Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen 2.5 32B Instruct di Alibaba e Qwen 2.5 7B Instruct di Alibaba sono due modelli di IA di riferimento. Qwen 2.5 32B Instruct offre una finestra di contesto di 131.1K token a $0.18/$0.36 per milione di token, mentre Qwen 2.5 7B Instruct mette a disposizione 131.1K token di contesto a $0.06/$0.12 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
Cambia e Confronta Qualsiasi Modello
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Qwen 2.5 32B Instruct
Modello Base a Pesi ApertiQwen 2.5 32B Instruct di Alibaba è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 131K, velocità di 85 token/s, indice di intelligenza di
$0.18 / 1M
$0.36 / 1M
131.1K tokens
40.8%
Qwen 2.5 7B Instruct
Modello Leggero ed EconomicoQwen 2.5 7B Instruct di Alibaba è un modello di IA progettato per la massima efficienza dei costi per token e l’inferenza rapida in produzione, con finestra di contesto da 131K, velocità di 130 token/s, indice di intelligenza di 35/100 e ta
$0.06 / 1M
$0.12 / 1M
131.1K tokens
28.5%
Differenze Chiave a Colpo d’Occhio
- Qwen 2.5 7B Instruct is 3.0x cheaper for API usage (input + output tokens combined).
- Qwen 2.5 7B Instruct is faster at 130 tok/s vs 85 tok/s.
- Qwen 2.5 32B Instruct leads on SWE-bench coding (40.8% vs 28.5%).
| Feature | Qwen 2.5 32B Instruct | Qwen 2.5 7B Instruct |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 41 | 35 |
Provider | Alibaba | Alibaba |
Input Price Cost per million input tokens | $0.18 / 1M | $0.06 / 1M |
Output Price Cost per million output tokens | $0.36 / 1M | $0.12 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 85 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | 40.8% | 28.5% |
GPQA Diamond Graduate-level reasoning | 48.5% | 36.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.04 / 1M | $0.01 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen 2.5 32B Instruct · Strengths & Key Advantages
- Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 40.8% · GPQA Diamond 48.5%)
- Throughput elevato di 85 token/secondo con latenza al primo token (TTFT) di soli 480 ms
- Pesi aperti (32B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Qwen 2.5 7B Instruct · Strengths & Key Advantages
- Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 28.5% · GPQA Diamond 36.8%)
- Throughput elevato di 130 token/secondo con latenza al primo token (TTFT) di soli 290 ms
- Pesi aperti (7B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen 2.5 32B Instruct | Qwen 2.5 7B Instruct |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.16 | $0.05 |
Output Tokens / Task Tokens generated per task | 28K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen 2.5 32B Instruct | Qwen 2.5 7B Instruct |
|---|---|---|
| Official API Model ID (SDK) | qwen-2-5-32b-instruct | qwen-2-5-7b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-09 | 2024-09 |
| Knowledge Cutoff | June 2024 | June 2024 |
| Architecture | Dense Autoregressive Transformer | Dense Autoregressive Transformer |
| Parameters | 32B | 7B |
| 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) | Flat rate ($0.18/1M in) across full 131K window | Flat rate ($0.06/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.036/1M tokens | Cached input at $0.012/1M 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) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 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 |
Qwen 2.5 32B Instruct vs Qwen 2.5 7B Instruct: Risposte Rapide (FAQ)
Which is more intelligent, Qwen 2.5 32B Instruct or Qwen 2.5 7B Instruct?
Qwen 2.5 32B Instruct is more intelligent. It scores 41 compared with 35 on the composite Intelligence Index.
Which model is faster?
Qwen 2.5 7B Instruct is faster, generating 130 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
Qwen 2.5 7B Instruct is cheaper at $0.03 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen 2.5 7B Instruct has lower latency, with a time to first token of 0.29s compared with 0.48s.
Verdetto
Sia Qwen 2.5 32B Instruct (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Qwen 2.5 7B Instruct (Ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Qwen 2.5 32B Instruct guida nei benchmark di ragionamento e programmazione, Qwen 2.5 7B Instruct garantisce una generazione più rapida e Qwen 2.5 7B Instruct offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Qwen 2.5 32B Instruct o Qwen 2.5 7B Instruct?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Qwen 2.5 32B Instruct. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Qwen 2.5 32B Instruct e Qwen 2.5 7B Instruct?+
Qwen 2.5 32B Instruct costa $0.18 / $0.36 per milione di token di input/output, mentre Qwen 2.5 7B Instruct costa $0.06 / $0.12. Nel costo combinato, Qwen 2.5 7B Instruct risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Qwen 2.5 32B Instruct supporta 131.1K token di contesto contro 131.1K token di Qwen 2.5 7B Instruct, mentre Qwen 2.5 7B Instruct offre una velocità di generazione superiore.
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.
Fonti ufficiali: Documentazione API ufficiale di Alibaba e Alibaba, schede di sistema e benchmark indipendenti (2026).