Head-to-Head Model Comparison

Mistral Saba vs GPT-4.1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

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Mistral SabaProfile

GPT-4.1Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

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Mistral AI

Mistral Saba

Model Multibahasa Global

Mistral Saba dari Mistral AI adalah model AI yang dirancang untuk penerjemahan multibahasa yang akurat dan komunikasi perusahaan global, menghadirkan jendela konteks 33K, kecepatan 80 token/detik, indeks kecerdasan 40/100, serta tarif $0.2/

Intelligence Index40 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

38%

OpenAI

GPT-4.1

Model Unggulan Garis Depan

GPT-4.1 dari OpenAI adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 1.0M, kecepatan 110 token/detik, indeks kecerdasan 48/100, serta tarif $2/$

Intelligence Index48 / 100

Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

54.6%

Perbedaan Utama Sekilas

Mistral Saba is 12.0x cheaper for API usage (input + output tokens combined).
GPT-4.1 has a larger context window (1M vs 33K tokens).
GPT-4.1 is faster at 110 tok/s vs 80 tok/s.
GPT-4.1 leads on SWE-bench coding (54.6% vs 38.0%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Mistral Saba	GPT-4.1
Intelligence Index Composite reasoning & knowledge score (0-100)	40	48
Provider	Mistral AI	OpenAI
Input Price Cost per million input tokens	$0.20 / 1M	$2.00 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$8.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.14 / 1M	$1.55 / 1M
Context Window Maximum input + output tokens	32.8K tokens~49 A4 pages of text	1.05M tokens~1,571 A4 pages of text
Max Output Tokens Per single request	8K	33K
Throughput Speed Tokens generated per second	80 tok/s	110 tok/s
SWE-bench Verified Real-world software engineering	38%	54.6%
GPQA Diamond Graduate-level reasoning	46.5%	65%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.50 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

GPT-4.1

+16.6% SWE-bench lead

Cost

Mistral Saba

12.0x cheaper monthly runtime

Speed

GPT-4.1

+38% faster generation

Context

GPT-4.1

1.05M tokens window

Skor Benchmark Terverifikasi

Official evaluation metrics verified by independent third-party research labs.

Mistral SabaGPT-4.1

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

38%vs54.6%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

46.5%vs65%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

65.8%vs81%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1260ptsvs1338pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

5.2%vs9.8%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

24.5%vs30%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

24%vs36.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

41%vs58.5%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

48%vs55.5%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

59%vs70%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs110 tok/s

Latensi & Waktu Respons

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Mistral Saba0.46s

GPT-4.10.48s

Est. End-to-End Response (500 tokens)TTFT + generation time

Mistral Saba6.71s

GPT-4.15.03s

Simulator Biaya Token Interaktif

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Mistral Saba Monthly API Bill

$0.92 / month

$0.2/1M in · $0.6/1M out

GPT-4.1 Monthly API Bill

$10.95 / month

$2/1M in · $8/1M out

Mistral Saba saves approximately $10.04 / month (92%) under this workload.12.0x Savings

Mistral Saba · Strengths & Key Advantages

Jendela konteks 33K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 38% · GPQA Diamond 46.5%)
Kecepatan generasi tinggi 80 token/detik dengan latensi token pertama (TTFT) hanya 460 ms
Skema harga API kompetitif (tarif $0.2/$0.6 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
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

GPT-4.1 · Strengths & Key Advantages

Jendela konteks 1.0M dengan kapasitas output maksimum hingga 33K token (SWE-bench Verified 54.6% · GPQA Diamond 65%)
Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 480 ms
Skema harga API kompetitif (tarif $2/$8 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 Saba	GPT-4.1
Cost per Task Estimated USD to finish one task	$0.18	$0.72
Output Tokens / Task Tokens generated per task	24K	52K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Mistral Saba	GPT-4.1
Official API Model ID (SDK)	mistral-saba-latest	gpt-4-1
API Protocol	Mistral AI REST API (OpenAI-Compatible)	OpenAI Responses API & Chat Completions v1
Release Date	2025-02	2025-04
Knowledge Cutoff	January 2025	June 2024
Architecture	Multilingual Dense Transformer	Dense multimodal transformer
Parameters	24B	Confidential
Enterprise Rate Limits (RPM / TPM)	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.2/1M in) across full 33K window	Flat rate ($2/1M in) across full 1048K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Automatic prefix caching at $0.5/1M (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	API Prompt Engineering & RAG Distillation	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Mistral AI Production Safety & Alignment Verified	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision
Output Modalities	text	text

Mistral Saba vs GPT-4.1: Jawaban Cepat (FAQ)

Which is more intelligent, Mistral Saba or GPT-4.1?

GPT-4.1 is more intelligent. It scores 48 compared with 40 on the composite Intelligence Index.

Which model is faster?

GPT-4.1 is faster, generating 110 tokens per second compared with 80 tokens per second.

Which model is cheaper to run?

Mistral Saba is cheaper at $0.14 per 1M tokens blended, compared with $1.55 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Saba has lower latency, with a time to first token of 0.46s compared with 0.48s.

Which model has a larger context window?

GPT-4.1 has a larger context window, supporting 1.05M tokens compared with 32.8K tokens.

Kesimpulan

Baik Mistral Saba (Sangat cocok untuk penerjemahan multibahasa yang akurat dan komunikasi perusahaan global, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-4.1 (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GPT-4.1 unggul dalam penalaran dan pemrograman, GPT-4.1 memberikan kecepatan generasi token lebih tinggi, dan Mistral Saba lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Mistral Saba atau GPT-4.1?+

Dalam indeks kecerdasan gabungan dan benchmark coding, GPT-4.1 memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Mistral Saba vs GPT-4.1?+

Mistral Saba mengenakan biaya $0.20 / $0.60 per 1M token input/output, sedangkan GPT-4.1 sebesar $2.00 / $8.00. Secara keseluruhan, Mistral Saba lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Mistral Saba mendukung konteks 32.8K token dibandingkan 1.05M token pada GPT-4.1, sementara GPT-4.1 unggul dalam kecepatan output token per detik.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Mistral Saba vs GPT-4.1

Ganti & Bandingkan Model Apa Saja

Mistral Saba

GPT-4.1

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Mistral Saba · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4.1 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Mistral Saba vs GPT-4.1: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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