Command A Translate vs Mistral Small 3.2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Command A Translate dari Cohere dan Mistral Small 3.2 dari Mistral AI adalah dua model AI unggulan. Command A Translate menawarkan jendela konteks 8.2K token dengan harga $0.20/$0.40 per 1 juta token, sedangkan Mistral Small 3.2 menyediakan konteks 128K token seharga $0.10/$0.30 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Command A Translate
Model Ringan & Hemat BiayaCommand A Translate dari Cohere adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 8K, kecepatan 130 token/detik, indeks kecerdasan 40/100, serta tar
$0.20 / 1M
$0.40 / 1M
8.2K tokens
N/A
Mistral Small 3.2
Model Dasar Bobot TerbukaMistral Small 3.2 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 100 token/detik, indeks kecerdasan 43/100, serta tar
$0.10 / 1M
$0.30 / 1M
128K tokens
34%
Perbedaan Utama Sekilas
- Mistral Small 3.2 is 1.5x cheaper for API usage (input + output tokens combined).
- Mistral Small 3.2 has a larger context window (128K vs 8K tokens).
- Command A Translate is faster at 130 tok/s vs 100 tok/s.
| Feature | Command A Translate | Mistral Small 3.2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 40 | 43 |
Provider | Cohere | Mistral AI |
Input Price Cost per million input tokens | $0.20 / 1M | $0.10 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $0.30 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.11 / 1M | $0.09 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 130 tok/s | 100 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 34% |
GPQA Diamond Graduate-level reasoning | N/A | 48.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.05 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor 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.
Command A Translate · Strengths & Key Advantages
- Jendela konteks 8K dengan kapasitas output maksimum hingga 4K token (MMLU-Pro 68% · Arena Elo 1235)
- Kecepatan generasi tinggi 130 token/detik dengan latensi token pertama (TTFT) hanya 320 ms
- Bobot terbuka (Confidential (~14B)) 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
Mistral Small 3.2 · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 34% · GPQA Diamond 48.5%)
- Kecepatan generasi tinggi 100 token/detik dengan latensi token pertama (TTFT) hanya 400 ms
- Bobot terbuka (24B) 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 | Command A Translate | Mistral Small 3.2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.04 | $0.09 |
Output Tokens / Task Tokens generated per task | 15K | 25K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Command A Translate | Mistral Small 3.2 |
|---|---|---|
| Official API Model ID (SDK) | command-a-translate | mistral-small-3-2-latest |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-08 | 2025-06 |
| Knowledge Cutoff | June 2025 | April 2025 |
| Architecture | Specialized Enterprise Neural Translation Transformer | Compact Vision-Language Transformer |
| Parameters | Confidential (~14B) | 24B |
| 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 ($0.2/1M in) across full 8K window | Flat rate ($0.1/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Cached input at $0.05/1M 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) · 9 GB VRAM (4-bit Quantized) · 30 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 15 GB VRAM (4-bit Quantized) · 52 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 |
Command A Translate vs Mistral Small 3.2: Jawaban Cepat (FAQ)
Which is more intelligent, Command A Translate or Mistral Small 3.2?
Mistral Small 3.2 is more intelligent. It scores 43 compared with 40 on the composite Intelligence Index.
Which model is faster?
Command A Translate is faster, generating 130 tokens per second compared with 100 tokens per second.
Which model is cheaper to run?
Mistral Small 3.2 is cheaper at $0.09 per 1M tokens blended, compared with $0.11 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Command A Translate has lower latency, with a time to first token of 0.32s compared with 0.40s.
Which model has a larger context window?
Mistral Small 3.2 has a larger context window, supporting 128K tokens compared with 8.2K tokens.
Kesimpulan
Baik Command A Translate (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Mistral Small 3.2 (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 Small 3.2 unggul dalam penalaran dan pemrograman, Command A Translate memberikan kecepatan generasi token lebih tinggi, dan Mistral Small 3.2 lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Command A Translate atau Mistral Small 3.2?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Mistral Small 3.2 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Command A Translate vs Mistral Small 3.2?+
Command A Translate mengenakan biaya $0.20 / $0.40 per 1M token input/output, sedangkan Mistral Small 3.2 sebesar $0.10 / $0.30. Secara keseluruhan, Mistral Small 3.2 lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Command A Translate mendukung konteks 8.2K token dibandingkan 128K token pada Mistral Small 3.2, sementara Command A Translate 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 Cohere & Mistral AI serta evaluasi benchmark independen (2026).