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DeepSeek R1 vs GLM 5.2

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 DeepSeek R1 and GLM 5.2.

DeepSeek

DeepSeek R1

A premier reasoning model employing large-scale reinforcement learning. Displays specialized math, coding, and logical validation capabilities comparable to OpenAI's o1.

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Zhipu AI

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,...

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Technical Specifications

SpecificationDeepSeek R1GLM 5.2
ProviderDeepSeekZhipu AI
Context Window163,840 tokens1,048,576 tokens
Agent Suitability78/100N/A
Time to First Token (TTFT)1800 msN/A
Deployment Modelmanaged apimanaged api
Production Stabilitystablebeta
API AvailableYesYes
Released Date2025-01-202026-06-16

API Pricing Comparison

Input Price per Million Tokens

DeepSeek R1

$0.70

GLM 5.2

$0.93

Output Price per Million Tokens

DeepSeek R1

$2.50

GLM 5.2

$3.00

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
9080.0%vs8950.0%
DeepSeek R1
GLM 5.2
HumanEvalPython coding & logic synthesis
9280.0%vs9120.0%
DeepSeek R1
GLM 5.2
MATHComplex mathematical problem solving
9310.0%vs8050.0%
DeepSeek R1
GLM 5.2
GPQAGraduate-level expert reasoning
6210.0%vs5350.0%
DeepSeek R1
GLM 5.2
HellaSwagCommonsense reasoning and inference
9050.0%vs8980.0%
DeepSeek R1
GLM 5.2
MT-BenchMulti-turn conversation flow quality
935.0%vs930.0%
DeepSeek R1
GLM 5.2

DeepSeek R1 Quirks & Gotchas

  • โ–ธReasoning model โ€” not designed for high-frequency tool calling
  • โ–ธPair with a smaller model (V4 Flash) for routing and use R1 for complex reasoning only

GLM 5.2 Quirks & Gotchas

No developer gotchas reported.