Qwen3-Coder 480B-A35B Instruct vs GPT-6 Astra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen3-Coder 480B-A35B Instruct by Alibaba against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.
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Qwen3-Coder 480B-A35B Instruct
Open-Weights-ModellQwen3-Coder 480B-A35B Instruct 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
56.5%
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-Coder 480B-A35B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
- GPT-6 Astra has a larger context window (272K vs 262K tokens).
- GPT-6 Astra is faster at 100 tok/s vs 52 tok/s.
- GPT-6 Astra leads on SWE-bench coding (82.4% vs 56.5%).
| Feature | Qwen3-Coder 480B-A35B Instruct | GPT-6 Astra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 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 | 16K | 128K |
Throughput Speed Tokens generated per second | 52 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | 56.5% | 82.4% |
GPQA Diamond Graduate-level reasoning | 70% | 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.
Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages
- Fortschrittliche Code-Specialized Sparse Mixture-of-Experts 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-Coder 480B-A35B Instruct | GPT-6 Astra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.15 |
Output Tokens / Task Tokens generated per task | 25K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 9K | 32K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3-Coder 480B-A35B Instruct | GPT-6 Astra |
|---|---|---|
| Official API Model ID (SDK) | qwen3-coder-480b-a35b-instruct | gpt-6-astra |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-04 | 2026-09 |
| Knowledge Cutoff | March 2025 | June 2026 |
| Architecture | Code-Specialized Sparse Mixture-of-Experts Transformer | Omni-Adaptive Autoregressive with Dynamic CoT |
| Parameters | 480B total (35B active 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) · 298 GB VRAM (4-bit Quantized) · 1032 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-Coder 480B-A35B Instruct vs GPT-6 Astra: Schnelle Antworten (FAQ)
Which is more intelligent, Qwen3-Coder 480B-A35B Instruct or GPT-6 Astra?
GPT-6 Astra is more intelligent. It scores 96 compared with 86 on the composite Intelligence Index.
Which model is faster?
GPT-6 Astra is faster, generating 100 tokens per second compared with 52 tokens per second.
Which model is cheaper to run?
Qwen3-Coder 480B-A35B Instruct 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-Coder 480B-A35B Instruct has lower latency, with a time to first token of 0.76s 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-Coder 480B-A35B Instruct 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-Coder 480B-A35B Instruct or GPT-6 Astra?+
Qwen3-Coder 480B-A35B Instruct 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-Coder 480B-A35B Instruct or GPT-6 Astra?+
Qwen3-Coder 480B-A35B Instruct 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