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Llama 3.3 70B Instruct vs GLM 5.2

Detailed technical comparison between Llama 3.3 70B Instruct (Meta) 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

Llama 3.3 70B Instruct: 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

Llama 3.3 70B Instruct

$0.13 / MTok
Metaactive

Llama 3.3 70B Instruct

Meta's state-of-the-art open weights model, providing enterprise-grade reasoning and logic. Exceptionally powerful for self-hosted customer support, text generation, and tooling workflows.

View Llama 3.3 70B Instruct 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
SpecificationLlama 3.3 70B InstructGLM 5.2
ProviderMetaZhipu AI
Context Window131,072 tokens1,048,576 tokens🏆
Agent Suitability83/100 (est.)Not yet benchmarked
Time to First Token (TTFT)280 ms (est.)No public TTFT data
Deployment Modelself hostablemanaged api
Production StabilityStable GA (est.)Beta Access (est.)
API AvailableYesYes
Released Date2024-12-062026-06-16

API Pricing Comparison

Input Price per Million Tokens

Llama 3.3 70B Instruct

$0.13

GLM 5.2

$0.80

Output Price per Million Tokens

Llama 3.3 70B Instruct

$0.40

GLM 5.2

$2.50

💡 Cost Ratio: Llama 3.3 70B Instruct is 6.1x 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
86.2%vs89.5%+3.3% GLM 5.2
Llama 3.3 70B Instruct
GLM 5.2 🏆
HumanEvalPython coding & logic synthesis
88.0%vs91.2%+3.2% GLM 5.2
Llama 3.3 70B Instruct
GLM 5.2 🏆
MATHComplex mathematical problem solving
75.0%vs80.5%+5.5% GLM 5.2
Llama 3.3 70B Instruct
GLM 5.2 🏆
GPQAGraduate-level expert reasoning
52.0%vs53.5%+1.5% GLM 5.2
Llama 3.3 70B Instruct
GLM 5.2 🏆
HellaSwagCommonsense reasoning and inference
88.5%vs89.8%+1.3% GLM 5.2
Llama 3.3 70B Instruct
GLM 5.2 🏆
MT-BenchMulti-turn conversation flow quality
8.8%vs9.3%+0.5% GLM 5.2
Llama 3.3 70B Instruct
GLM 5.2 🏆

Llama 3.3 70B Instruct Quirks & Gotchas

  • ▸Stable, well-documented self-hosted option with strong community support
  • ▸Outperformed by Llama 4 Maverick for agentic tool-calling workflows

GLM 5.2 Quirks & Gotchas

No developer gotchas reported.

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