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GLM 4.7 vs Llama 3.3 70B Instruct

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for GLM 4.7 and Llama 3.3 70B Instruct.

Zhipu AI

GLM 4.7

GLM-4.7 is Z.aiโ€™s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing complex agent tasks while...

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Meta

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.

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Technical Specifications

SpecificationGLM 4.7Llama 3.3 70B Instruct
ProviderZhipu AIMeta
Context Window202,752 tokens131,072 tokens
Agent SuitabilityN/A83/100
Time to First Token (TTFT)N/A280 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2025-12-222024-12-06

API Pricing Comparison

Input Price per Million Tokens

GLM 4.7

$0.40

Llama 3.3 70B Instruct

$0.10

Output Price per Million Tokens

GLM 4.7

$1.75

Llama 3.3 70B Instruct

$0.32

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
8150.0%vs8620.0%
GLM 4.7
Llama 3.3 70B Instruct
HumanEvalPython coding & logic synthesis
8200.0%vs8800.0%
GLM 4.7
Llama 3.3 70B Instruct
MATHComplex mathematical problem solving
5150.0%vs7500.0%
GLM 4.7
Llama 3.3 70B Instruct
GPQAGraduate-level expert reasoning
3800.0%vs5200.0%
GLM 4.7
Llama 3.3 70B Instruct
HellaSwagCommonsense reasoning and inference
8350.0%vs8850.0%
GLM 4.7
Llama 3.3 70B Instruct
MT-BenchMulti-turn conversation flow quality
865.0%vs880.0%
GLM 4.7
Llama 3.3 70B Instruct

GLM 4.7 Quirks & Gotchas

No developer gotchas reported.

Llama 3.3 70B Instruct Quirks & Gotchas

  • โ–ธStable, well-documented self-hosted option with strong community support
  • โ–ธOutperformed by Llama 4 Maverick for agentic tool-calling workflows