Head-to-Head Model Comparison

Devstral 2 vs Qwen3-Coder 480B-A35B Instruct

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

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Devstral 2Profile

Qwen3-Coder 480B-A35B InstructProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Devstral 2

IA Agéntica y Uso de Herramientas

Devstral 2 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 262K, velocidad de 70 tokens/s, índice de inteligencia de 56/100 y

Intelligence Index56 / 100

Input Token:

$1.50 / 1M

Output Token:

$4.50 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

58.2%

Alibaba

Qwen3-Coder 480B-A35B Instruct

Modelo Base de Pesos Abiertos

Qwen3-Coder 480B-A35B 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 52 tokens/s, índice de inteligen

Intelligence Index86 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

56.5%

Diferencias Clave de un Vistazo

Qwen3-Coder 480B-A35B Instruct is 1.0x cheaper for API usage (input + output tokens combined).
Devstral 2 is faster at 70 tok/s vs 52 tok/s.
Devstral 2 leads on SWE-bench coding (58.2% vs 56.5%).

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

Característica	Devstral 2	Qwen3-Coder 480B-A35B Instruct
Intelligence Index Composite reasoning & knowledge score (0-100)	56	86
Provider	Mistral AI	Alibaba
Input Price Cost per million input tokens	$1.50 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$4.50 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.99 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	262.1K tokens~393 A4 pages of text
Max Output Tokens Per single request	66K	16K
Throughput Speed Tokens generated per second	70 tok/s	52 tok/s
SWE-bench Verified Real-world software engineering	58.2%	56.5%
GPQA Diamond Graduate-level reasoning	66.5%	70%
Cached Input Price Prompt caching discount per 1M tokens	$0.35 / 1M	$0.00 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Devstral 2

+1.7% SWE-bench lead

Costo

Qwen3-Coder 480B-A35B Instruct

1.0x cheaper monthly runtime

Velocidad

Devstral 2

+35% faster generation

Contexto

Tied

262.1K tokens window

Puntuaciones de Benchmarks Verificadas

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

Devstral 2Qwen3-Coder 480B-A35B Instruct

SWE-bench Verified (Autonomous Coding)

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

58.2%vs56.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

66.5%vs70%

MMLU-Pro (Multi-task Language Understanding)

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

83.5%vs83%

LMSYS Chatbot Arena Elo

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

1365ptsvs1355pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

52%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs73%

AIME (Advanced Math Competition)

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

78%vsN/A

Long-Context Reasoning

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

82%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

70 tok/s vs52 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

Devstral 20.52s

Qwen3-Coder 480B-A35B Instruct0.76s

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

Devstral 27.66s

Qwen3-Coder 480B-A35B Instruct10.38s

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)

Devstral 2 Monthly API Bill

$6.82 / month

$1.5/1M in · $4.5/1M out

Qwen3-Coder 480B-A35B Instruct Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3-Coder 480B-A35B Instruct saves approximately $6.82 / month (100%) under this workload.1.0x Savings

Devstral 2 · Strengths & Key Advantages

Ventana de contexto de 262K con hasta 66K tokens de salida máxima (SWE-bench Verified 58.2% · GPQA Diamond 66.5%)
Rendimiento ágil de 70 tokens/segundo con latencia inicial (TTFT) de solo 520 ms
Pesos abiertos (123B) 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

Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages

Ventana de contexto de 262K con hasta 16K tokens de salida máxima (SWE-bench Verified 56.5% · GPQA Diamond 70%)
Rendimiento ágil de 52 tokens/segundo con latencia inicial (TTFT) de solo 760 ms
Pesos abiertos (480B total (35B 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

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Devstral 2	Qwen3-Coder 480B-A35B Instruct
Cost per Task Estimated USD to finish one task	$0.65	$0.00
Output Tokens / Task Tokens generated per task	42K	25K
Reasoning Tokens / Task Thinking tokens burned per task	20K	9K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Devstral 2	Qwen3-Coder 480B-A35B Instruct
Official API Model ID (SDK)	devstral-2-latest	qwen3-coder-480b-a35b-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-12	2025-04
Knowledge Cutoff	October 2025	March 2025
Architecture	Advanced Agentic Code Transformer	Code-Specialized Sparse Mixture-of-Experts Transformer
Parameters	123B	480B total (35B active MoE)
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Flat rate ($1.5/1M in) across full 262K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.35/1M tokens	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
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) · 76 GB VRAM (4-bit Quantized) · 264 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 298 GB VRAM (4-bit Quantized) · 1032 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text	text
Output Modalities	text	text

Devstral 2 vs Qwen3-Coder 480B-A35B Instruct: Respuestas Rápidas (FAQ)

Which is more intelligent, Devstral 2 or Qwen3-Coder 480B-A35B Instruct?

Qwen3-Coder 480B-A35B Instruct is more intelligent. It scores 86 compared with 56 on the composite Intelligence Index.

Which model is faster?

Devstral 2 is faster, generating 70 tokens per second compared with 52 tokens per second.

Which model is cheaper to run?

Qwen3-Coder 480B-A35B Instruct is cheaper at $0.00 per 1M tokens blended, compared with $0.99 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Devstral 2 has lower latency, with a time to first token of 0.52s compared with 0.76s.

Veredicto

Tanto Devstral 2 (Ideal para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, sistemas RAG empresariales y automatización mediante API.) como Qwen3-Coder 480B-A35B Instruct (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Qwen3-Coder 480B-A35B Instruct lidera en inteligencia general y programación, Devstral 2 ofrece mayor velocidad de generación de tokens y Qwen3-Coder 480B-A35B Instruct resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Devstral 2 o Qwen3-Coder 480B-A35B Instruct?+

En las pruebas de inteligencia compuesta y resolución de código, Qwen3-Coder 480B-A35B Instruct obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Devstral 2 frente a Qwen3-Coder 480B-A35B Instruct?+

Devstral 2 cuesta $1.50 / $4.50 por millón de tokens de entrada/salida, mientras que Qwen3-Coder 480B-A35B Instruct cuesta $0.00 / $0.00. En costo combinado, Qwen3-Coder 480B-A35B Instruct es la opción más económica.

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

Devstral 2 cuenta con 262.1K tokens de contexto frente a 262.1K tokens de Qwen3-Coder 480B-A35B Instruct. En velocidad de inferencia, Devstral 2 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)

Devstral 2 vs Qwen3-Coder 480B-A35B Instruct

Cambia y Compara Cualquier Modelo

Devstral 2

Qwen3-Coder 480B-A35B Instruct

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Devstral 2 · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3-Coder 480B-A35B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Devstral 2 vs Qwen3-Coder 480B-A35B Instruct: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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