Mistral Small 3.2 vs Mistral Medium 3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Small 3.2 by Mistral AI and Mistral Medium 3 by Mistral AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Mistral Small 3.2 holds a notable pricing advantage for token ingestion. On output generation, Mistral Small 3.2 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Mistral Medium 3 provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Mistral Small 3.2 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Mistral Small 3.2
Открытые веса (Open Weights)Mistral Small 3.2 от Mistral AI — передовая модель искусственного интеллекта с контекстным окном 128K токенов и стоимостью API $0.1/1M входящих и $0.3/1M исходящих токенов для программирования, аналитики и автоматизации.
$0.10 / 1M
$0.30 / 1M
128K tokens
34%
Mistral Medium 3
Открытые веса (Open Weights)Mistral Medium 3 от Mistral AI — передовая модель искусственного интеллекта с контекстным окном 131K токенов и стоимостью API $0.4/1M входящих и $2/1M исходящих токенов для программирования, аналитики и автоматизации.
$0.40 / 1M
$2.00 / 1M
131.1K tokens
40.5%
Ключевые отличия кратко
- Mistral Small 3.2 is 5.2x cheaper for API usage (input + output tokens combined).
- Mistral Medium 3 has a larger context window (131K vs 128K tokens).
- Mistral Small 3.2 is faster at 100 tok/s vs 85 tok/s.
- Mistral Medium 3 leads on SWE-bench coding (40.5% vs 34.0%).
| Feature | Mistral Small 3.2 | Mistral Medium 3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 43 | 47 |
Provider | Mistral AI | Mistral AI |
Input Price Cost per million input tokens | $0.10 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $0.30 / 1M | $2.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.09 / 1M | $0.35 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 100 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 34% | 40.5% |
GPQA Diamond Graduate-level reasoning | 48.5% | 57.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.10 / 1M |
Кто лидирует в каждой категории?
At-a-glance decision frameworkРезультаты проверенных бенчмарков
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.
Задержка и скорость ответа
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Интерактивный калькулятор стоимости токенов
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Small 3.2 · Strengths & Key Advantages
- Современная архитектура Compact Vision-Language Transformer от Mistral AI с поддержкой до 128K токенов контекста
- Оптимальная стоимость использования ($0.1/1M вход, $0.3/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
Mistral Medium 3 · Strengths & Key Advantages
- Современная архитектура Enterprise Multimodal Transformer от Mistral AI с поддержкой до 131K токенов контекста
- Оптимальная стоимость использования ($0.4/1M вход, $2/1M выход) и высокая скорость генерации
- Высокие результаты в задачах написания кода, логического мышления и многоязычной обработки
Considerations & Trade-offs
- При работе с максимальным контекстом требуется контроль расхода токенов
- Для узкоспециализированных задач рекомендуется точная настройка системных промптов
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Small 3.2 | Mistral Medium 3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $0.35 |
Output Tokens / Task Tokens generated per task | 25K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Технические характеристики
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Small 3.2 | Mistral Medium 3 |
|---|---|---|
| Official API Model ID (SDK) | mistral-small-3-2-latest | mistral-medium-3-latest |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-06 | 2025-05 |
| Knowledge Cutoff | April 2025 | March 2025 |
| Architecture | Compact Vision-Language Transformer | Enterprise Multimodal Transformer |
| Parameters | 24B | Confidential (~70B) |
| 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 ($0.1/1M in) across full 128K window | Flat rate ($0.4/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M 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) · 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) · 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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 Small 3.2 vs Mistral Medium 3: Быстрые ответы (FAQ)
Which is more intelligent, Mistral Small 3.2 or Mistral Medium 3?
Mistral Medium 3 is more intelligent. It scores 47 compared with 43 on the composite Intelligence Index.
Which model is faster?
Mistral Small 3.2 is faster, generating 100 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
Mistral Small 3.2 is cheaper at $0.09 per 1M tokens blended, compared with $0.35 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Mistral Small 3.2 has lower latency, with a time to first token of 0.40s compared with 0.48s.
Which model has a larger context window?
Mistral Medium 3 has a larger context window, supporting 131.1K tokens compared with 128K tokens.
Вердикт
Verdict & Model Selection Recommendation: Choose Mistral Small 3.2 if your priority is High-volume multimodal triage, edge image-text extraction, and responsive chat assistants. Its performance in demanding tasks justifies its operational token cost. Choose Mistral Medium 3 if your workflow prioritizes Enterprise internal assistants, document workflow automation, and multimodal analytics. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Часто задаваемые вопросы (FAQ)
How does Mistral Small 3.2 compare to Mistral Medium 3 in API token pricing and cost per token?+
Mistral Small 3.2 costs $0.10 per 1M input tokens and $0.30 per 1M output tokens, whereas Mistral Medium 3 costs $0.40 input and $2.00 output. Overall, Mistral Small 3.2 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Mistral Small 3.2 or Mistral Medium 3?+
Mistral Small 3.2 supports a context window of 128K tokens (~96k words), while Mistral Medium 3 supports 131K tokens (~98k words). Therefore, Mistral Medium 3 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Mistral Small 3.2 or Mistral Medium 3?+
Mistral Small 3.2 scores 34% on SWE-bench Verified while Mistral Medium 3 scores 40.5%. For autonomous software engineering and multi-file debugging, Mistral Medium 3 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Mistral Small 3.2 and Mistral Medium 3?+
Mistral Small 3.2 is optimized for low-latency direct generation. In contrast, Mistral Medium 3 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Mistral Small 3.2 and Mistral Medium 3?+
Mistral Small 3.2 generates approximately 100 tokens per second, while Mistral Medium 3 delivers 85 tokens per second. In latency-sensitive production environments, Mistral Small 3.2 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mistral Small 3.2 and Mistral Medium 3 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Mistral Small 3.2 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Mistral Medium 3. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
Официальные источники: GetInfoUs Independent AI Benchmarks & Official API Specs