Mistral Small 4 vs GPT-4.1 mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Mistral Small 4 by Mistral AI and GPT-4.1 mini by OpenAI 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 4 holds a notable pricing advantage for token ingestion. On output generation, Mistral Small 4 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-4.1 mini provides an expanded memory horizon with 1,048K tokens. In latency-sensitive production environments, GPT-4.1 mini 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.
Troque e Compare Qualquer Modelo
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Mistral Small 4
Modelo de Pesos AbertosO Mistral Small 4 da Mistral AI é um modelo de inteligência artificial com janela de contexto de 256K tokens e preço de API de $0.15/1M na entrada e $0.6/1M na saída, projetado para programação, raciocínio e automação empresarial.
$0.10 / 1M
$0.30 / 1M
256K tokens
44.5%
GPT-4.1 mini
IA de Alta PerformanceO GPT-4.1 mini da OpenAI é um modelo de inteligência artificial com janela de contexto de 1.0M tokens e preço de API de $0.4/1M na entrada e $1.6/1M na saída, projetado para programação, raciocínio e automação empresarial.
$0.40 / 1M
$1.60 / 1M
1.05M tokens
36.5%
Principais Diferenças em Resumo
- Mistral Small 4 is 4.7x cheaper for API usage (input + output tokens combined).
- GPT-4.1 mini has a larger context window (1M vs 256K tokens).
- GPT-4.1 mini is faster at 165 tok/s vs 120 tok/s.
- Mistral Small 4 leads on SWE-bench coding (44.5% vs 36.5%).
| Feature | Mistral Small 4 | GPT-4.1 mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 36 |
Provider | Mistral AI | OpenAI |
Input Price Cost per million input tokens | $0.10 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $0.30 / 1M | $1.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.31 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 A4 pages of text | 1.05M tokens~1,571 A4 pages of text |
Max Output Tokens Per single request | 16K | 33K |
Throughput Speed Tokens generated per second | 120 tok/s | 165 tok/s |
SWE-bench Verified Real-world software engineering | 44.5% | 36.5% |
GPQA Diamond Graduate-level reasoning | 56% | 49.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.10 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Small 4 · Strengths & Key Advantages
- Arquitetura Next-Gen Compact Multimodal Transformer avançada da Mistral AI com contexto de 256K tokens
- Excelente relação custo-benefício ($0.15/1M entrada, $0.6/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
GPT-4.1 mini · Strengths & Key Advantages
- Arquitetura Dense multimodal transformer (small) avançada da OpenAI com contexto de 1.0M tokens
- Excelente relação custo-benefício ($0.4/1M entrada, $1.6/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Small 4 | GPT-4.1 mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.12 | $0.14 |
Output Tokens / Task Tokens generated per task | 28K | 48K |
Reasoning Tokens / Task Thinking tokens burned per task | 12K | 0K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Small 4 | GPT-4.1 mini |
|---|---|---|
| Official API Model ID (SDK) | mistral-small-4-latest | gpt-4-1-mini |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-03 | 2025-04 |
| Knowledge Cutoff | January 2026 | June 2024 |
| Architecture | Next-Gen Compact Multimodal Transformer | Dense multimodal transformer (small) |
| Parameters | 24B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 256K window | Flat rate ($0.4/1M in) across full 1048K window |
| Prompt Cache Policy & TTL | Cached input at $0.03/1M tokens | Automatic prefix caching at $0.1/1M (no extra cache-write fee) |
| 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 tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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) | 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) | OpenAI Preparedness Framework Audited & System Card Published |
| 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 Small 4 vs GPT-4.1 mini: Respostas Rápidas (FAQ)
Which is more intelligent, Mistral Small 4 or GPT-4.1 mini?
Mistral Small 4 is more intelligent. It scores 48 compared with 36 on the composite Intelligence Index.
Which model is faster?
GPT-4.1 mini is faster, generating 165 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Mistral Small 4 is cheaper at $0.07 per 1M tokens blended, compared with $0.31 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4.1 mini has lower latency, with a time to first token of 0.30s compared with 0.34s.
Which model has a larger context window?
GPT-4.1 mini has a larger context window, supporting 1.05M tokens compared with 256K tokens.
Veredito
Verdict & Model Selection Recommendation: Choose Mistral Small 4 if your priority is High-volume agent orchestration, low-cost document parsing, and edge deployments. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4.1 mini if your workflow prioritizes High-volume extraction, classification and agent routing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Perguntas Frequentes (FAQ)
How does Mistral Small 4 compare to GPT-4.1 mini in API token pricing and cost per token?+
Mistral Small 4 costs $0.10 per 1M input tokens and $0.30 per 1M output tokens, whereas GPT-4.1 mini costs $0.40 input and $1.60 output. Overall, Mistral Small 4 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 4 or GPT-4.1 mini?+
Mistral Small 4 supports a context window of 256K tokens (~192k words), while GPT-4.1 mini supports 1,048K tokens (~786k words). Therefore, GPT-4.1 mini can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Mistral Small 4 or GPT-4.1 mini?+
Mistral Small 4 scores 44.5% on SWE-bench Verified while GPT-4.1 mini scores 36.5%. For autonomous software engineering and multi-file debugging, Mistral Small 4 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Mistral Small 4 and GPT-4.1 mini?+
Mistral Small 4 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-4.1 mini focuses on standard fast inference.
How does inference speed (tokens per second) compare between Mistral Small 4 and GPT-4.1 mini?+
Mistral Small 4 generates approximately 120 tokens per second, while GPT-4.1 mini delivers 165 tokens per second. In latency-sensitive production environments, GPT-4.1 mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mistral Small 4 and GPT-4.1 mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Mistral Small 4 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4.1 mini. 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.
Fontes oficiais: GetInfoUs Independent AI Benchmarks & Official API Specs