Qwen3 Coder Flash vs Devstral Medium
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3 Coder Flash 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 Flash holds a notable pricing advantage for token ingestion. On output generation, Qwen3 Coder Flash is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Qwen3 Coder Flash provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Qwen3 Coder Flash 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.
원하는 AI 모델 선택 및 실시간 비교
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Qwen3 Coder Flash
고성능 AI 모델Alibaba의 Qwen3 Coder Flash 모델은 1M 토큰 컨텍스트 윈도우와 입력 $0.1/1M 토큰, 출력 $0.4/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.10 / 1M
$0.40 / 1M
1M 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 Flash is 7.0x cheaper for API usage (input + output tokens combined).
- Qwen3 Coder Flash has a larger context window (1M vs 128K tokens).
- Qwen3 Coder Flash is faster at 130 tok/s vs 75 tok/s.
| Feature | Qwen3 Coder Flash | Devstral Medium |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 77 | 52 |
Provider | Alibaba | Mistral AI |
Input Price Cost per million input tokens | $0.10 / 1M | $0.80 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $2.40 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.54 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 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 | 130 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 | $0.01 / 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 Flash · Strengths & Key Advantages
- Alibaba의 첨단 Optimized Lightweight Hosted Code Transformer 아키텍처와 최대 1M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.1/1M, 출력 $0.4/1M)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
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 Flash | Devstral Medium |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $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 Flash | Devstral Medium |
|---|---|---|
| Official API Model ID (SDK) | qwen3-coder-flash-instruct | devstral-medium-latest |
| API Protocol | Alibaba REST API (OpenAI-Compatible) | Mistral AI REST API (OpenAI-Compatible) |
| Release Date | 2025-07 | 2025-07 |
| Knowledge Cutoff | May 2025 | May 2025 |
| Architecture | Optimized Lightweight Hosted Code Transformer | High-Capacity Agentic Code Transformer |
| Parameters | Confidential | Confidential (~70B) |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 1000K window | Flat rate ($0.8/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.01/1M tokens | 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 | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Alibaba Production Safety & Alignment Verified | Mistral AI Production Safety & Alignment Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text |
| Output Modalities | text | text |
Qwen3 Coder Flash vs Devstral Medium: 핵심 요약 문답 (FAQ)
Which is more intelligent, Qwen3 Coder Flash or Devstral Medium?
Qwen3 Coder Flash is more intelligent. It scores 77 compared with 52 on the composite Intelligence Index.
Which model is faster?
Qwen3 Coder Flash is faster, generating 130 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Qwen3 Coder Flash is cheaper at $0.07 per 1M tokens blended, compared with $0.54 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen3 Coder Flash has lower latency, with a time to first token of 0.33s compared with 0.50s.
Which model has a larger context window?
Qwen3 Coder Flash has a larger context window, supporting 1M tokens compared with 128K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose Qwen3 Coder Flash if your priority is CI/CD linters, PR automated feedback bots, and responsive interactive code generators. 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 Flash compare to Devstral Medium in API token pricing and cost per token?+
Qwen3 Coder Flash costs $0.10 per 1M input tokens and $0.40 per 1M output tokens, whereas Devstral Medium costs $0.80 input and $2.40 output. Overall, Qwen3 Coder Flash 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 Flash or Devstral Medium?+
Qwen3 Coder Flash supports a context window of 1,000K tokens (~750k words), while Devstral Medium supports 128K tokens (~96k words). Therefore, Qwen3 Coder Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3 Coder Flash or Devstral Medium?+
Qwen3 Coder Flash 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 Flash and Devstral Medium?+
Qwen3 Coder Flash 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 Flash and Devstral Medium?+
Qwen3 Coder Flash generates approximately 130 tokens per second, while Devstral Medium delivers 75 tokens per second. In latency-sensitive production environments, Qwen3 Coder Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3 Coder Flash and Devstral Medium to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Qwen3 Coder Flash 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.
Related reading
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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