Gemini 4 Argon vs Qwen3.8 Max Preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 4 Argon by Google against Qwen3.8 Max Preview by Alibaba across real-world coding, reasoning, token economics, and latency.
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Gemini 4 Argon
深度推理模型Google 推出的 Gemini 4 Argon 人工智能模型，具备 1M 上下文窗口，输入价格为 $2/百万 Token，输出价格为 $10/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Qwen3.8 Max Preview
开源权重模型Alibaba 推出的 Qwen3.8 Max Preview 人工智能模型，具备 1M 上下文窗口，输入价格为 $2.0/百万 Token，输出价格为 $6.0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.00 / 1M
$0.00 / 1M
1M tokens
75.2%
核心差异一览
- Qwen3.8 Max Preview is 1.0x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon is faster at 115 tok/s vs 60 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 75.2%).
| Feature | Gemini 4 Argon | Qwen3.8 Max Preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 97 | 93 |
Provider | Alibaba | |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.47 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 1000K | 32K |
Throughput Speed Tokens generated per second | 115 tok/s | 60 tok/s |
SWE-bench Verified Real-world software engineering | 81.9% | 75.2% |
GPQA Diamond Graduate-level reasoning | 81.5% | 79.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.00 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 4 Argon · Strengths & Key Advantages
- 由 Google 研发的高精度 Pathways Multimodal Sparse Mixture-of-Experts 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $2/1M，输出 $10/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Qwen3.8 Max Preview · Strengths & Key Advantages
- 由 Alibaba 研发的高精度 Colossus-Scale Open Sparse Mixture-of-Experts 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $2.0/1M，输出 $6.0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 4 Argon | Qwen3.8 Max Preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.45 | $0.00 |
Output Tokens / Task Tokens generated per task | 75K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 15K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 4 Argon | Qwen3.8 Max Preview |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-4-argon | qwen3-8-max-preview-instruct |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-07 |
| Knowledge Cutoff | June 2026 | April 2026 |
| Architecture | Pathways Multimodal Sparse Mixture-of-Experts | Colossus-Scale Open Sparse Mixture-of-Experts |
| Parameters | Confidential | 2.4T total (95B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1488 GB VRAM (4-bit Quantized) · 5160 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision, audio, video | text, vision |
| Output Modalities | text | text |
Gemini 4 Argon vs Qwen3.8 Max Preview: 常见问题解答 (FAQ)
Which is more intelligent, Gemini 4 Argon or Qwen3.8 Max Preview?
Gemini 4 Argon is more intelligent. It scores 97 compared with 93 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 60 tokens per second.
Which model is cheaper to run?
Qwen3.8 Max Preview is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3.8 Max Preview has lower latency, with a time to first token of 0.68s compared with 0.85s.
综合结论
Choose Gemini 4 Argon for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 google 生态深度集成, or choose Qwen3.8 Max Preview for 企业级 ai 自动化、代码研发、智能体（agent）工作流以及 alibaba 生态深度集成.
常见问题 (FAQ)
Which is cheaper: Gemini 4 Argon or Qwen3.8 Max Preview?+
Gemini 4 Argon costs $2/1M input and $10/1M output tokens, while Qwen3.8 Max Preview costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Gemini 4 Argon or Qwen3.8 Max Preview?+
Gemini 4 Argon supports 1M tokens whereas Qwen3.8 Max Preview 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.
数据来源: Google & Alibaba Official Documentation