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Llama 3.3 70B Instruct vs GLM 4.7 Flash

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

Llama 3.3 70B Instruct: 6 WinsvsGLM 4.7 Flash: 0 Wins
Context Leader

GLM 4.7 Flash

202,752 tokens
Agentic Tool-Calling

Tie

Equal Capability
Lowest Latency (TTFT)

Tie

Equal Speed
Input Price Leader

GLM 4.7 Flash

$0.06 / MTok
Metaactive

Llama 3.3 70B Instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...

View Llama 3.3 70B Instruct 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
SpecificationLlama 3.3 70B InstructGLM 4.7 Flash
ProviderMetaZhipu AI
Context Window131,072 tokens202,752 tokens🏆
Agent SuitabilityNot yet benchmarkedNot yet benchmarked
Time to First Token (TTFT)No public TTFT dataNo public TTFT data
Deployment Modelself hostablemanaged api
Production StabilityStable GA (est.)Stable GA (est.)
API AvailableYesYes
Released Date2024-12-062026-01-19

API Pricing Comparison

Input Price per Million Tokens

Llama 3.3 70B Instruct

$0.13

GLM 4.7 Flash

$0.06

Output Price per Million Tokens

Llama 3.3 70B Instruct

$0.40

GLM 4.7 Flash

$0.40

💡 Cost Ratio: GLM 4.7 Flash is 2.2x cheaper per input token than Llama 3.3 70B Instruct.

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
87.0%vs77.2%+9.8% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
GLM 4.7 Flash
HumanEvalPython coding & logic synthesis
86.2%vs78.5%+7.7% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
GLM 4.7 Flash
MATHComplex mathematical problem solving
67.4%vs40.0%+27.4% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
GLM 4.7 Flash
GPQAGraduate-level expert reasoning
48.8%vs31.0%+17.8% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
GLM 4.7 Flash
HellaSwagCommonsense reasoning and inference
88.0%vs80.0%+8.0% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
GLM 4.7 Flash
MT-BenchMulti-turn conversation flow quality
9.1%vs8.1%+1.0% Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 🏆
GLM 4.7 Flash

Llama 3.3 70B Instruct Quirks & Gotchas

No developer gotchas reported.

GLM 4.7 Flash Quirks & Gotchas

No developer gotchas reported.

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