Ministral 3B vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Ministral 3B by Mistral AI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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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%
Gemini 4 Argon
1M Output Multimodal KingGoogle DeepMind's flagship frontier reasoning engine featuring 1-million-token output limits, autonomous cybersecurity defense, and real-time audio/vision ingestion.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Key Differences at a Glance
- Ministral 3B is 96.4x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 128K tokens).
- Ministral 3B is faster at 180 tok/s vs 115 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 22.1%).
| Feature | Ministral 3B | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 33 | 97 |
Provider | Mistral AI | |
Input Price Cost per million input tokens | $0.04 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.04 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 1000K |
Throughput Speed Tokens generated per second | 180 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 22.1% | 81.9% |
GPQA Diamond Graduate-level reasoning | 32.5% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.10 / 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 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
Gemini 4 Argon · Strengths & Key Advantages
- Groundbreaking 1,000,000 token maximum output generation capacity via Long Decode Continuation
- 1M-token input window with 99.7% GraphWalks recall (84.2% in the 256K-1M range)
- Native processing of full audio, video streams, and visual data (91.7% on LVBench)
- Half the token price of Anthropic's Opus 5.5 and #1 on Chatbot Arena (1525 Elo)
Considerations & Trade-offs
- Massive token outputs can incur high absolute costs if unconstrained (~62K output tokens per index task)
- $2/$10 pricing is an introductory 50% discount; standard rates revert to $4/$20 once the promotion ends
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Ministral 3B | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.03 | $2.45 |
Output Tokens / Task Tokens generated per task | 15K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Ministral 3B | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | ministral-3b-latest | models/gemini-4-argon |
| API Protocol | Mistral AI REST API (OpenAI-Compatible) | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-10 | 2026-09 |
| Knowledge Cutoff | October 2024 | June 2026 |
| Architecture | Ultra-Compact Dense Transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 3B | 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 ($0.04/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| 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 | Google DeepMind Frontier Safety Framework 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, vision, audio, video |
| Output Modalities | text | text |
Ministral 3B vs Gemini 4 Argon: Quick Answers
Which is more intelligent, Ministral 3B or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 33 on the composite Intelligence Index.
Which model is faster?
Ministral 3B is faster, generating 180 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Ministral 3B is cheaper at $0.03 per 1M tokens blended, compared with $1.47 (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 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 128K tokens.
Verdict
Choose Ministral 3B for smartphone native apps, iot controllers, and instant prompt classification, or choose Gemini 4 Argon for whole-repository code generation, full-book synthesis, video understanding, and multi-hour audio processing.
Frequently asked questions
Which is cheaper: Ministral 3B or Gemini 4 Argon?+
Ministral 3B costs $0.04/1M input and $0.04/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Ministral 3B or Gemini 4 Argon?+
Ministral 3B supports 128K tokens whereas Gemini 4 Argon supports 1M tokens.
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: Mistral AI & Google Official Documentation