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Gemini 3.6 Flash vs GLM 4.7 Flash

Detailed technical comparison between Gemini 3.6 Flash (Google) 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

Gemini 3.6 Flash: 1 WinvsGLM 4.7 Flash: 5 Wins
Context Leader

Gemini 3.6 Flash

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
Googleactive

Gemini 3.6 Flash

Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and...

View Gemini 3.6 Flash 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
SpecificationGemini 3.6 FlashGLM 4.7 Flash
ProviderGoogleZhipu AI
Context Window1,048,576 tokens🏆202,752 tokens
Agent SuitabilityN/AN/A
Time to First Token (TTFT)N/AN/A
Deployment Modelmanaged apimanaged api
Production Stabilitybetastable
API AvailableYesYes
Released Date2026-07-212026-01-19

API Pricing Comparison

Input Price per Million Tokens

Gemini 3.6 Flash

$1.50

GLM 4.7 Flash

$0.06

Output Price per Million Tokens

Gemini 3.6 Flash

$7.50

GLM 4.7 Flash

$0.40

💡 Cost Ratio: GLM 4.7 Flash is 24.8x cheaper per input token than Gemini 3.6 Flash.

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
73.6%vs77.2%+3.6% GLM 4.7 Flash
Gemini 3.6 Flash
GLM 4.7 Flash 🏆
HumanEvalPython coding & logic synthesis
69.8%vs78.5%+8.7% GLM 4.7 Flash
Gemini 3.6 Flash
GLM 4.7 Flash 🏆
MATHComplex mathematical problem solving
43.0%vs40.0%+3.0% Gemini 3.6 Flash
Gemini 3.6 Flash 🏆
GLM 4.7 Flash
GPQAGraduate-level expert reasoning
30.4%vs31.0%+0.6% GLM 4.7 Flash
Gemini 3.6 Flash
GLM 4.7 Flash 🏆
HellaSwagCommonsense reasoning and inference
77.6%vs80.0%+2.4% GLM 4.7 Flash
Gemini 3.6 Flash
GLM 4.7 Flash 🏆
MT-BenchMulti-turn conversation flow quality
8.0%vs8.1%+0.1% GLM 4.7 Flash
Gemini 3.6 Flash
GLM 4.7 Flash 🏆

Gemini 3.6 Flash Quirks & Gotchas

No developer gotchas reported.

GLM 4.7 Flash Quirks & Gotchas

No developer gotchas reported.

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