Qwen3.5 9B vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen3.5 9B by Alibaba against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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Qwen3.5 9B
Open-Weights-ModellQwen3.5 9B von Alibaba ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 262K Token zu API-Preisen von $0/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
GPT-6 Astra
Deep-Reasoning-ModellGPT-6 Astra von OpenAI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $10.0/1M Input- und $50.0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$10.00 / 1M
$50.00 / 1M
272K tokens
82.4%
Wichtigste Unterschiede auf einen Blick
- Qwen3.5 9B is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 262K tokens).
- Qwen3.5 9B is faster at 165 tok/s vs 100 tok/s.
| Feature | Qwen3.5 9B | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 70 | 96 |
Provider | Alibaba | 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 | 262.1K tokens~393 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 | 165 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.4% |
GPQA Diamond Graduate-level reasoning | N/A | 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 frameworkEvaluations ongoing
Geprüfte 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.
Qwen3.5 9B · Strengths & Key Advantages
- Fortschrittliche Compact Dense Autoregressive Transformer-Architektur von Alibaba mit 262K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0/1M Input, $0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
GPT-6 Astra · Strengths & Key Advantages
- Fortschrittliche Omni-Adaptive Autoregressive with Dynamic CoT-Architektur von OpenAI mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($10.0/1M Input, $50.0/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3.5 9B | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.15 |
Output Tokens / Task Tokens generated per task | 10K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 32K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3.5 9B | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | qwen3-5-9b-instruct | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-02 | 2026-09 |
| Knowledge Cutoff | December 2025 | June 2026 |
| Architecture | Compact Dense Autoregressive Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 9B | 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) · 6 GB VRAM (4-bit Quantized) · 19 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 |
Qwen3.5 9B vs GPT-6 Astra: Schnelle Antworten (FAQ)
Which is more intelligent, Qwen3.5 9B or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 70 on the composite Intelligence Index.
Which model is faster?
Qwen3.5 9B is faster, generating 165 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Qwen3.5 9B 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?
Qwen3.5 9B has lower latency, with a time to first token of 0.22s compared with 0.95s.
Which model has a larger context window?
GPT-6 Astra has a larger context window, supporting 272K tokens compared with 262.1K tokens.
Fazit
Choose Qwen3.5 9B for unternehmensautomatisierung, software-engineering, autonome ki-agenten und alibaba-produktionssysteme, or choose GPT-6 Astra for unternehmensautomatisierung, software-engineering, autonome ki-agenten und openai-produktionssysteme.
Häufig gestellte Fragen (FAQ)
Which is cheaper: Qwen3.5 9B or GPT-6 Astra?+
Qwen3.5 9B 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: Qwen3.5 9B or GPT-6 Astra?+
Qwen3.5 9B supports 262K 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: Alibaba & OpenAI Official Documentation