Qwen 2.5 32B Instruct vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Qwen 2.5 32B Instruct by Alibaba against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Qwen 2.5 32B Instruct
Open-Weights-ModellQwen 2.5 32B Instruct von Alibaba ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 131K Token zu API-Preisen von $0.18/1M Input- und $0.36/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.18 / 1M
$0.36 / 1M
131.1K tokens
40.8%
Gemini 4 Argon
Deep-Reasoning-ModellGemini 4 Argon von Google ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $2/1M Input- und $10/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Wichtigste Unterschiede auf einen Blick
- Qwen 2.5 32B Instruct is 18.8x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 131K tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 85 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 40.8%).
| Feature | Qwen 2.5 32B Instruct | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 41 | 97 |
Provider | Alibaba | |
Input Price Cost per million input tokens | $0.18 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.36 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 1000K |
Throughput Speed Tokens generated per second | 85 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 40.8% | 81.9% |
GPQA Diamond Graduate-level reasoning | 48.5% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.04 / 1M | $0.10 / 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.
Qwen 2.5 32B Instruct · Strengths & Key Advantages
- Fortschrittliche Dense Autoregressive Transformer-Architektur von Alibaba mit 131K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.18/1M Input, $0.36/1M 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
Gemini 4 Argon · Strengths & Key Advantages
- Fortschrittliche Pathways Multimodal Sparse Mixture-of-Experts-Architektur von Google mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($2/1M Input, $10/1M 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 | Qwen 2.5 32B Instruct | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.16 | $2.45 |
Output Tokens / Task Tokens generated per task | 28K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen 2.5 32B Instruct | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | qwen-2-5-32b-instruct | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-09 | 2026-09 |
| Knowledge Cutoff | June 2024 | June 2026 |
| Architecture | Dense Autoregressive Transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 32B | 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.18/1M in) across full 131K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.036/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| 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) · Video (~258–300 tokens/sec) |
| 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) | 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) | Google DeepMind Frontier Safety Framework Verified |
| 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, video |
| Output Modalities | text | text |
Qwen 2.5 32B Instruct vs Gemini 4 Argon: Schnelle Antworten (FAQ)
Which is more intelligent, Qwen 2.5 32B Instruct or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 41 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
Qwen 2.5 32B Instruct is cheaper at $0.10 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen 2.5 32B Instruct has lower latency, with a time to first token of 0.48s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 131.1K tokens.
Fazit
Choose Qwen 2.5 32B Instruct for unternehmensautomatisierung, software-engineering, autonome ki-agenten und alibaba-produktionssysteme, or choose Gemini 4 Argon for unternehmensautomatisierung, software-engineering, autonome ki-agenten und google-produktionssysteme.
Häufig gestellte Fragen (FAQ)
Which is cheaper: Qwen 2.5 32B Instruct or Gemini 4 Argon?+
Qwen 2.5 32B Instruct costs $0.18/1M input and $0.36/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Qwen 2.5 32B Instruct or Gemini 4 Argon?+
Qwen 2.5 32B Instruct supports 131K tokens whereas Gemini 4 Argon supports 1M 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 & Google Official Documentation