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Head-to-Head Model Comparison

Mistral Medium 3.5 vs DeepSeek V4.1-Flash

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Mistral Medium 3.5 vs DeepSeek V4.1-Flash: Benchmarks, Pricing & Speed Comparison

Mistral AI tarafından geliştirilen Mistral Medium 3.5 ve DeepSeek tarafından sunulan DeepSeek V4.1-Flash, kendi sınıflarının öne çıkan yapay zeka modelleridir. Mistral Medium 3.5, 262.1K token bağlam penceresi ve 1M token başına $0.40/$2.00 fiyat sunarken; DeepSeek V4.1-Flash, 1M token bağlam penceresi ve $0.30/$1.20 fiyatla gelir. Aşağıda tüm benchmark, hız ve maliyet detaylarını inceleyebilirsiniz.

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Mistral AI

Mistral Medium 3.5

Açık Ağırlıklı Temel Model

Mistral AI tarafından geliştirilen Mistral Medium 3.5; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 262K bağlam penceresi, 90 token/sn hız, 55/100 zeka indeksi ve 1M token baş

Intelligence Index55 / 100
Input Token:

$0.40 / 1M

Output Token:

$2.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

54%

DeepSeek

DeepSeek V4.1-Flash

Açık Ağırlıklı Temel Model

DeepSeek tarafından geliştirilen DeepSeek V4.1-Flash; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 1M bağlam penceresi, 110 token/sn hız, 58/100 zeka indeksi ve 1M token başın

Intelligence Index58 / 100
Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

74.2%

Bir Bakışta Temel Farklar

  • DeepSeek V4.1-Flash is 1.7x cheaper for API usage (input + output tokens combined).
  • DeepSeek V4.1-Flash has a larger context window (1M vs 262K tokens).
  • DeepSeek V4.1-Flash is faster at 110 tok/s vs 90 tok/s.
  • DeepSeek V4.1-Flash leads on SWE-bench coding (74.2% vs 54.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMistral Medium 3.5DeepSeek V4.1-Flash
Intelligence Index
Composite reasoning & knowledge score (0-100)
5558
Provider
Mistral AIDeepSeek
Input Price
Cost per million input tokens
$0.40 / 1M$0.30 / 1M
Output Price
Cost per million output tokens
$2.00 / 1M$1.20 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.35 / 1M$0.18 / 1M
Context Window
Maximum input + output tokens
262.1K tokens~393 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
33K384K
Throughput Speed
Tokens generated per second
90 tok/s110 tok/s
SWE-bench Verified
Real-world software engineering
54%74.2%
GPQA Diamond
Graduate-level reasoning
68%90.9%
Cached Input Price
Prompt caching discount per 1M tokens
$0.10 / 1M$0.01 / 1M

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework
Coding
DeepSeek V4.1-Flash
+20.2% SWE-bench lead
Cost
DeepSeek V4.1-Flash
1.7x cheaper monthly runtime
Speed
DeepSeek V4.1-Flash
+22% faster generation
Context
DeepSeek V4.1-Flash
1M tokens window

Doğrulanmış Benchmark Puanları

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

Mistral Medium 3.5DeepSeek V4.1-Flash
SWE-bench Verified (Autonomous Coding)

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

54%vs74.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

68%vs90.9%
MMLU-Pro (Multi-task Language Understanding)

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

84.5%vs89.5%
LMSYS Chatbot Arena Elo

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

1370ptsvs1455pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs16.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

46%vs31.2%
SciCode (Scientific Computing)

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

N/Avs48%
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%vs92.4%
Long-Context Reasoning

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

85%vs93%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

90 tok/s vs110 tok/s

Gecikme Süresi ve Yanıt Hızı

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
Mistral Medium 3.50.45s
DeepSeek V4.1-Flash0.46s
Est. End-to-End Response (500 tokens)TTFT + generation time
Mistral Medium 3.56.01s
DeepSeek V4.1-Flash5.01s

