Mistral Saba vs GPT-4.1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Mistral Saba by Mistral AI and GPT-4.1 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 Saba holds a notable pricing advantage for token ingestion. On output generation, Mistral Saba is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-4.1 provides an expanded memory horizon with 1,048K tokens. In latency-sensitive production environments, GPT-4.1 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.
Changez et Comparez les Modèles
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Mistral Saba
IA d entreprise spécialiséeModèle Mistral Saba développé par Mistral AI, offrant une inférence rapide et une grande fiabilité pour les flux professionnels.
$0.20 / 1M
$0.60 / 1M
32.8K tokens
38%
GPT-4.1
Bourreau de travail à contexte longproduction bourreau de travail avec un 1M-token fenêtre de contexte et markedly improved instruction following.
$2.00 / 1M
$8.00 / 1M
1.05M tokens
54.6%
Différences Clés en un Coup d’Œil
- 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%).
| Caractéristique | 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 |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mistral Saba · Strengths & Key Advantages
- Architecture haute performance optimisée par Mistral AI
- Excellente prise en charge des sorties structurées JSON et des appels d outils
- Stabilité démontrée en environnement de production
Considerations & Trade-offs
- Nécessite une infrastructure dédiée ou des clés API adaptées pour les forts volumes
- Attention à la taille de la fenêtre de contexte pour les très grands documents
GPT-4.1 · Strengths & Key Advantages
- massif 1M-token fenêtre de contexte à un faible prix
- Excellent instruction following et codage précision
- solide structuré extraction pour agent pipelines
- fiable à long contexte recall up à roughly 300K tokens
Considerations & Trade-offs
- No raisonnement mode, so il trails o3 et GPT-5 sur multi-étapes problems
- contexte beyond ~300K tokens degrades dans précision
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 |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | 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: Réponses Rapides (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.
Verdict
Verdict & Model Selection Recommendation: Choose Mistral Saba if your priority is Regional enterprise SaaS in the MENA/South Asia region and localized government portals. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4.1 if your workflow prioritizes Long-document analysis, large-codebase review and structured extraction. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Mistral Saba compare to GPT-4.1 in API token pricing and cost per token?+
Mistral Saba costs $0.20 per 1M input tokens and $0.60 per 1M output tokens, whereas GPT-4.1 costs $2.00 input and $8.00 output. Overall, Mistral Saba 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 Saba or GPT-4.1?+
Mistral Saba supports a context window of 33K tokens (~25k words), while GPT-4.1 supports 1,048K tokens (~786k words). Therefore, GPT-4.1 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Mistral Saba or GPT-4.1?+
Mistral Saba scores 38% on SWE-bench Verified while GPT-4.1 scores 54.6%. For autonomous software engineering and multi-file debugging, GPT-4.1 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Mistral Saba and GPT-4.1?+
Mistral Saba is optimized for low-latency direct generation. In contrast, GPT-4.1 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Mistral Saba and GPT-4.1?+
Mistral Saba generates approximately 80 tokens per second, while GPT-4.1 delivers 110 tokens per second. In latency-sensitive production environments, GPT-4.1 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mistral Saba and GPT-4.1 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Mistral Saba to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4.1. 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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs