Qwen3-Next 80B-A3B Instruct vs Qwen 2.5 72B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3-Next 80B-A3B Instruct by Alibaba and Qwen 2.5 72B by Alibaba 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, Qwen3-Next 80B-A3B Instruct holds a notable pricing advantage for token ingestion. On output generation, Qwen3-Next 80B-A3B Instruct is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen3-Next 80B-A3B Instruct provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Qwen3-Next 80B-A3B Instruct 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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Qwen3-Next 80B-A3B Instruct
Open-Weights-ModellQwen3-Next 80B-A3B Instruct von Alibaba ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 131K 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
131.1K tokens
N/A
Qwen 2.5 72B
Open-Weights-ModellQwen 2.5 72B von Alibaba ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 128K Token zu API-Preisen von $0.35/1M Input- und $0.7/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.35 / 1M
$0.70 / 1M
128K tokens
45.2%
Wichtigste Unterschiede auf einen Blick
- Qwen3-Next 80B-A3B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
- Qwen3-Next 80B-A3B Instruct has a larger context window (131K vs 128K tokens).
- Qwen3-Next 80B-A3B Instruct is faster at 135 tok/s vs 64 tok/s.
| Feature | Qwen3-Next 80B-A3B Instruct | Qwen 2.5 72B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 78 | 44 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.00 / 1M | $0.35 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.70 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.19 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 135 tok/s | 64 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 45.2% |
GPQA Diamond Graduate-level reasoning | 60% | 52.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.07 / 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-Next 80B-A3B Instruct · Strengths & Key Advantages
- Fortschrittliche Standard Instruction-Tuned Sparse Mixture-of-Experts-Architektur von Alibaba mit 131K 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
Qwen 2.5 72B · Strengths & Key Advantages
- Fortschrittliche Dense Autoregressive Transformer-Architektur von Alibaba mit 128K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.35/1M Input, $0.7/1M 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-Next 80B-A3B Instruct | Qwen 2.5 72B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.28 |
Output Tokens / Task Tokens generated per task | 15K | 32K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3-Next 80B-A3B Instruct | Qwen 2.5 72B |
|---|---|---|
| Official API Model ID (SDK) | qwen3-next-80b-a3b-instruct-instruct | qwen-2-5-72b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-09 | 2024-09 |
| Knowledge Cutoff | August 2025 | September 2024 |
| Architecture | Standard Instruction-Tuned Sparse Mixture-of-Experts | Dense Autoregressive Transformer |
| Parameters | 80B total (3B active MoE) | 72B |
| 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) | Flat rate ($0.35/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.07/1M tokens |
| 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) · 50 GB VRAM (4-bit Quantized) · 172 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 45 GB VRAM (4-bit Quantized) · 155 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 |
Qwen3-Next 80B-A3B Instruct vs Qwen 2.5 72B: Schnelle Antworten (FAQ)
Which is more intelligent, Qwen3-Next 80B-A3B Instruct or Qwen 2.5 72B?
Qwen3-Next 80B-A3B Instruct is more intelligent. It scores 78 compared with 44 on the composite Intelligence Index.
Which model is faster?
Qwen3-Next 80B-A3B Instruct is faster, generating 135 tokens per second compared with 64 tokens per second.
Which model is cheaper to run?
Qwen3-Next 80B-A3B Instruct is cheaper at $0.00 per 1M tokens blended, compared with $0.19 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3-Next 80B-A3B Instruct has lower latency, with a time to first token of 0.31s compared with 0.61s.
Which model has a larger context window?
Qwen3-Next 80B-A3B Instruct has a larger context window, supporting 131.1K tokens compared with 128K tokens.
Fazit
Verdict & Model Selection Recommendation: Choose Qwen3-Next 80B-A3B Instruct if your priority is General conversational agents, customer care, and low-latency API wrappers. Its performance in demanding tasks justifies its operational token cost. Choose Qwen 2.5 72B if your workflow prioritizes Self-hosted enterprise agents, mathematical computing, and private coding assistants. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does Qwen3-Next 80B-A3B Instruct compare to Qwen 2.5 72B in API token pricing and cost per token?+
Qwen3-Next 80B-A3B Instruct costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas Qwen 2.5 72B costs $0.35 input and $0.70 output. Overall, Qwen3-Next 80B-A3B Instruct is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Qwen3-Next 80B-A3B Instruct or Qwen 2.5 72B?+
Qwen3-Next 80B-A3B Instruct supports a context window of 131K tokens (~98k words), while Qwen 2.5 72B supports 128K tokens (~96k words). Therefore, Qwen3-Next 80B-A3B Instruct can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3-Next 80B-A3B Instruct or Qwen 2.5 72B?+
Qwen3-Next 80B-A3B Instruct scores at the frontier of coding benchmarks, while Qwen 2.5 72B scores 45.2%. For complex autonomous debugging and agentic engineering, Qwen 2.5 72B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen3-Next 80B-A3B Instruct and Qwen 2.5 72B?+
Qwen3-Next 80B-A3B Instruct is optimized for low-latency direct generation. In contrast, Qwen 2.5 72B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Qwen3-Next 80B-A3B Instruct and Qwen 2.5 72B?+
Qwen3-Next 80B-A3B Instruct generates approximately 135 tokens per second, while Qwen 2.5 72B delivers 64 tokens per second. In latency-sensitive production environments, Qwen3-Next 80B-A3B Instruct provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3-Next 80B-A3B Instruct and Qwen 2.5 72B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3-Next 80B-A3B Instruct to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Qwen 2.5 72B. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs