GLM-5.1 vs GLM-5.2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GLM-5.1 by Zhipu AI and GLM-5.2 by Zhipu AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, GLM-5.2 provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, GLM-5.1 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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GLM-5.1
Agente de codificaciónAdvanced de pesos abiertos codificación champion tailored para terminal ejecución, git workspace manipulation, y autónomo ingeniería de software.
$0.00 / 1M
$0.00 / 1M
200K tokens
63%
GLM-5.2
Contexto largo económicomasivo millones-token de código abierto insignia ofreciendo sustained multiturno razonamiento, vast repositorio síntesis, y de frontera autonomy.
$0.00 / 1M
$0.00 / 1M
1M tokens
67.5%
Diferencias Clave de un Vistazo
- Both models cost the same under this workload — choose on capability, not price.
- GLM-5.2 has a larger context window (1M vs 200K tokens).
- GLM-5.1 is faster at 65 tok/s vs 60 tok/s.
- GLM-5.2 leads on SWE-bench coding (67.5% vs 63.0%).
| Característica | GLM-5.1 | GLM-5.2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 89 | 91 |
Provider | Zhipu AI | Zhipu AI |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 32K |
Throughput Speed Tokens generated per second | 65 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | 63% | 67.5% |
GPQA Diamond Graduate-level reasoning | 70% | 73.5% |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-5.1 · Strengths & Key Advantages
- SWE-bench Verified score de 63.0% setting un de código abierto estándar para codificación modelos
- superior terminal ejecución lógica y autónomo error backtracking
- Comprehensive de pesos abiertos allowing fully air-gapped corporativo software automatización
Considerations & Trade-offs
- requiere sustancial datacenter infraestructura (4x un 8x H100) para unquantized servicio
- carece de nativo visual entrada soporte
GLM-5.2 · Strengths & Key Advantages
- completo 1M token ventana de contexto paired con un staggering 1440 Arena Elo score
- 67.5% SWE-bench score rivaling el la mayoría de capaz comercial propietario modelos
- Complete de pesos abiertos freedom con extensive empresarial customization options
Considerations & Trade-offs
- La colosal huella de memoria requiere clústeres aceleradores multinodo
- texto-solo modelo; visual tareas require integración con GLM visión canalizaciones
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-5.1 | GLM-5.2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 26K | 30K |
Reasoning Tokens / Task Thinking tokens burned per task | 9K | 12K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | GLM-5.1 | GLM-5.2 |
|---|---|---|
| Official API Model ID (SDK) | glm-5-1 | glm-5-2 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-06 |
| Knowledge Cutoff | February 2026 | February 2026 |
| Architecture | Expanded Code-Centric MoE Frontier Transformer | Massive Million-Token Open MoE Frontier Transformer |
| Parameters | 280B MoE | 350B MoE |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 174 GB VRAM (4-bit Quantized) · 602 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 217 GB VRAM (4-bit Quantized) · 753 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
GLM-5.1 vs GLM-5.2: Respuestas Rápidas (FAQ)
Which is more intelligent, GLM-5.1 or GLM-5.2?
GLM-5.2 is more intelligent. It scores 91 compared with 89 on the composite Intelligence Index.
Which model is faster?
GLM-5.1 is faster, generating 65 tokens per second compared with 60 tokens per second.
Which model has lower latency?
GLM-5.1 has lower latency, with a time to first token of 0.61s compared with 0.65s.
Which model has a larger context window?
GLM-5.2 has a larger context window, supporting 1M tokens compared with 200K tokens.
Veredicto
Verdict & Model Selection Recommendation: Choose GLM-5.1 if your priority is Sovereign coding agents, automated pull request validation, and enterprise software modernisation. Its performance in demanding tasks justifies its operational token cost. Choose GLM-5.2 if your workflow prioritizes Large-scale repository refactoring, national research initiatives, and sovereign enterprise intelligence. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does GLM-5.1 compare to GLM-5.2 in API token pricing and cost per token?+
Both GLM-5.1 and GLM-5.2 are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: GLM-5.1 or GLM-5.2?+
GLM-5.1 supports a context window of 200K tokens (~150k words), while GLM-5.2 supports 1,000K tokens (~750k words). Therefore, GLM-5.2 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GLM-5.1 or GLM-5.2?+
GLM-5.1 scores 63% on SWE-bench Verified while GLM-5.2 scores 67.5%. For autonomous software engineering and multi-file debugging, GLM-5.2 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GLM-5.1 and GLM-5.2?+
GLM-5.1 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GLM-5.2 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GLM-5.1 and GLM-5.2?+
GLM-5.1 generates approximately 65 tokens per second, while GLM-5.2 delivers 60 tokens per second. In latency-sensitive production environments, GLM-5.1 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GLM-5.1 and GLM-5.2 to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to GLM-5.1, and route deep architectural or complex reasoning queries to GLM-5.1. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs