Mistral Medium 3.5 vs DeepSeek V4.1-Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Medium 3.5 di Mistral AI e DeepSeek V4.1-Flash di DeepSeek sono due modelli di IA di riferimento. Mistral Medium 3.5 offre una finestra di contesto di 262.1K token a $0.40/$2.00 per milione di token, mentre DeepSeek V4.1-Flash mette a disposizione 1M token di contesto a $0.30/$1.20 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
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Mistral Medium 3.5
Modello Base a Pesi ApertiMistral Medium 3.5 di Mistral AI è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 262K, velocità di 90 token/s, indice di intelligenza di
$0.40 / 1M
$2.00 / 1M
262.1K tokens
54%
DeepSeek V4.1-Flash
Modello Base a Pesi ApertiDeepSeek V4.1-Flash di DeepSeek è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 1M, velocità di 110 token/s, indice di intelligenza di 58
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
Differenze Chiave a Colpo d’Occhio
- 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 |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Medium 3.5 · Strengths & Key Advantages
- Finestra di contesto da 262K con capacità massima di output fino a 33K token (SWE-bench Verified 54% · GPQA Diamond 68%)
- Throughput elevato di 90 token/secondo con latenza al primo token (TTFT) di soli 450 ms
- Pesi aperti (Confidential (~80B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
DeepSeek V4.1-Flash · Strengths & Key Advantages
- Finestra di contesto da 1M con capacità massima di output fino a 384K token (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
- Throughput elevato di 110 token/secondo con latenza al primo token (TTFT) di soli 460 ms
- Pesi aperti (763B total (8B prefill / 16B decode )) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
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 |
Specifiche Tecniche
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: Risposte Rapide (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.
Verdetto
Sia Mistral Medium 3.5 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia DeepSeek V4.1-Flash (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: DeepSeek V4.1-Flash guida nei benchmark di ragionamento e programmazione, DeepSeek V4.1-Flash garantisce una generazione più rapida e DeepSeek V4.1-Flash offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Mistral Medium 3.5 o DeepSeek V4.1-Flash?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale DeepSeek V4.1-Flash. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Mistral Medium 3.5 e DeepSeek V4.1-Flash?+
Mistral Medium 3.5 costa $0.40 / $2.00 per milione di token di input/output, mentre DeepSeek V4.1-Flash costa $0.30 / $1.20. Nel costo combinato, DeepSeek V4.1-Flash risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Mistral Medium 3.5 supporta 262.1K token di contesto contro 1M token di DeepSeek V4.1-Flash, mentre DeepSeek V4.1-Flash offre una velocità di generazione superiore.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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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.
Fonti ufficiali: Documentazione API ufficiale di Mistral AI e DeepSeek, schede di sistema e benchmark indipendenti (2026).