catalogue model capabilities do not certify an endpoint.

Check alternatives to Kimi K3

Estimate a fixed request shape

Private to this tab unless you save locally or explicitly export private details. These are estimates, not observed invoices. Cross-model token counts and task success are assumptions.

Requests Input tokens per request Output tokens per request Cached share of input (%) Extra attempts per request

0.1 extra attempts means 10 extra full attempts per 100 requests. Cache writes, tool charges, taxes and unlisted charges are excluded.

Kimi K3Estimated total: $6.46 1,000 full attempts · base rates
GLM 5.2Estimated total: $2.3945 1,000 full attempts · base rates

Prompt and output sensitivity · sampled scenarios

Every point recalculates the entire request shape. Tier-boundary samples include one token below, at and above each threshold. Ordering between samples is not guaranteed. Lowest known cost excludes unknown estimates and is not a replacement recommendation.

Vary input; hold output, requests, cache and retries fixed.
Input tokens	Kimi K3	GLM 5.2	Coverage
2,000	$6.46	$2.3945Lowest known	All computed
4,096	$7.99008	$3.042164Lowest known	All computed
32,768	$28.92064	$11.901812Lowest known	All computed
131,072	$100.68256	$42.277748Lowest known	All computed

Vary output; hold input, requests, cache and retries fixed.
Output tokens	Kimi K3	GLM 5.2
128	$2.74	$1.072784
2,048	$21.94	$7.894544
8,192	$83.38	$29.724176

Bring your observed evaluation results

JSON is parsed locally. Prompts, filenames, individual responses and extra fields are not retained. Import only aggregate measurements for selected models. Task/version labels do not establish that two evaluations used the same dataset.

Import private evaluation JSON

Expected format

[{"model":"provider/exact-model-id","task":"support-answer","version":"rubric-1","attempts":100,"successes":84,"costUsd":2.4,"latencyMs":950}]

Use the exact catalogue ID. Replace the example values with observed measurements. Omit costUsd or latencyMs when they are unknown. No public score is inferred from these observations.

Keep the decision and its evidence

A portable record retains selected facts, complete tier ladders, public requirements and the publication manifest. It also embeds the full permitted public catalogue to verify the input artifact checksum. Files are larger for this reason (up to 2 MB); local storage is limited to 12 records. Team review is local to this browser; there is no hosted collaboration.

Owner (private) Review date (private) Recorded choice (private) Rationale (private)

Include private review fields, scenario and observations in downloaded files

Store private decision records on this device

Saving is opt-in and includes private review fields. Shared comparison links never include them. Version 1 Check references remain separate and are not overwritten or guessed into this format.

Import a v2 decision JSON file

Saved local decisions · 0

No version 2 decision records are stored in this browser.

Questions this page answers

Is Kimi K3 a better-and-cheaper replacement for GLM 5.2?

Neither model has both an equal-or-higher measured score and an equal-or-lower estimated price in this pair. Retaining your current model is a valid outcome.

Which model is higher rated: Kimi K3 or GLM 5.2?

Kimi K3 scores higher on LMArena general (1475.6 ±4.6) compared to GLM 5.2 (1470.0 ±4.2), a margin of 5.6 Elo points. However, because the 95% confidence intervals overlap, this does not establish an unconditional superiority across all tasks.

Which model is more cost-effective: Kimi K3 or GLM 5.2?

On a standard balanced workload (3 prompt tokens per 1 generated token), GLM 5.2 costs $1.12 (price under confirmation since 2026-10-12) per 1M tokens versus $3.05 (price under confirmation since 2026-10-13) for Kimi K3. Kimi K3 is approximately 2.7× the price of GLM 5.2. Base input rates are $0.730 for Kimi K3 and $0.309 for GLM 5.2; base output rates are $10.00 for Kimi K3 and $3.55 for GLM 5.2.

How do their context windows and generation limits compare?

Both Kimi K3 and GLM 5.2 provide an identical 1,048,576-token context window. Maximum output generation limits are 943,718 tokens for Kimi K3 and 943,718 tokens for GLM 5.2.

What capabilities are lost when switching between Kimi K3 and GLM 5.2?

Switching from Kimi K3 to GLM 5.2 involves: image input is not supported by the candidate; video input is not supported by the candidate. Run test suites on your specific prompt templates and tool declarations before cutting over production traffic.

Can Kimi K3 or GLM 5.2 be self-hosted or deployed privately?

Both Kimi K3 and GLM 5.2 provide open weights, enabling self-hosting on private GPU infrastructure, local quantization, and private data pipelines. Managed inference is also offered across third-party API providers (25 for Kimi K3, 33 for GLM 5.2).

What is included in this comparison?

Accepted model capabilities, context and output limits, complete price-tier ladders, independent LMArena measurements and missing evidence. Provider endpoint precision and application success still need verification.

Can I compare a different workload or keep a decision record?

Yes. Change the public workload, prompt length and requirements in the comparison workspace. Fixed-request scenarios, aggregate evaluations and review notes stay local unless explicitly exported.

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Kimi K3 vs GLM 5.2

Kimi K3 vs GLM 5.2: Measured Technical Breakdown

Cost Comparison Across Canonical Production Workloads

Architectural & Operational Specifications

When to Choose Kimi K3

When to Choose GLM 5.2

Detailed Evidence Passports & Independent Audits

Choose two or three models

What changes between these models

Kimi K3

GLM 5.2

Kimi K3 → GLM 5.2

Estimate a fixed request shape

Bring your observed evaluation results

Keep the decision and its evidence

Saved local decisions · 0

Questions this page answers