Head-to-Head Model Comparison

GLM-5 vs GPT-6 Astra

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale
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Zhipu AIOpenAIopen-sourceflagship

Compare GLM-5 by Zhipu AI against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.

Comparaison Face-à-Face (VS)

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Live comparison: no in-depth editorial review has been published for this pair yet.

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Zhipu AI

GLM-5

Modèle phare général

Zhipu d'AI next-génération open source phare introducing revolutionary raisonnement, ingénierie logicielle autonomy, et advanced mathématique proofs.

Intelligence Index87 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

204.8K tokens

SWE-bench Verified:

58%

OpenAI

GPT-6 Astra

Agent d'OS autonome

d'OpenAI late-2026 phare modèle de fondation intégrant natif utilisation de l'ordinateur agents, dynamic UI orchestration, et profond STEM raisonnement.

Intelligence Index96 / 100
Input Token:

$10.00 / 1M

Output Token:

$50.00 / 1M

Context Window:

272K tokens

SWE-bench Verified:

82.4%

Différences Clés en un Coup d’Œil

  • GLM-5 is 1.0x cheaper for API usage (input + output tokens combined).
  • GPT-6 Astra has a larger context window (272K vs 205K tokens).
  • GPT-6 Astra is faster at 100 tok/s vs 68 tok/s.
  • GPT-6 Astra leads on SWE-bench coding (82.4% vs 58.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaractéristiqueGLM-5GPT-6 Astra
Intelligence Index
Composite reasoning & knowledge score (0-100)
8796
Provider
Zhipu AIOpenAI
Input Price
Cost per million input tokens
$0.00 / 1M$10.00 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$50.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$7.70 / 1M
Context Window
Maximum input + output tokens
204.8K tokens~307 A4 pages of text272K tokens~408 A4 pages of text
Max Output Tokens
Per single request
16K128K
Throughput Speed
Tokens generated per second
68 tok/s100 tok/s
SWE-bench Verified
Real-world software engineering
58%82.4%
GPQA Diamond
Graduate-level reasoning
68%80.9%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$1.00 / 1M

Qui gagne où ?

At-a-glance decision framework
Code
GPT-6 Astra
+24.4% SWE-bench lead
Coût
GLM-5
1.0x cheaper monthly runtime
Vitesse
GPT-6 Astra
+47% faster generation
Contexte
GPT-6 Astra
272K tokens window

Scores de Benchmarks Vérifiés

Official evaluation metrics verified by independent third-party research labs.

GLM-5GPT-6 Astra
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

58%vs82.4%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

68%vs80.9%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

84%vs89.9%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1390ptsvs1520pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs18.2%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs63.8%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs54.9%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs79.8%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs93.6%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs91.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

68 tok/s vs100 tok/s

Latence et Temps de Réponse

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
GLM-50.58s
GPT-6 Astra0.95s
Est. End-to-End Response (500 tokens)TTFT + generation time
GLM-57.93s
GPT-6 Astra5.95s

Simulateur Interactif de Coût de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
GLM-5 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
GPT-6 Astra Monthly API Bill
$61.50 / month
$10/1M in · $50/1M out
GLM-5 saves approximately $61.50 / month (100%) under this workload.1.0x Savings

GLM-5 · Strengths & Key Advantages

  • de pointe-classe ouvert raisonnement achieving un Elo rating de 1390 sur LMSYS Arena
  • solide SWE-bench Verified codage score (58%) pour complet-featured development agents
  • Complete open source version empowering souverain d'entreprise installations
Considerations & Trade-offs
  • texte-seulement à initial launch (vision handled par separate Turbo VL variants)
  • élevé GPU requirements require distributed cluster nodes pour unquantized hébergement

GPT-6 Astra · Strengths & Key Advantages

  • Flawless OS-level navigation et utilisation de l'ordinateur automatisation
  • Seamless en temps réel multimodal voix et vision interactivity
  • Dynamic chaîne de pensée qui self-adjusts profondeur based sur query difficulty
  • immense 1M fenêtre de contexte avec instant prefix caching
Considerations & Trade-offs
  • Strict sécurité filters peut occasionally throttle complexe penetration testing prompts
  • Slightly supérieur raisonnement latence sur élevé-effort mathématiques queries

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricGLM-5GPT-6 Astra
Cost per Task
Estimated USD to finish one task
$0.00$2.15
Output Tokens / Task
Tokens generated per task
24K58K
Reasoning Tokens / Task
Thinking tokens burned per task
8K32K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

CaractéristiqueGLM-5GPT-6 Astra
Official API Model ID (SDK)glm-5gpt-6-astra
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI Responses API & Chat Completions v1
Release Date2026-022026-09
Knowledge CutoffDecember 2025June 2026
ArchitectureFrontier Sparse Mixture-of-Experts Autoregressive TransformerOmni-Adaptive Autoregressive with Dynamic CoT
Parameters240B MoEConfidential
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Free across full context window (only KV-cache VRAM scales)Flat rate ($10/1M in) across full 272K window
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix caching at $1/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 149 GB VRAM (4-bit Quantized) · 516 GB VRAM (Native BF16)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 ControlSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext, vision, audio
Output Modalitiestexttext, audio

GLM-5 vs GPT-6 Astra: Réponses Rapides (FAQ)

Which is more intelligent, GLM-5 or GPT-6 Astra?

GPT-6 Astra is more intelligent. It scores 96 compared with 87 on the composite Intelligence Index.

Which model is faster?

GPT-6 Astra is faster, generating 100 tokens per second compared with 68 tokens per second.

Which model is cheaper to run?

GLM-5 is cheaper at $0.00 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GLM-5 has lower latency, with a time to first token of 0.58s compared with 0.95s.

Which model has a larger context window?

GPT-6 Astra has a larger context window, supporting 272K tokens compared with 204.8K tokens.

Verdict

Choose GLM-5 for d'entreprise logiciel development agents, recherche automatisation, et privé d'entreprise intelligence, or choose GPT-6 Astra for autonome browser/computer flux de travail, en temps réel voix agents, et élevé-difficulty mathématique raisonnement.

Questions fréquentes

Which is cheaper: GLM-5 or GPT-6 Astra?+

GLM-5 costs $0/1M input and $0/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.

Which model has a larger context window: GLM-5 or GPT-6 Astra?+

GLM-5 supports 205K tokens whereas GPT-6 Astra supports 272K tokens.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.

Sources: Zhipu AI & OpenAI Official Documentation