Head-to-Head Model Comparison

Qwen3-Coder 30B-A3B Instruct vs Devstral Medium

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

Cambia y Compara Cualquier Modelo

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Qwen3-Coder 30B-A3B InstructProfile

Devstral MediumProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3-Coder 30B-A3B Instruct

Modelo Base de Pesos Abiertos

Qwen3-Coder 30B-A3B Instruct de Alibaba es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 262K, velocidad de 140 tokens/s, índice de inteligenc

Intelligence Index76 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Mistral AI

Devstral Medium

IA Agéntica y Uso de Herramientas

Devstral Medium de Mistral AI es un modelo de IA diseñado para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, con ventana de contexto de 128K, velocidad de 75 tokens/s, índice de inteligencia de 52/

Intelligence Index52 / 100

Input Token:

$0.80 / 1M

Output Token:

$2.40 / 1M

Context Window:

128K tokens

SWE-bench Verified:

51.5%

Diferencias Clave de un Vistazo

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.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Característica	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

¿Quién gana en cada área?

At-a-glance decision framework

Código

Evaluations ongoing

Costo

Qwen3-Coder 30B-A3B Instruct

1.0x cheaper monthly runtime

Velocidad

Qwen3-Coder 30B-A3B Instruct

+87% faster generation

Contexto

Qwen3-Coder 30B-A3B Instruct

262.1K tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Qwen3-Coder 30B-A3B InstructDevstral Medium

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs51.5%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs62%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

74%vs80.5%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1285ptsvs1330pts

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs45%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

63%vsN/A

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs72%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs78%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

140 tok/s vs75 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Qwen3-Coder 30B-A3B Instruct0.28s

Devstral Medium0.50s

Est. End-to-End Response (500 tokens)TTFT + generation time

Qwen3-Coder 30B-A3B Instruct3.85s

Devstral Medium7.17s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Qwen3-Coder 30B-A3B Instruct Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Devstral Medium Monthly API Bill

$3.66 / month

$0.8/1M in · $2.4/1M out

Qwen3-Coder 30B-A3B Instruct saves approximately $3.66 / month (100%) under this workload.1.0x Savings

Qwen3-Coder 30B-A3B Instruct · Strengths & Key Advantages

Ventana de contexto de 262K con hasta 8K tokens de salida máxima (MMLU-Pro 74% · Arena Elo 1285)
Rendimiento ágil de 140 tokens/segundo con latencia inicial (TTFT) de solo 280 ms
Pesos abiertos (30B total (3B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Devstral Medium · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 33K tokens de salida máxima (SWE-bench Verified 51.5% · GPQA Diamond 62%)
Rendimiento ágil de 75 tokens/segundo con latencia inicial (TTFT) de solo 500 ms
Economía de API competitiva ($0.8/$2.4 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas

Considerations & Trade-offs

Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

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

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	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: Respuestas Rápidas (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.

Veredicto

Tanto Qwen3-Coder 30B-A3B Instruct (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Devstral Medium (Ideal para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Qwen3-Coder 30B-A3B Instruct lidera en inteligencia general y programación, Qwen3-Coder 30B-A3B Instruct ofrece mayor velocidad de generación de tokens y Qwen3-Coder 30B-A3B Instruct resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Qwen3-Coder 30B-A3B Instruct o Devstral Medium?+

En las pruebas de inteligencia compuesta y resolución de código, Qwen3-Coder 30B-A3B Instruct obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Qwen3-Coder 30B-A3B Instruct frente a Devstral Medium?+

Qwen3-Coder 30B-A3B Instruct cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que Devstral Medium cuesta $0.80 / $2.40. En costo combinado, Qwen3-Coder 30B-A3B Instruct es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

Qwen3-Coder 30B-A3B Instruct cuenta con 262.1K tokens de contexto frente a 128K tokens de Devstral Medium. En velocidad de inferencia, Qwen3-Coder 30B-A3B Instruct ofrece un rendimiento superior.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Qwen3-Coder 30B-A3B Instruct vs Devstral Medium

Cambia y Compara Cualquier Modelo

Qwen3-Coder 30B-A3B Instruct

Devstral Medium

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Qwen3-Coder 30B-A3B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Devstral Medium · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Qwen3-Coder 30B-A3B Instruct vs Devstral Medium: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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