Qwen3-Coder 30B-A3B Instruct vs Devstral Medium
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Alibaba 的 Qwen3-Coder 30B-A3B Instruct 与 Mistral AI 的 Devstral Medium 是当前备受关注的两款主力 AI 模型。Qwen3-Coder 30B-A3B Instruct 提供 262.1K Token 上下文窗口，API 定价为每百万 Token 输入 $0.00 / 输出 $0.00；而 Devstral Medium 提供 128K Token 上下文窗口，定价为每百万 Token 输入 $0.80 / 输出 $2.40。以下为两者的基准测试、延迟与成本全面对比。
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Qwen3-Coder 30B-A3B Instruct
开源权重基础大模型Alibaba 推出的 Qwen3-Coder 30B-A3B Instruct 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 262K 上下文窗口、140 Token/秒生成速度与 76/100 综合智能指数（免费开源权重）。
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Devstral Medium
自主智能体与工具专家Mistral AI 推出的 Devstral Medium 是一款专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的大模型，具备 128K 上下文窗口、75 Token/秒生成速度与 52/100 综合智能指数（每百万 Token $0.8 输入 / $2.4 输出）。
$0.80 / 1M
$2.40 / 1M
128K tokens
51.5%
核心差异一览
- Qwen3-Coder 30B-A3B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
- Qwen3-Coder 30B-A3B Instruct has a larger context window (262K vs 128K tokens).
- Qwen3-Coder 30B-A3B Instruct is faster at 140 tok/s vs 75 tok/s.
| Feature | Qwen3-Coder 30B-A3B Instruct | Devstral Medium |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 76 | 52 |
Provider | Alibaba | Mistral AI |
Input Price Cost per million input tokens | $0.00 / 1M | $0.80 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $2.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.54 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 33K |
Throughput Speed Tokens generated per second | 140 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 51.5% |
GPQA Diamond Graduate-level reasoning | N/A | 62% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.20 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
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.
Qwen3-Coder 30B-A3B Instruct · Strengths & Key Advantages
- 提供 262K 上下文窗口与最高 8K 输出容量，核心基准表现优异（MMLU-Pro 74% · Arena Elo 1285）
- 高达 140 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 280 毫秒
- 开放模型权重 (30B total (3B MoE))，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Devstral Medium · Strengths & Key Advantages
- 提供 128K 上下文窗口与最高 33K 输出容量，核心基准表现优异（SWE-bench Verified 51.5% · GPQA Diamond 62%）
- 高达 75 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 500 毫秒
- 极具竞争力的 API 定价（每百万 Token $0.8 输入 / $2.4 输出），原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
- 闭源专有云端模型，不支持下载权重进行离线本地物理隔离部署
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Qwen3-Coder 30B-A3B Instruct | Devstral Medium |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.55 |
Output Tokens / Task Tokens generated per task | 15K | 38K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 18K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Qwen3-Coder 30B-A3B Instruct | Devstral Medium |
|---|---|---|
| Official API Model ID (SDK) | qwen3-coder-30b-a3b-instruct | devstral-medium-latest |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Mistral AI REST API (OpenAI-Compatible) |
| Release Date | 2025-04 | 2025-07 |
| Knowledge Cutoff | March 2025 | May 2025 |
| Architecture | Compact Sparse Mixture-of-Experts Transformer | High-Capacity Agentic Code Transformer |
| Parameters | 30B total (3B active MoE) | Confidential (~70B) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.8/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.2/1M 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) | API Prompt Engineering & RAG Distillation |
| 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) · 19 GB VRAM (4-bit Quantized) · 65 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) | Mistral AI Production Safety & Alignment 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 |
| Output Modalities | text | text |
Qwen3-Coder 30B-A3B Instruct vs Devstral Medium: 常见问题解答 (FAQ)
Which is more intelligent, Qwen3-Coder 30B-A3B Instruct or Devstral Medium?
Qwen3-Coder 30B-A3B Instruct is more intelligent. It scores 76 compared with 52 on the composite Intelligence Index.
Which model is faster?
Qwen3-Coder 30B-A3B Instruct is faster, generating 140 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen3-Coder 30B-A3B Instruct is cheaper at $0.00 per 1M tokens blended, compared with $0.54 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3-Coder 30B-A3B Instruct has lower latency, with a time to first token of 0.28s compared with 0.50s.
Which model has a larger context window?
Qwen3-Coder 30B-A3B Instruct has a larger context window, supporting 262.1K tokens compared with 128K tokens.
综合结论
综合来看，Qwen3-Coder 30B-A3B Instruct（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Devstral Medium（适合：专为长周期自主智能体 (Agent)、工具调用与自动化工作流打造的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Qwen3-Coder 30B-A3B Instruct 在综合智能与编程基准测试中表现更强，Qwen3-Coder 30B-A3B Instruct 拥有更快的 Token 生成速度，而 Qwen3-Coder 30B-A3B Instruct 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，Qwen3-Coder 30B-A3B Instruct 和 Devstral Medium 哪个更强？+
在综合智能指数与编程基准评测中，Qwen3-Coder 30B-A3B Instruct 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
Qwen3-Coder 30B-A3B Instruct 与 Devstral Medium 的 API 价格哪个更便宜？+
Qwen3-Coder 30B-A3B Instruct 的 API 价格为输入 $0.00 / 输出 $0.00（每百万 Token），Devstral Medium 为输入 $0.80 / 输出 $2.40。综合混合成本来看，Qwen3-Coder 30B-A3B Instruct 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
Qwen3-Coder 30B-A3B Instruct 支持 262.1K Token 上下文窗口，而 Devstral Medium 支持 128K Token。在流式生成速度方面，Qwen3-Coder 30B-A3B Instruct 的吞吐表现更快。
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: Alibaba 与 Mistral AI 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。