Mistral Large 2 vs Mistral Medium 3.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Large 2 dari Mistral AI dan Mistral Medium 3.5 dari Mistral AI adalah dua model AI unggulan. Mistral Large 2 menawarkan jendela konteks 128K token dengan harga $2.00/$6.00 per 1 juta token, sedangkan Mistral Medium 3.5 menyediakan konteks 262.1K token seharga $0.40/$2.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Mistral Large 2
Model Dasar Bobot TerbukaMistral Large 2 dari Mistral AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 75 token/detik, indeks kecerdasan 46/100, serta tarif
$2.00 / 1M
$6.00 / 1M
128K tokens
38%
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%
Perbedaan Utama Sekilas
- Mistral Medium 3.5 is 3.9x cheaper for API usage (input + output tokens combined).
- Mistral Medium 3.5 has a larger context window (262K vs 128K tokens).
- Mistral Medium 3.5 is faster at 90 tok/s vs 75 tok/s.
- Mistral Medium 3.5 leads on SWE-bench coding (54.0% vs 38.0%).
| Feature | Mistral Large 2 | Mistral Medium 3.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 55 |
Provider | Mistral AI | Mistral AI |
Input Price Cost per million input tokens | $2.00 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.70 / 1M | $0.35 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 8K | 33K |
Throughput Speed Tokens generated per second | 75 tok/s | 90 tok/s |
SWE-bench Verified Real-world software engineering | 38% | 54% |
GPQA Diamond Graduate-level reasoning | 54% | 68% |
Cached Input Price Prompt caching discount per 1M tokens | $1.00 / 1M | $0.10 / 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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
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 Large 2 · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 38% · GPQA Diamond 54%)
- Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 520 ms
- Bobot terbuka (123B) 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
- Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya
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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Large 2 | Mistral Medium 3.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.38 |
Output Tokens / Task Tokens generated per task | 24K | 38K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 18K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Large 2 | Mistral Medium 3.5 |
|---|---|---|
| Official API Model ID (SDK) | mistral-large-2-latest | mistral-medium-3-5-latest |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-07 | 2026-04 |
| Knowledge Cutoff | July 2024 | February 2026 |
| Architecture | 123B Dense Frontier Transformer | Enterprise Hybrid Reasoning MoE |
| Parameters | 123B | Confidential (~80B MoE) |
| 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 ($2/1M in) across full 128K window | Flat rate ($0.4/1M in) across full 262K window |
| Prompt Cache Policy & TTL | Cached input at $1/1M tokens | Cached input at $0.1/1M tokens |
| 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) | 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) · 76 GB VRAM (4-bit Quantized) · 264 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 50 GB VRAM (4-bit Quantized) · 172 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 | text, vision |
| Output Modalities | text | text |
Mistral Large 2 vs Mistral Medium 3.5: Jawaban Cepat (FAQ)
Which is more intelligent, Mistral Large 2 or Mistral Medium 3.5?
Mistral Medium 3.5 is more intelligent. It scores 55 compared with 46 on the composite Intelligence Index.
Which model is faster?
Mistral Medium 3.5 is faster, generating 90 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Mistral Medium 3.5 is cheaper at $0.35 per 1M tokens blended, compared with $1.70 (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 0.52s.
Which model has a larger context window?
Mistral Medium 3.5 has a larger context window, supporting 262.1K tokens compared with 128K tokens.
Kesimpulan
Baik Mistral Large 2 (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun 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.) sangat tangguh: Mistral Medium 3.5 unggul dalam penalaran dan pemrograman, Mistral Medium 3.5 memberikan kecepatan generasi token lebih tinggi, dan Mistral Medium 3.5 lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Mistral Large 2 atau Mistral Medium 3.5?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Mistral Medium 3.5 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Mistral Large 2 vs Mistral Medium 3.5?+
Mistral Large 2 mengenakan biaya $2.00 / $6.00 per 1M token input/output, sedangkan Mistral Medium 3.5 sebesar $0.40 / $2.00. Secara keseluruhan, Mistral Medium 3.5 lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Mistral Large 2 mendukung konteks 128K token dibandingkan 262.1K token pada Mistral Medium 3.5, sementara Mistral Medium 3.5 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
- Mistral Large 2 vs GPT-4o: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Mistral Large 2 vs Llama 3.3 70B: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Mistral Medium 3.5 vs Claude Sonnet 5.5: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Mistral Medium 3.5 vs DeepSeek V4.1-Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Mistral Medium 3.5 vs GPT-5.5: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Pixtral 12B vs Mistral Large 2: 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 & Mistral AI serta evaluasi benchmark independen (2026).