Qwen3-Coder 30B-A3B Instruct vs Devstral Medium
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3-Coder 30B-A3B Instruct by Alibaba and Devstral Medium by Mistral AI 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-Coder 30B-A3B Instruct holds a notable pricing advantage for token ingestion. On output generation, Qwen3-Coder 30B-A3B Instruct is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen3-Coder 30B-A3B Instruct provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Qwen3-Coder 30B-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-Coder 30B-A3B Instruct
오픈 웨이트 모델Alibaba의 Qwen3-Coder 30B-A3B Instruct 모델은 262K 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Devstral Medium
심층 추론 모델Mistral AI의 Devstral Medium 모델은 128K 토큰 컨텍스트 윈도우와 입력 $0.8/1M 토큰, 출력 $2.4/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.80 / 1M
$2.40 / 1M
128K tokens
51.5%
한눈에 보는 핵심 차이점
- 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 |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3-Coder 30B-A3B Instruct · Strengths & Key Advantages
- Alibaba의 첨단 Compact Sparse Mixture-of-Experts Transformer 아키텍처와 최대 262K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Devstral Medium · Strengths & Key Advantages
- Mistral AI의 첨단 High-Capacity Agentic Code Transformer 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.8/1M, 출력 $2.4/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
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 |
기술 사양 (Specifications)
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: 핵심 요약 문답 (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.
종합 평가
Verdict & Model Selection Recommendation: Choose Qwen3-Coder 30B-A3B Instruct if your priority is IDE plugins, fast inline copilot completions, unit test generation, and CI bug triage. Its performance in demanding tasks justifies its operational token cost. Choose Devstral Medium if your workflow prioritizes Autonomous bug-fixing bots, CI/CD pipeline automation, and enterprise repository refactoring. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does Qwen3-Coder 30B-A3B Instruct compare to Devstral Medium in API token pricing and cost per token?+
Qwen3-Coder 30B-A3B Instruct costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas Devstral Medium costs $0.80 input and $2.40 output. Overall, Qwen3-Coder 30B-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-Coder 30B-A3B Instruct or Devstral Medium?+
Qwen3-Coder 30B-A3B Instruct supports a context window of 262K tokens (~197k words), while Devstral Medium supports 128K tokens (~96k words). Therefore, Qwen3-Coder 30B-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-Coder 30B-A3B Instruct or Devstral Medium?+
Qwen3-Coder 30B-A3B Instruct scores at the frontier of coding benchmarks, while Devstral Medium scores 51.5%. For complex autonomous debugging and agentic engineering, Devstral Medium provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen3-Coder 30B-A3B Instruct and Devstral Medium?+
Qwen3-Coder 30B-A3B Instruct is optimized for low-latency direct generation. In contrast, Devstral Medium provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Qwen3-Coder 30B-A3B Instruct and Devstral Medium?+
Qwen3-Coder 30B-A3B Instruct generates approximately 140 tokens per second, while Devstral Medium delivers 75 tokens per second. In latency-sensitive production environments, Qwen3-Coder 30B-A3B Instruct provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3-Coder 30B-A3B Instruct and Devstral Medium to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3-Coder 30B-A3B Instruct to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Devstral Medium. 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.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs