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Laguna S 2.1 vs GLM 4.7 Flash

Detailed technical comparison between Laguna S 2.1 (Poolside) and GLM 4.7 Flash (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 S 2.1: 6 WinsvsGLM 4.7 Flash: 0 Wins
Context Leader

Laguna S 2.1

1,048,576 tokens
Agentic Tool-Calling

Tie

Equal Capability
Lowest Latency (TTFT)

Tie

Equal Speed
Input Price Leader

GLM 4.7 Flash

$0.06 / MTok
Poolsideactive

Laguna S 2.1

Laguna S 2.1 is the latest coding agent model from [Poolside](<https://poolside.ai/>). Laguna S 2.1 is a 118B total parameter model with 8B active parameters, scoring 70.2% on Terminal-Bench 2.1 and...

View Laguna S 2.1 Full Specs →
Zhipu AIactive

GLM 4.7 Flash

As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning,...

View GLM 4.7 Flash Full Specs →

Technical Specifications

🏆 = Superior Spec
SpecificationLaguna S 2.1GLM 4.7 Flash
ProviderPoolsideZhipu AI
Context Window1,048,576 tokens🏆202,752 tokens
Agent SuitabilityNot yet benchmarkedNot yet benchmarked
Time to First Token (TTFT)No public TTFT dataNo public TTFT data
Deployment Modelmanaged apimanaged api
Production StabilityBeta Access (est.)Stable GA (est.)
API AvailableYesYes
Released Date2026-07-212026-01-19

API Pricing Comparison

Input Price per Million Tokens

Laguna S 2.1

$0.10

GLM 4.7 Flash

$0.06

Output Price per Million Tokens

Laguna S 2.1

$0.20

GLM 4.7 Flash

$0.40

💡 Cost Ratio: GLM 4.7 Flash is 1.7x cheaper per input token than Laguna S 2.1.

Want to test both models live?

Run side-by-side prompt benchmarks in our dynamic multi-model Sandbox. Compare execution speeds, latency metrics, and compute actual costs in real-time.

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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
81.0%vs77.2%+3.8% Laguna S 2.1
Laguna S 2.1 🏆
GLM 4.7 Flash
HumanEvalPython coding & logic synthesis
79.2%vs78.5%+0.7% Laguna S 2.1
Laguna S 2.1 🏆
GLM 4.7 Flash
MATHComplex mathematical problem solving
56.4%vs40.0%+16.4% Laguna S 2.1
Laguna S 2.1 🏆
GLM 4.7 Flash
GPQAGraduate-level expert reasoning
38.4%vs31.0%+7.4% Laguna S 2.1
Laguna S 2.1 🏆
GLM 4.7 Flash
HellaSwagCommonsense reasoning and inference
80.6%vs80.0%+0.6% Laguna S 2.1
Laguna S 2.1 🏆
GLM 4.7 Flash
MT-BenchMulti-turn conversation flow quality
8.7%vs8.1%+0.6% Laguna S 2.1
Laguna S 2.1 🏆
GLM 4.7 Flash

Laguna S 2.1 Quirks & Gotchas

No developer gotchas reported.

GLM 4.7 Flash Quirks & Gotchas

No developer gotchas reported.

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