Models

GLM 5.2 vs Kimi K2 Turbo Preview

Compare GLM 5.2 from Z.AI and Kimi K2 Turbo Preview from Moonshot AI on key metrics including benchmarks, price, context length, and other model features. Access both models and hundreds of others through the AIHubMix API.

Z.AIGLM 5.2Moonshot AIKimi K2 Turbo Preview
Z.AI logo
GLM 5.2
Z.AI · text → text

GLM-5.2 is Z.ai’s flagship model for the era of long-horizon tasks. With a truly usable 1M-token context window, it can handle project-level engineering context, execute long-running tasks more reliably, follow engineering standards more consistently, and complete the full development workflow from requirements to multi-platform deployment in a single task.

Input$1.13 /M
Output$3.94 /M
Moonshot AI logo
Kimi K2 Turbo Preview
Moonshot AI · text → text

The kimi-k2-turbo-preview model is a high-speed version of kimi-k2, with the same model parameters as kimi-k2, but the output speed has been increased from 10 tokens per second to 40 tokens per second.

Input$1.20 /M
Output$4.80 /M

Pricing & Specifications

Prices are per million tokens. Time to First Token and throughput are rolling averages measured on AIHubMix.

GLM 5.2
Kimi K2 Turbo Preview
Input /M
$1.13
$1.20
Output /M
$3.94
$4.80
Cache read /M
$0.28
$0.30
Context length
1,000,000
262,144
Max output
128,000
262,144
Time to First Token
1.1 s
3.1 s
Throughput
36.8 tok/s
19.7 tok/s
Modalities
text
text
Supported Parameters
thinkingtoolsfunction callingstructured outputs
toolsfunction callingstructured outputs
API Formats
chat_completions · claude_api
Released
June 16, 2026
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Promotional prices show the discounted rate; see each model page for promotion windows.

Activity Past 30 Days

Daily traffic served through AIHubMix — how demand for each model is trending.

glm-5.2kimi-k2-turbo-preview

Tokens / day

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Requests / day

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Performance Past 3 Days

Measured on real AIHubMix traffic, hourly buckets. Gaps mean no traffic in that hour.

glm-5.2kimi-k2-turbo-preview

Throughput (tok/s)

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TTFT (s)

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Uptime (%)

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LMArena Benchmarks

LMArena ratings by capability (Bradley-Terry, commonly called Elo). Higher is better.

Text
glm-5.2kimi-k2-turbo-preview
1360142014801540
Overall
14181470
Coding
14671505
Math
14161475
Hard prompts
14371488
Instruction following
13901462
Multi-turn
14041470
Creative writing
13821450
Longer query
14031479
Chinese
14541516
English
14231477

Source: LMArena (arena.ai) leaderboard, imported by AIHubMix. Models without published ratings are omitted per chart.

Cost calculator

Estimate your monthly bill for the same workload on each model.

GLM 5.2
$127 /mo
Kimi K2 Turbo Preview
$144 /mo

Monthly = daily × 30. Discounted rates applied where a promotion is active.

FAQ

Which is cheaper: GLM 5.2, Kimi K2 Turbo Preview?

GLM 5.2: $3.94/M output tokens; Kimi K2 Turbo Preview: $4.80/M. Use the cost calculator above to estimate your own workload.

How do their coding arena scores compare?

GLM 5.2: 1505; Kimi K2 Turbo Preview: 1467 (LMArena coding leaderboard).

Which responds faster?

GLM 5.2: 1.1s time to first token measured on AIHubMix; see the live performance charts above for how each model behaves across the day.

How large is each context window?

GLM 5.2 accepts 1,000,000 and Kimi K2 Turbo Preview accepts 262,144 input tokens. Maximum output per request is 128,000 tokens on GLM 5.2 and 262,144 tokens on Kimi K2 Turbo Preview.

Which one generates tokens faster?

GLM 5.2 at 36.8 tok/s and Kimi K2 Turbo Preview at 19.7 tok/s, measured as output throughput on AIHubMix — a separate metric from time to first token.

What inputs and capabilities does each model support?

GLM 5.2 accepts text input and supports thinking, tool calling, function calling and structured outputs; Kimi K2 Turbo Preview accepts text input and supports tool calling, function calling and structured outputs.

Can I call GLM 5.2 and Kimi K2 Turbo Preview with the same API key?

Yes. AIHubMix serves every model on this page behind one OpenAI-compatible endpoint, so switching between them is a one-line change to the model field — no second account, key or SDK.

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