Gerçek Zamanlı Token Maliyet Simülatörü

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Mistral Medium 3.5 Monthly API Bill
$2.55 / month
$0.4/1M in · $2/1M out
DeepSeek V4.1-Flash Monthly API Bill
$1.54 / month
$0.3/1M in · $1.2/1M out
DeepSeek V4.1-Flash saves approximately $1.01 / month (40%) under this workload.1.7x Savings

Mistral Medium 3.5 · Strengths & Key Advantages

  • 262K bağlam penceresi ve yanıt başına maksimum 33K çıktı tokenı kapasitesi (SWE-bench Verified 54% · GPQA Diamond 68%)
  • Sadece 450 ms ilk token gecikmesi (TTFT) ile saniyede 90 token yüksek üretim hızı
  • vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (Confidential (~80B MoE))
Considerations & Trade-offs
  • Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
  • Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

DeepSeek V4.1-Flash · Strengths & Key Advantages

  • 1M bağlam penceresi ve yanıt başına maksimum 384K çıktı tokenı kapasitesi (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
  • Sadece 460 ms ilk token gecikmesi (TTFT) ile saniyede 110 token yüksek üretim hızı
  • vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (763B total (8B prefill / 16B decode ))
Considerations & Trade-offs
  • Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
  • Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Token Use & Task Economics

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

MetricMistral Medium 3.5DeepSeek V4.1-Flash
Cost per Task
Estimated USD to finish one task
$0.38$0.27
Output Tokens / Task
Tokens generated per task
38K58K
Reasoning Tokens / Task
Thinking tokens burned per task
18K28K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

FeatureMistral Medium 3.5DeepSeek V4.1-Flash
Official API Model ID (SDK)mistral-medium-3-5-latestdeepseek-reasoner
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-042026-09
Knowledge CutoffFebruary 2026June 2026
ArchitectureEnterprise Hybrid Reasoning MoECausal Encoder-Decoder (20 enc + 20 dec layers)
ParametersConfidential (~80B MoE)763B total (8B prefill / 16B decode active)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.4/1M in) across full 262K windowFlat rate ($0.3/1M in) across full 1000K window
Prompt Cache Policy & TTLCached input at $0.1/1M tokensAutomatic prefix caching at $0.006/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 50 GB VRAM (4-bit Quantized) · 172 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 473 GB VRAM (4-bit Quantized) · 1640 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Mistral Medium 3.5 vs DeepSeek V4.1-Flash: Hızlı Yanıtlar (SSS)

Which is more intelligent, Mistral Medium 3.5 or DeepSeek V4.1-Flash?

DeepSeek V4.1-Flash is more intelligent. It scores 58 compared with 55 on the composite Intelligence Index.

Which model is faster?

DeepSeek V4.1-Flash is faster, generating 110 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

DeepSeek V4.1-Flash is cheaper at $0.18 per 1M tokens blended, compared with $0.35 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Medium 3.5 has lower latency, with a time to first token of 0.45s compared with 0.46s.

Which model has a larger context window?

DeepSeek V4.1-Flash has a larger context window, supporting 1M tokens compared with 262.1K tokens.

Sonuç

Hem Mistral Medium 3.5 (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de DeepSeek V4.1-Flash (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: DeepSeek V4.1-Flash karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, DeepSeek V4.1-Flash daha yüksek üretim hızı sağlar; DeepSeek V4.1-Flash ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Mistral Medium 3.5 mi yoksa DeepSeek V4.1-Flash mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde DeepSeek V4.1-Flash önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Mistral Medium 3.5 ve DeepSeek V4.1-Flash API fiyatları nasıl karşılaştırılır?+

Mistral Medium 3.5 1M giriş/çıkış tokeni başına $0.40 / $2.00 ücretlendirilirken, DeepSeek V4.1-Flash $0.30 / $1.20 seviyesindedir. Karma maliyette DeepSeek V4.1-Flash daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Mistral Medium 3.5 262.1K token bağlam penceresi sunarken DeepSeek V4.1-Flash 1M token kapasiteye sahiptir. Saniyedeki token üretim hızında ise DeepSeek V4.1-Flash öndedir.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Resmi kaynaklar: Mistral AI ve DeepSeek resmi API dokümantasyonu, sistem kartları ve bağımsız benchmark testleri (2026).