Mistral Medium 3.5 vs DeepSeek V4.1-Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Mistral Medium 3.5 by Mistral AI and DeepSeek V4.1-Flash by DeepSeek 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, DeepSeek V4.1-Flash holds a notable pricing advantage for token ingestion. On output generation, DeepSeek V4.1-Flash is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, DeepSeek V4.1-Flash provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, DeepSeek V4.1-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.
بدّل وقارن بين أي نموذجين
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Mistral Medium 3.5
نموذج مفتوح الأوزاننموذج Mistral Medium 3.5 المقدم من Mistral AI يوفر نافذة سياق تصل إلى 262K رمز، بتكلفة $0.4 لكل مليون رمز إدخال و$2 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.40 / 1M
$2.00 / 1M
262.1K tokens
54%
DeepSeek V4.1-Flash
نموذج مفتوح الأوزاننموذج DeepSeek V4.1-Flash المقدم من DeepSeek يوفر نافذة سياق تصل إلى 1M رمز، بتكلفة $0.3 لكل مليون رمز إدخال و$1.2 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
أهم الفروقات في لمحة
- 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%).
| Feature | Mistral Medium 3.5 | DeepSeek V4.1-Flash |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 58 |
Provider | Mistral AI | DeepSeek |
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 text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 384K |
Throughput Speed Tokens generated per second | 90 tok/s | 110 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 |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج الاختبارات المعيارية الموثقة
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Medium 3.5 · Strengths & Key Advantages
- بنية Enterprise Hybrid Reasoning MoE متطورة من Mistral AI مع سعة سياق تبلغ 262K رمز
- كفاءة عالية في التكلفة ($0.4/1M إدخال و$2/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
DeepSeek V4.1-Flash · Strengths & Key Advantages
- بنية Causal Encoder-Decoder (20 enc + 20 dec layers) متطورة من DeepSeek مع سعة سياق تبلغ 1M رمز
- كفاءة عالية في التكلفة ($0.3/1M إدخال و$1.2/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Medium 3.5 | DeepSeek V4.1-Flash |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.38 | $0.27 |
Output Tokens / Task Tokens generated per task | 38K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 28K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Medium 3.5 | DeepSeek V4.1-Flash |
|---|---|---|
| Official API Model ID (SDK) | mistral-medium-3-5-latest | deepseek-reasoner |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | February 2026 | June 2026 |
| Architecture | Enterprise Hybrid Reasoning MoE | Causal Encoder-Decoder (20 enc + 20 dec layers) |
| Parameters | Confidential (~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 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 ($0.4/1M in) across full 262K window | Flat rate ($0.3/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | Cached input at $0.1/1M tokens | Automatic prefix caching at $0.006/1M (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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / 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 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, vision | text, vision |
| Output Modalities | text | text |
Mistral Medium 3.5 vs DeepSeek V4.1-Flash: إجابات سريعة (FAQ)
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.
الحكم النهائي
Verdict & Model Selection Recommendation: Choose Mistral Medium 3.5 if your priority is Enterprise intelligence hubs, sovereign multilingual workflows, and multi-turn analytical agents. Its performance in demanding tasks justifies its operational token cost. Choose DeepSeek V4.1-Flash if your workflow prioritizes Extreme budget batch processing, self-hosted commodity hardware clusters, and massive long-form reasoning tasks. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
الأسئلة الشائعة (FAQ)
How does Mistral Medium 3.5 compare to DeepSeek V4.1-Flash in API token pricing and cost per token?+
Mistral Medium 3.5 costs $0.40 per 1M input tokens and $2.00 per 1M output tokens, whereas DeepSeek V4.1-Flash costs $0.30 input and $1.20 output. Overall, DeepSeek V4.1-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: Mistral Medium 3.5 or DeepSeek V4.1-Flash?+
Mistral Medium 3.5 supports a context window of 262K tokens (~197k words), while DeepSeek V4.1-Flash supports 1,000K tokens (~750k words). Therefore, DeepSeek V4.1-Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Mistral Medium 3.5 or DeepSeek V4.1-Flash?+
Mistral Medium 3.5 scores 54% on SWE-bench Verified while DeepSeek V4.1-Flash scores 74.2%. For autonomous software engineering and multi-file debugging, DeepSeek V4.1-Flash holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Mistral Medium 3.5 and DeepSeek V4.1-Flash?+
Mistral Medium 3.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, DeepSeek V4.1-Flash provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Mistral Medium 3.5 and DeepSeek V4.1-Flash?+
Mistral Medium 3.5 generates approximately 90 tokens per second, while DeepSeek V4.1-Flash delivers 110 tokens per second. In latency-sensitive production environments, DeepSeek V4.1-Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mistral Medium 3.5 and DeepSeek V4.1-Flash to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to DeepSeek V4.1-Flash to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Mistral Medium 3.5. 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.
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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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs