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Laguna M.1 vs GLM 5.2

Detailed technical comparison between Laguna M.1 (Poolside) and GLM 5.2 (Zhipu AI). Review live API token pricing, context window capabilities, time-to-first-token latency, and verified benchmark scores side-by-side.

⚡ Executive Summary & Verdict

Comparison Snapshot

Laguna M.1: 0 WinsvsGLM 5.2: 6 Wins
Context Leader

GLM 5.2

1,048,576 tokens
Agentic Tool-Calling

Tie

Equal Capability
Lowest Latency (TTFT)

Tie

Equal Speed
Input Price Leader

Laguna M.1

$0.20 / MTok
Poolsideactive

Laguna M.1

Laguna M.1 is the flagship coding agent model from [Poolside](https://poolside.ai/), optimized for complex software engineering tasks. Designed for agentic coding workflows, it supports tool calling and reasoning, with a 256K...

View Laguna M.1 Full Specs →
Zhipu AIactive

GLM 5.2

GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...

View GLM 5.2 Full Specs →

Technical Specifications

🏆 = Superior Spec
SpecificationLaguna M.1GLM 5.2
ProviderPoolsideZhipu AI
Context Window262,144 tokens1,048,576 tokens🏆
Agent SuitabilityNot yet benchmarkedNot yet benchmarked
Time to First Token (TTFT)No public TTFT dataNo public TTFT data
Deployment Modelmanaged apimanaged api
Production StabilityStable GA (est.)Beta Access (est.)
API AvailableYesYes
Released Date2026-04-282026-06-16

API Pricing Comparison

Input Price per Million Tokens

Laguna M.1

$0.20

GLM 5.2

$0.80

Output Price per Million Tokens

Laguna M.1

$0.40

GLM 5.2

$2.50

💡 Cost Ratio: Laguna M.1 is 4.0x cheaper per input token than GLM 5.2.

Want to test both models live?

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Benchmark Performance Metrics

Standardized Scores (0–100%)

Scores show verified raw accuracy percentages across standardized AI evaluation suites. Higher bars indicate superior performance in that domain.

MMLUGeneral knowledge & multi-task understanding
81.2%vs89.5%+8.3% GLM 5.2
Laguna M.1
GLM 5.2 🏆
HumanEvalPython coding & logic synthesis
79.4%vs91.2%+11.8% GLM 5.2
Laguna M.1
GLM 5.2 🏆
MATHComplex mathematical problem solving
56.6%vs80.5%+23.9% GLM 5.2
Laguna M.1
GLM 5.2 🏆
GPQAGraduate-level expert reasoning
38.6%vs53.5%+14.9% GLM 5.2
Laguna M.1
GLM 5.2 🏆
HellaSwagCommonsense reasoning and inference
80.8%vs89.8%+9.0% GLM 5.2
Laguna M.1
GLM 5.2 🏆
MT-BenchMulti-turn conversation flow quality
8.3%vs9.3%+1.0% GLM 5.2
Laguna M.1
GLM 5.2 🏆

Laguna M.1 Quirks & Gotchas

No developer gotchas reported.

GLM 5.2 Quirks & Gotchas

No developer gotchas reported.

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