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GPT-3.5 Turbo 16k vs GLM 4.7 Flash

Detailed technical comparison between GPT-3.5 Turbo 16k (OpenAI) 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

GPT-3.5 Turbo 16k: 5 WinsvsGLM 4.7 Flash: 1 Win
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
OpenAIactive

GPT-3.5 Turbo 16k

This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up...

View GPT-3.5 Turbo 16k 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
SpecificationGPT-3.5 Turbo 16kGLM 4.7 Flash
ProviderOpenAIZhipu AI
Context Window16,385 tokens202,752 tokens🏆
Agent SuitabilityNot yet benchmarkedNot yet benchmarked
Time to First Token (TTFT)No public TTFT dataNo public TTFT data
Deployment Modelmanaged apimanaged api
Production StabilityStable GA (est.)Stable GA (est.)
API AvailableYesYes
Released Date2023-08-282026-01-19

API Pricing Comparison

Input Price per Million Tokens

GPT-3.5 Turbo 16k

$3.00

GLM 4.7 Flash

$0.06

Output Price per Million Tokens

GPT-3.5 Turbo 16k

$4.00

GLM 4.7 Flash

$0.40

💡 Cost Ratio: GLM 4.7 Flash is 50.0x cheaper per input token than GPT-3.5 Turbo 16k.

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
79.4%vs77.2%+2.2% GPT-3.5 Turbo 16k
GPT-3.5 Turbo 16k 🏆
GLM 4.7 Flash
HumanEvalPython coding & logic synthesis
77.6%vs78.5%+0.9% GLM 4.7 Flash
GPT-3.5 Turbo 16k
GLM 4.7 Flash 🏆
MATHComplex mathematical problem solving
54.8%vs40.0%+14.8% GPT-3.5 Turbo 16k
GPT-3.5 Turbo 16k 🏆
GLM 4.7 Flash
GPQAGraduate-level expert reasoning
40.2%vs31.0%+9.2% GPT-3.5 Turbo 16k
GPT-3.5 Turbo 16k 🏆
GLM 4.7 Flash
HellaSwagCommonsense reasoning and inference
82.4%vs80.0%+2.4% GPT-3.5 Turbo 16k
GPT-3.5 Turbo 16k 🏆
GLM 4.7 Flash
MT-BenchMulti-turn conversation flow quality
8.5%vs8.1%+0.4% GPT-3.5 Turbo 16k
GPT-3.5 Turbo 16k 🏆
GLM 4.7 Flash

GPT-3.5 Turbo 16k Quirks & Gotchas

No developer gotchas reported.

GLM 4.7 Flash Quirks & Gotchas

No developer gotchas reported.

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