Mistral Medium 3.5 vs Claude Sonnet 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Medium 3.5 dari Mistral AI dan Claude Sonnet 5.5 dari Anthropic 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 Claude Sonnet 5.5 menyediakan konteks 1M token seharga $2.00/$10.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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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%
Claude Sonnet 5.5
Model Unggulan Garis DepanClaude Sonnet 5.5 dari Anthropic adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 1M, kecepatan 105 token/detik, indeks kecerdasan 78/100, serta
$2.00 / 1M
$10.00 / 1M
1M tokens
82.9%
Perbedaan Utama Sekilas
- Mistral Medium 3.5 is 4.8x cheaper for API usage (input + output tokens combined).
- Claude Sonnet 5.5 has a larger context window (1M vs 262K tokens).
- Claude Sonnet 5.5 is faster at 105 tok/s vs 90 tok/s.
- Claude Sonnet 5.5 leads on SWE-bench coding (82.9% vs 54.0%).
| Feature | Mistral Medium 3.5 | Claude Sonnet 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 55 | 78 |
Provider | Mistral AI | Anthropic |
Input Price Cost per million input tokens | $0.40 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.35 / 1M | $1.54 / 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 | 128K |
Throughput Speed Tokens generated per second | 90 tok/s | 105 tok/s |
SWE-bench Verified Real-world software engineering | 54% | 82.9% |
GPQA Diamond Graduate-level reasoning | 68% | 79.4% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.20 / 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 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
Claude Sonnet 5.5 · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 82.9% · GPQA Diamond 79.4%)
- Kecepatan generasi tinggi 105 token/detik dengan latensi token pertama (TTFT) hanya 550 ms
- Skema harga API kompetitif (tarif $2/$10 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Medium 3.5 | Claude Sonnet 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.38 | $3.29 |
Output Tokens / Task Tokens generated per task | 38K | 72K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 37K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Medium 3.5 | Claude Sonnet 5.5 |
|---|---|---|
| Official API Model ID (SDK) | mistral-medium-3-5-latest | claude-sonnet-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-04 | 2026-09 |
| Knowledge Cutoff | February 2026 | June 2026 |
| Architecture | Enterprise Hybrid Reasoning MoE | Adaptive Frontier Multimodal Transformer |
| Parameters | Confidential (~80B MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.4/1M in) across full 262K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.1/1M tokens | $0.2/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Mistral Medium 3.5 vs Claude Sonnet 5.5: Jawaban Cepat (FAQ)
Which is more intelligent, Mistral Medium 3.5 or Claude Sonnet 5.5?
Claude Sonnet 5.5 is more intelligent. It scores 78 compared with 55 on the composite Intelligence Index.
Which model is faster?
Claude Sonnet 5.5 is faster, generating 105 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
Mistral Medium 3.5 is cheaper at $0.35 per 1M tokens blended, compared with $1.54 (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 0.55s.
Which model has a larger context window?
Claude Sonnet 5.5 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 Claude Sonnet 5.5 (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Claude Sonnet 5.5 unggul dalam penalaran dan pemrograman, Claude Sonnet 5.5 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 Medium 3.5 atau Claude Sonnet 5.5?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Claude Sonnet 5.5 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Mistral Medium 3.5 vs Claude Sonnet 5.5?+
Mistral Medium 3.5 mengenakan biaya $0.40 / $2.00 per 1M token input/output, sedangkan Claude Sonnet 5.5 sebesar $2.00 / $10.00. Secara keseluruhan, Mistral Medium 3.5 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 Claude Sonnet 5.5, sementara Claude Sonnet 5.5 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
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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.
Sumber resmi: Dokumentasi API resmi Mistral AI & Anthropic serta evaluasi benchmark independen (2026).