Mistral Medium 3.5 vs DeepSeek V4.1-Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Medium 3.5 dari Mistral AI dan DeepSeek V4.1-Flash dari DeepSeek adalah dua model AI unggulan. Mistral Medium 3.5 menawarkan jendela konteks 262.1K token dengan harga $0.40/$2.00 per 1 juta token, sedangkan DeepSeek V4.1-Flash menyediakan konteks 1M token seharga $0.30/$1.20 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
Ganti & Bandingkan Model Apa Saja
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Mistral Medium 3.5
Model Dasar Bobot TerbukaMistral Medium 3.5 dari Mistral AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 262K, kecepatan 90 token/detik, indeks kecerdasan 55/100, serta tar
$0.40 / 1M
$2.00 / 1M
262.1K tokens
54%
DeepSeek V4.1-Flash
Model Dasar Bobot TerbukaDeepSeek V4.1-Flash dari DeepSeek adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 1M, kecepatan 110 token/detik, indeks kecerdasan 58/100, serta tarif
$0.30 / 1M
$1.20 / 1M
1M tokens
74.2%
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Medium 3.5 · Strengths & Key Advantages
- Jendela konteks 262K dengan kapasitas output maksimum hingga 33K token (SWE-bench Verified 54% · GPQA Diamond 68%)
- Kecepatan generasi tinggi 90 token/detik dengan latensi token pertama (TTFT) hanya 450 ms
- Bobot terbuka (Confidential (~80B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
DeepSeek V4.1-Flash · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 384K token (SWE-bench Verified 74.2% · GPQA Diamond 90.9%)
- Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 460 ms
- Bobot terbuka (763B total (8B prefill / 16B decode )) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik Mistral Medium 3.5 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun DeepSeek V4.1-Flash (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: DeepSeek V4.1-Flash unggul dalam penalaran dan pemrograman, DeepSeek V4.1-Flash memberikan kecepatan generasi token lebih tinggi, dan DeepSeek V4.1-Flash lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Mistral Medium 3.5 atau DeepSeek V4.1-Flash?+
Dalam indeks kecerdasan gabungan dan benchmark coding, DeepSeek V4.1-Flash memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Mistral Medium 3.5 vs DeepSeek V4.1-Flash?+
Mistral Medium 3.5 mengenakan biaya $0.40 / $2.00 per 1M token input/output, sedangkan DeepSeek V4.1-Flash sebesar $0.30 / $1.20. Secara keseluruhan, DeepSeek V4.1-Flash lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Mistral Medium 3.5 mendukung konteks 262.1K token dibandingkan 1M token pada DeepSeek V4.1-Flash, sementara DeepSeek V4.1-Flash unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- DeepSeek V4.1-Flash vs Gemini 4 Argon: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- DeepSeek V4.1-Flash vs GLM-5.2: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- GPT-6 Luna vs DeepSeek V4.1-Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Grok 4.7 vs DeepSeek V4.1-Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Mistral Large 2 vs Mistral Medium 3.5: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Mistral Medium 3.5 vs Claude Sonnet 5.5: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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.
Sumber resmi: Dokumentasi API resmi Mistral AI & DeepSeek serta evaluasi benchmark independen (2026).