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Grok 4.20 Multi-Agent vs GLM 4.7 Flash

Detailed technical comparison between Grok 4.20 Multi-Agent (xAI) 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.

โšก Executive Summary & Verdict

Comparison Snapshot

Grok 4.20 Multi-Agent: 6 WinsvsGLM 4.7 Flash: 0 Wins
Context Leader

Grok 4.20 Multi-Agent

2,000,000 tokens
Agentic Tool-Calling

Tie

Equal Capability
Lowest Latency (TTFT)

Tie

Equal Speed
Input Price Leader

GLM 4.7 Flash

$0.06 / MTok
xAIactive

Grok 4.20 Multi-Agent

Grok 4.20 Multi-Agent is a variant of xAIโ€™s Grok 4.20 designed for collaborative, agent-based workflows. Multiple agents operate in parallel to conduct deep research, coordinate tool use, and synthesize information...

View Grok 4.20 Multi-Agent Full Specs โ†’
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 โ†’

Technical Specifications

๐Ÿ† = Superior Spec
SpecificationGrok 4.20 Multi-AgentGLM 4.7 Flash
ProviderxAIZhipu AI
Context Window2,000,000 tokens๐Ÿ†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 Date2026-03-312026-01-19

API Pricing Comparison

Input Price per Million Tokens

Grok 4.20 Multi-Agent

$1.25

GLM 4.7 Flash

$0.06

Output Price per Million Tokens

Grok 4.20 Multi-Agent

$2.50

GLM 4.7 Flash

$0.40

๐Ÿ’ก Cost Ratio: GLM 4.7 Flash is 20.8x cheaper per input token than Grok 4.20 Multi-Agent.

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.

Benchmark Performance Metrics

Standardized Scores (0โ€“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
80.8%vs77.2%+3.6% Grok 4.20 Multi-Agent
Grok 4.20 Multi-Agent ๐Ÿ†
GLM 4.7 Flash
HumanEvalPython coding & logic synthesis
79.0%vs78.5%+0.5% Grok 4.20 Multi-Agent
Grok 4.20 Multi-Agent ๐Ÿ†
GLM 4.7 Flash
MATHComplex mathematical problem solving
56.2%vs40.0%+16.2% Grok 4.20 Multi-Agent
Grok 4.20 Multi-Agent ๐Ÿ†
GLM 4.7 Flash
GPQAGraduate-level expert reasoning
41.6%vs31.0%+10.6% Grok 4.20 Multi-Agent
Grok 4.20 Multi-Agent ๐Ÿ†
GLM 4.7 Flash
HellaSwagCommonsense reasoning and inference
80.4%vs80.0%+0.4% Grok 4.20 Multi-Agent
Grok 4.20 Multi-Agent ๐Ÿ†
GLM 4.7 Flash
MT-BenchMulti-turn conversation flow quality
8.6%vs8.1%+0.5% Grok 4.20 Multi-Agent
Grok 4.20 Multi-Agent ๐Ÿ†
GLM 4.7 Flash

Grok 4.20 Multi-Agent Quirks & Gotchas

No developer gotchas reported.

GLM 4.7 Flash Quirks & Gotchas

No developer gotchas reported.

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