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.
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Mistral Medium 3.5
Equilibrado empresarialde frontera empresarial generalist que ofrece 262K contexto, multimodal visión, hybrid razonamiento, y de pesos abiertos bajo attractive $0.40/$2.00 precios.
$0.40 / 1M
$2.00 / 1M
262.1K tokens
54%
DeepSeek V4.1-Flash
Asesino de caché KV763B total / 16B activo causal encoder-decoder MoE con 1/4 KV-cache usage, nativo visión, MIT license, y mundo-record bajo off-pico precios.
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
Diferencias Clave de un Vistazo
- 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%).
| Característica | 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 |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Medium 3.5 · Strengths & Key Advantages
- Expansive 262K contexto capacidad con near-zero needle recuperación degradation
- Highly attractive $0.40 en / $2.00 out precios para un empresarial insignia
- de pesos abiertos availability con nativo visual y razonamiento profundidad
Considerations & Trade-offs
- Trails propietario multi-billones parámetro giants en bleeding-perimetral codificación
- requiere dedicado empresarial hardware para completo local implementación
DeepSeek V4.1-Flash · Strengths & Key Advantages
- MIT permissive abierto license con completo weights, arquitectura, y code
- Revolutionary causal encoder-decoder arquitectura: KV cache solo 890 bytes/token (se ejecuta en commodity NVMe SSDs)
- de mundo más bajo precios: off-pico cached entrada un just $0.003/1M y $0.27 costo por benchmark tarea
- Colossal 1M contexto entrada paired con 384,000 maximum salida tokens y nativo visión
Considerations & Trade-offs
- Extremely verbose generación (averages 8.9K salida tokens por inquiry sin explicit caps)
- de largo alcance rendimiento degrades en Terminal-Bench 4.0 (31.2%)
- Factual knowledge recall dips en SimpleQA (42.3) y visión resolución es capped un 672x672
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas Rápidas (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.
Veredicto
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.
Preguntas frecuentes
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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs