Ministral 8B vs Ministral 3B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Ministral 8B by Mistral AI and Ministral 3B 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, Ministral 3B holds a notable pricing advantage for token ingestion. On output generation, Ministral 3B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Ministral 3B 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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Ministral 8B
Mistral Edge PowerhouseMistral AI's cutting-edge edge model designed for local on-device inference, intelligent robotics, and low-latency agent orchestration.
$0.10 / 1M
$0.10 / 1M
128K tokens
32.5%
Ministral 3B
Ultra-Light Mobile SpecialistMistral's smallest sub-4B powerhouse built for mobile phones, embedded devices, and ultra-high-frequency classification tasks.
$0.04 / 1M
$0.04 / 1M
128K tokens
22.1%
Key Differences at a Glance
- Ministral 3B is 2.5x cheaper for API usage (input + output tokens combined).
- Ministral 3B is faster at 180 tok/s vs 110 tok/s.
- Ministral 8B leads on SWE-bench coding (32.5% vs 22.1%).
| Feature | Ministral 8B | Ministral 3B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 33 |
Provider | Mistral AI | Mistral AI |
Input Price Cost per million input tokens | $0.10 / 1M | $0.04 / 1M |
Output Price Cost per million output tokens | $0.10 / 1M | $0.04 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 110 tok/s | 180 tok/s |
SWE-bench Verified Real-world software engineering | 32.5% | 22.1% |
GPQA Diamond Graduate-level reasoning | 41.5% | 32.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.02 / 1M |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Ministral 8B · Strengths & Key Advantages
- Huge 128K context window in an ultra-fast 8B footprint
- Symmetric pricing model ($0.10 input and $0.10 output per 1M tokens)
- Exceptional function calling and multi-turn conversational steering
- Commercial license available for edge appliance embedding
Considerations & Trade-offs
- More modest code synthesis compared to Codestral
- Text-only processing
Ministral 3B · Strengths & Key Advantages
- Massive 128K context window in a featherweight 3B parameter model
- Extremely high inference speed (180+ tokens/sec) on modest CPUs and mobile NPUs
- Ultra-cheap API price ($0.04 / 1M tokens)
- Great multi-language conversational fluency
Considerations & Trade-offs
- Struggles with advanced algorithmic programming
- Knowledge depth compressed due to tiny footprint
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Ministral 8B | Ministral 3B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.08 | $0.03 |
Output Tokens / Task Tokens generated per task | 21K | 15K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Ministral 8B | Ministral 3B |
|---|---|---|
| Official API Model ID (SDK) | ministral-8b-latest | ministral-3b-latest |
| API Protocol | Mistral AI REST API (OpenAI-Compatible) | Mistral AI REST API (OpenAI-Compatible) |
| Release Date | 2024-10 | 2024-10 |
| Knowledge Cutoff | October 2024 | October 2024 |
| Architecture | Compact Dense Transformer with Interleaved Sliding Window | Ultra-Compact Dense Transformer |
| Parameters | 8B | 3B |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 128K window | Flat rate ($0.04/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Cached input at $0.02/1M 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 | API Prompt Engineering & RAG Distillation | API Prompt Engineering & RAG Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| 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 | Mistral AI Production Safety & Alignment Verified |
| 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 |
| Output Modalities | text | text |
Ministral 8B vs Ministral 3B: Quick Answers
Which is more intelligent, Ministral 8B or Ministral 3B?
Ministral 8B is more intelligent. It scores 38 compared with 33 on the composite Intelligence Index.
Which model is faster?
Ministral 3B is faster, generating 180 tokens per second compared with 110 tokens per second.
Which model is cheaper to run?
Ministral 3B is cheaper at $0.03 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Ministral 3B has lower latency, with a time to first token of 0.21s compared with 0.33s.
Verdict
Verdict & Model Selection Recommendation: Choose Ministral 8B if your priority is Local edge computing, smart automotive assistants, and fast API microservices. Its performance in demanding tasks justifies its operational token cost. Choose Ministral 3B if your workflow prioritizes Smartphone native apps, IoT controllers, and instant prompt classification. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Ministral 8B compare to Ministral 3B in API token pricing and cost per token?+
Ministral 8B costs $0.10 per 1M input tokens and $0.10 per 1M output tokens, whereas Ministral 3B costs $0.04 input and $0.04 output. Overall, Ministral 3B 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: Ministral 8B or Ministral 3B?+
Both Ministral 8B and Ministral 3B offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Ministral 8B or Ministral 3B?+
Ministral 8B scores 32.5% on SWE-bench Verified while Ministral 3B scores 22.1%. For autonomous software engineering and multi-file debugging, Ministral 8B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Ministral 8B and Ministral 3B?+
Ministral 8B is optimized for low-latency direct generation. In contrast, Ministral 3B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Ministral 8B and Ministral 3B?+
Ministral 8B generates approximately 110 tokens per second, while Ministral 3B delivers 180 tokens per second. In latency-sensitive production environments, Ministral 3B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Ministral 8B and Ministral 3B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Ministral 3B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Ministral 8B. 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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs