Head-to-Head Model Comparison

Codestral vs DeepSeek-Coder-V2 236B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

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CodestralProfile

DeepSeek-Coder-V2 236BProfile

Multi-Model Arena Stack:

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Mistral AI

Codestral

Especialista Abierto en Código

Codestral de Mistral AI es un modelo de IA diseñado para desarrollo de software y generación de código con pesos abiertos autoalojables, con ventana de contexto de 256K, velocidad de 110 tokens/s, índice de inteligencia de 40/100 y tarifa d

Intelligence Index40 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

256K tokens

SWE-bench Verified:

28%

DeepSeek

DeepSeek-Coder-V2 236B

Modelo Base de Pesos Abiertos

DeepSeek-Coder-V2 236B de DeepSeek es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 128K, velocidad de 74 tokens/s, índice de inteligencia de

Intelligence Index42 / 100

Input Token:

$0.14 / 1M

Output Token:

$0.28 / 1M

Context Window:

128K tokens

SWE-bench Verified:

42.1%

Diferencias Clave de un Vistazo

DeepSeek-Coder-V2 236B is 1.9x cheaper for API usage (input + output tokens combined).
Codestral has a larger context window (256K vs 128K tokens).
Codestral is faster at 110 tok/s vs 74 tok/s.
DeepSeek-Coder-V2 236B leads on SWE-bench coding (42.1% vs 28.0%).

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

Característica	Codestral	DeepSeek-Coder-V2 236B
Intelligence Index Composite reasoning & knowledge score (0-100)	40	42
Provider	Mistral AI	DeepSeek
Input Price Cost per million input tokens	$0.20 / 1M	$0.14 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$0.28 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.14 / 1M	$0.07 / 1M
Context Window Maximum input + output tokens	256K tokens~384 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	110 tok/s	74 tok/s
SWE-bench Verified Real-world software engineering	28%	42.1%
GPQA Diamond Graduate-level reasoning	42%	49.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.01 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

DeepSeek-Coder-V2 236B

+14.1% SWE-bench lead

Costo

DeepSeek-Coder-V2 236B

1.9x cheaper monthly runtime

Velocidad

Codestral

+49% faster generation

Contexto

Codestral

256K tokens window

Puntuaciones de Benchmarks Verificadas

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

CodestralDeepSeek-Coder-V2 236B

SWE-bench Verified (Autonomous Coding)

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

28%vs42.1%

GPQA Diamond (Doctorate-Level Science & Math)

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

42%vs49.5%

MMLU-Pro (Multi-task Language Understanding)

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

70%vs69.8%

LMSYS Chatbot Arena Elo

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

1240ptsvs1272pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs5.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs27.8%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs29.4%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs48.6%

AIME (Advanced Math Competition)

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

N/Avs61.2%

Long-Context Reasoning

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

N/Avs66.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs74 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

Codestral0.35s

DeepSeek-Coder-V2 236B0.51s

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

Codestral4.90s

DeepSeek-Coder-V2 236B7.27s

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)

Codestral Monthly API Bill

$0.92 / month

$0.2/1M in · $0.6/1M out

DeepSeek-Coder-V2 236B Monthly API Bill

$0.48 / month

$0.14/1M in · $0.28/1M out

DeepSeek-Coder-V2 236B saves approximately $0.43 / month (47%) under this workload.1.9x Savings

Codestral · Strengths & Key Advantages

Ventana de contexto de 256K con hasta 8K tokens de salida máxima (SWE-bench Verified 28% · GPQA Diamond 42%)
Rendimiento ágil de 110 tokens/segundo con latencia inicial (TTFT) de solo 350 ms
Pesos abiertos (22B) 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

DeepSeek-Coder-V2 236B · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 42.1% · GPQA Diamond 49.5%)
Rendimiento ágil de 74 tokens/segundo con latencia inicial (TTFT) de solo 510 ms
Pesos abiertos (236B) 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	Codestral	DeepSeek-Coder-V2 236B
Cost per Task Estimated USD to finish one task	$0.20	$0.14
Output Tokens / Task Tokens generated per task	24K	30K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Codestral	DeepSeek-Coder-V2 236B
Official API Model ID (SDK)	codestral-latest	deepseek-chat
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-05	2024-06
Knowledge Cutoff	May 2024	December 2023
Architecture	Code-Optimized Transformer with 32K Window	236B Mixture-of-Experts (21B active)
Parameters	22B	236B
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 ($0.2/1M in) across full 256K window	Flat rate ($0.14/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Automatic prefix caching at $0.014/1M (no extra cache-write fee)
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) · 14 GB VRAM (4-bit Quantized) · 47 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 146 GB VRAM (4-bit Quantized) · 507 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

Codestral vs DeepSeek-Coder-V2 236B: Respuestas Rápidas (FAQ)

Which is more intelligent, Codestral or DeepSeek-Coder-V2 236B?

DeepSeek-Coder-V2 236B is more intelligent. It scores 42 compared with 40 on the composite Intelligence Index.

Which model is faster?

Codestral is faster, generating 110 tokens per second compared with 74 tokens per second.

Which model is cheaper to run?

DeepSeek-Coder-V2 236B is cheaper at $0.07 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Codestral has lower latency, with a time to first token of 0.35s compared with 0.51s.

Which model has a larger context window?

Codestral has a larger context window, supporting 256K tokens compared with 128K tokens.

Veredicto

Tanto Codestral (Ideal para desarrollo de software y generación de código con pesos abiertos autoalojables, sistemas RAG empresariales y automatización mediante API.) como DeepSeek-Coder-V2 236B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: DeepSeek-Coder-V2 236B lidera en inteligencia general y programación, Codestral ofrece mayor velocidad de generación de tokens y DeepSeek-Coder-V2 236B resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Codestral o DeepSeek-Coder-V2 236B?+

En las pruebas de inteligencia compuesta y resolución de código, DeepSeek-Coder-V2 236B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Codestral frente a DeepSeek-Coder-V2 236B?+

Codestral cuesta $0.20 / $0.60 por millón de tokens de entrada/salida, mientras que DeepSeek-Coder-V2 236B cuesta $0.14 / $0.28. En costo combinado, DeepSeek-Coder-V2 236B es la opción más económica.

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

Codestral cuenta con 256K tokens de contexto frente a 128K tokens de DeepSeek-Coder-V2 236B. En velocidad de inferencia, Codestral 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)

Codestral vs DeepSeek-Coder-V2 236B

Cambia y Compara Cualquier Modelo

Codestral

DeepSeek-Coder-V2 236B

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Codestral · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek-Coder-V2 236B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Codestral vs DeepSeek-Coder-V2 236B: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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