GLM-4.5 vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GLM-4.5 by Zhipu AI against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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GLM-4.5
Open-Weights-Reasoning-ModellGLM-4.5 von Zhipu AI ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 131K Kontextfenster, 75 Token/s Durchsatz, einem Intelligenz-Index von 80/100 und kostenlosem Open-Weight
$0.00 / 1M
$0.00 / 1M
131.1K tokens
42%
GPT-6 Astra
Natives Sprach- & AudiomodellGPT-6 Astra von OpenAI ist ein KI-Modell für natürliche Sprachsynthese, Echtzeit-Audioverarbeitung und präzise Transkription mit 272K Kontextfenster, 100 Token/s Durchsatz, einem Intelligenz-Index von 96/100 und $10/$50 pro 1M Token.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Wichtigste Unterschiede auf einen Blick
- GLM-4.5 is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 131K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 75 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 42.0%).
| Feature | GLM-4.5 | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 80 | 96 |
Provider | Zhipu AI | OpenAI |
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 | 131.1K tokens~197 A4 pages of text | 272K tokens~408 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 75 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 42% | 82.4% |
GPQA Diamond Graduate-level reasoning | 61% | 80.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $1.00 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-4.5 · Strengths & Key Advantages
- 131K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 42% · GPQA Diamond 61%)
- Hohe Generierungsgeschwindigkeit von 75 Token/Sekunde bei nur 550 ms Erstlatenz (TTFT)
- Offene Modellgewichte (130B MoE) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
- Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
- Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen
GPT-6 Astra · Strengths & Key Advantages
- 272K Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 82.4% · GPQA Diamond 80.9%)
- Hohe Generierungsgeschwindigkeit von 100 Token/Sekunde bei nur 950 ms Erstlatenz (TTFT)
- Effiziente API-Struktur ($10/$50 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
- Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
- Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.5 | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.15 |
Output Tokens / Task Tokens generated per task | 16K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 32K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.5 | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | glm-4-5 | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-07 | 2026-09 |
| Knowledge Cutoff | May 2025 | June 2026 |
| Architecture | Hybrid Reasoning Mixture-of-Experts Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 130B MoE | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 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 & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $1/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 81 GB VRAM (4-bit Quantized) · 280 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-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 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision, audio |
| Output Modalities | text | text, audio |
GLM-4.5 vs GPT-6 Astra: Schnelle Antworten (FAQ)
Which is more intelligent, GLM-4.5 or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 80 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
GLM-4.5 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-4.5 has lower latency, with a time to first token of 0.55s compared with 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 131.1K tokens.
Fazit
Choose GLM-4.5 for optimal für open-weights-reasoning mit transparenter gedankenketten-verarbeitung (chain-of-thought), enterprise-rag-systeme und skalierbare api-pipelines., or choose GPT-6 Astra for optimal für natürliche sprachsynthese, echtzeit-audioverarbeitung und präzise transkription, enterprise-rag-systeme und skalierbare api-pipelines..
Häufig gestellte Fragen (FAQ)
Which is cheaper: GLM-4.5 or GPT-6 Astra?+
GLM-4.5 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-4.5 or GPT-6 Astra?+
GLM-4.5 supports 131K tokens whereas GPT-6 Astra supports 272K tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Offizielle Quellen: Zhipu AI & OpenAI Official Documentation